system

Abstract

The system according to this embodiment aims to easily find playmates for children who share common interests. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives input from the parent about their child's hobbies and interests. The analysis unit analyzes the information received by the reception unit and searches for children with the same hobbies. The provision unit provides information about children with the same hobbies identified by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to find a playmate who has a common hobby with a child, and it is necessary for parents to take trouble.

[0005] The system according to the embodiment aims to easily find a playmate who has a common hobby with a child.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the hobbies and interests of a child by a parent. The analysis unit analyzes the information input by the reception unit and searches for children who have common hobbies. The provision unit provides information on children who have common hobbies identified by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment makes it easy for children to find playmates who share their hobbies. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The child playmate matching system according to an embodiment of the present invention is a system that allows children to easily find playmates with common interests. In this system, parents input their child's hobbies and interests, and a generating AI analyzes this information to search for children with similar interests and also provides information about the parents. For example, parents input their child's hobbies and interests, such as soccer, painting, or reading. This information is input into the generating AI. Next, the generating AI analyzes the input information and searches for children with similar interests. The generating AI identifies children with similar interests based on data collected from other families. For example, it matches children who like soccer. Furthermore, the generating AI also analyzes the parents' information and provides information about the parents. For example, it provides information such as the parents' occupation and the area where they live. This makes it easier for parents to contact each other. This system makes it easy to find children with similar interests and allows parents to understand each other's information, thus saving the effort of finding playmates. It also makes it easier to find playmates whose schedules match on holidays. For example, if a parent inputs "I'm looking for a child who likes soccer," the generating AI analyzes data collected from other families and identifies children who like soccer. Furthermore, since parental information is also provided, it becomes easier for parents to contact each other. This service targets all families with children and reduces the burden on parents by making it easy to find playmates for their children. In addition, playing with children who share common interests contributes to improving children's social skills and communication abilities. In short, this children's playmate matching system makes it easy to find playmates for children and reduces the burden on parents.

[0029] The child playmate matching system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives input from parents about their children's hobbies and interests. Parents can input their children's hobbies and interests using, for example, a web form or a mobile application. For example, parents can input hobbies such as soccer, painting, and reading. The reception unit transmits the input information to a generation AI. The analysis unit uses the generation AI to analyze the information input by the reception unit and search for children with common hobbies. The generation AI analyzes the input information using, for example, a text generation AI (e.g., LLM). The generation AI identifies children with common hobbies based on data collected from other families. For example, the generation AI matches children who like soccer with each other. The provision unit provides information about children with common hobbies identified by the analysis unit. The provision unit provides, for example, information such as the parents' occupation and the area where they live. The provision unit can also provide information about the parents to facilitate communication between them. For example, the provision unit provides information such as the parents' occupation and the area where they live. As a result, the child playmate matching system according to the embodiment allows parents to input their child's hobbies and interests and easily find children with similar interests. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide parent information using an AI model that takes parent information as input and outputs parent information.

[0030] The reception desk allows parents to input their children's hobbies and interests. Parents can use web forms or mobile applications to input this information. Specifically, parents access the web form or mobile application and enter detailed information about their child's hobbies and interests. For example, they can select hobbies such as soccer, painting, or reading from a list of options, but also enter specific activities, frequency, and past experiences. Furthermore, parents can also input information such as their child's age, gender, and personality. This allows the reception desk to collect detailed data about children's hobbies and interests and prepare it for transmission to the generating AI. The reception desk processes the entered information in real time and stores it in a database. The database is secure and uses encryption technology to protect parental privacy. Additionally, the reception desk has a feedback function to verify the accuracy of the entered information, allowing parents to review and correct their entries. This enables the reception desk to collect accurate and detailed information and transmit it to the generating AI, supporting accurate matching by the analysis department.

[0031] The analysis unit uses a generative AI to analyze the information entered by the reception unit and search for children with shared hobbies. The generative AI analyzes the entered information using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI uses natural language processing technology to analyze text data about children's hobbies and interests entered by parents to identify children with shared hobbies. The generative AI identifies children with shared hobbies based on data collected from other families. For example, to match children who like soccer, the generative AI analyzes keywords and phrases related to soccer to find common ground. The generative AI also considers information such as the child's age, gender, and personality to perform the optimal match. Furthermore, the generative AI learns from past matching data and successful cases to improve the accuracy of the matching. Based on the analysis results by the generative AI, the analysis unit creates a list of children with shared hobbies and sends it to the provision unit. This allows the analysis unit to efficiently analyze the information entered by parents and find the most suitable playmates.

[0032] The information provider will provide information about children with shared hobbies, identified by the analysis unit. Specifically, it will provide information such as the parents' occupation and place of residence. The information provider can also provide information about the parents to facilitate communication between them. For example, it will provide information such as the parents' occupation and place of residence. Based on the list of children with shared hobbies received from the analysis unit, the information provider will provide appropriate information to the parents. For example, it will provide parents with the names, ages, and detailed information about the hobbies of children with shared hobbies. It can also provide parents' contact information and social media accounts to facilitate communication between them. Furthermore, the information provider can provide a platform to facilitate communication between parents. For example, it can enable parents to communicate directly with each other through a dedicated chat room or messaging app. This allows the information provider to provide information that makes it easy for parents to find and contact children with shared hobbies. In addition, the information provider can collect parent feedback and use it to improve the system. For example, it can allow parents to leave ratings and comments on the information provided, and use that feedback to improve the accuracy and usability of the system. This allows the service provider to offer an environment where parents can confidently find playmates for their children.

[0033] The information provider can provide information about parents. For example, the information provider can provide information such as the parents' names, contact information, and occupations. The information provider can also provide information about parents to facilitate communication between them. For example, the information provider can provide information such as the parents' occupations and the areas where they live. By providing information about parents, it becomes easier for them to communicate with each other. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information about parents using an AI model that takes parent information as input and outputs parent information.

[0034] The information provider can provide information with the consent of the parents. The information provider can obtain, for example, written or electronic consent from the parents. The information provider can protect privacy by providing information with the consent of the parents. For example, the information provider can provide the parents' information with the consent of the parents. This protects privacy by providing information with the consent of the parents. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can obtain parental consent using an AI model that takes parental consent as input and outputs parental consent.

[0035] The analysis unit can identify children with common hobbies based on data collected from other households. The analysis unit collects data such as survey results and online registration information, and analyzes it using a generative AI. The generative AI analyzes the collected data using, for example, a text generation AI (e.g., LLM). The generative AI identifies children with common hobbies based on data collected from other households. For example, the generative AI matches children who like soccer. This improves the accuracy of matching by identifying children with common hobbies based on data collected from other households. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can identify children with common hobbies using an AI model that takes collected data as input and outputs children with common hobbies.

[0036] The reception area allows users to input their child's hobbies and interests. For example, parents can input their child's hobbies and interests using a web form or mobile application. For instance, parents can input hobbies such as soccer, painting, or reading. This makes it easier to find children with similar interests. Some or all of the above processing in the reception area may be performed using AI, or not. For example, the reception area can input the hobbies and interests entered by parents into a generating AI, providing the AI ​​with data for analysis.

[0037] The information provider can provide information such as the parents' occupation and the area where they live. The information provider can provide information about parents to make it easier for parents to contact each other. For example, the information provider can provide information such as the parents' occupation and the area where they live. By providing information such as the parents' occupation and the area where they live, it becomes easier for parents to contact each other. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can provide information about parents using an AI model that takes parent information as input and outputs parent information.

[0038] The reception desk can analyze a child's past hobbies and interests and suggest the optimal input method. For example, the reception desk can automatically display hobbies that the child has frequently shown interest in in the past as candidates. The reception desk can suggest relevant hobbies based on events and activities the child has participated in in the past. The reception desk can predict and suggest hobbies that the child may be interested in during specific seasons or periods based on the child's past hobby history. In this way, the reception desk can suggest the optimal input method by analyzing the child's past hobbies and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest the optimal input method using an AI model that takes the child's past hobbies and interests as input and outputs the optimal input method.

[0039] The reception unit can filter the input of hobbies and interests based on the child's age and gender. For example, the reception unit can automatically filter and display appropriate hobbies and interests according to the child's age. The reception unit can suggest generally popular hobbies and interests based on the child's gender. The reception unit can suggest the most suitable hobbies and interests based on the combination of the child's age and gender. In this way, appropriate hobbies and interests can be suggested by filtering based on the child's age and gender. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can perform filtering using an AI model that takes the child's age and gender as input and outputs filtered hobbies and interests.

[0040] The reception system can prioritize inputting highly relevant hobbies and interests by considering the parent's geographical location when inputting hobbies and interests. For example, the reception system can suggest events and activities held in the vicinity based on the parent's current location. The reception system can suggest region-specific hobbies and interests based on the characteristics of the area where the parent lives. The reception system can suggest hobbies and interests in easily accessible locations based on the parent's geographical location. In this way, by considering the parent's geographical location, highly relevant hobbies and interests can be prioritized. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can prioritize inputting highly relevant hobbies and interests by using an AI model that takes the parent's geographical location as input and outputs highly relevant hobbies and interests.

[0041] The reception desk can analyze the parent's social media activity when inputting hobbies and interests, and input related hobbies and interests. For example, the reception desk can analyze the content of the parent's social media posts and suggest related hobbies and interests. The reception desk can suggest hobbies and interests based on information about accounts and groups that the parent follows. The reception desk can analyze the parent's social media activity history and suggest hobbies that the parent has shown interest in in the past. In this way, related hobbies and interests can be input by analyzing the parent's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input related hobbies and interests using an AI model that takes the parent's social media activity as input and outputs related hobbies and interests.

[0042] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of children's hobbies and interests during the analysis process. For example, if a child has multiple hobbies, the analysis unit will consider the relationships between those hobbies during the analysis. If a child's hobbies depend on seasons or time of year, the analysis unit can consider these fluctuations during the analysis. If a child's hobbies are related to specific events or activities, the analysis unit can consider that information during the analysis. In this way, the accuracy of the analysis is improved by considering the interrelationships of children's hobbies and interests. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that takes the interrelationships of children's hobbies and interests as input and outputs the analysis results.

[0043] The analysis unit can perform analysis while considering attribute information such as the child's age and gender. For example, the analysis unit can analyze appropriate hobbies and interests according to the child's age. The analysis unit can analyze generally popular hobbies and interests based on the child's gender. The analysis unit can analyze the optimal hobbies and interests based on the combination of the child's age and gender. In this way, appropriate analysis results can be obtained by considering attribute information such as the child's age and gender. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes attribute information such as the child's age and gender as input and outputs analysis results.

[0044] The analysis unit can perform analysis while considering the geographical distribution of children. For example, the analysis unit can analyze children who share common hobbies in their neighborhood based on the area in which they live. The analysis unit can analyze region-specific hobbies and interests while considering the geographical distribution of children. The analysis unit can analyze hobbies and interests in easily accessible locations based on the geographical distribution of children. In this way, appropriate analysis results can be obtained by considering the geographical distribution of children. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes the geographical distribution of children as input and outputs analysis results.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on children during the analysis process. For example, the analysis unit can perform analysis by referring to academic papers related to children's hobbies. The analysis unit can perform analysis based on books and articles related to children's interests. The analysis unit can perform analysis by referring to the latest research findings on children's hobbies. As a result, the accuracy of the analysis is improved by referring to relevant literature on children. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that takes relevant literature on children as input and outputs analysis results.

[0046] The information provider can adjust the level of detail of the information provided based on the importance of the child's hobbies and interests. For example, the provider can provide detailed information about hobbies that the child is particularly interested in. If the child has multiple hobbies, the provider can provide information about each hobby in a balanced manner. The provider can adjust the level of detail of the information provided in accordance with changes in the child's interests. This allows the provider to provide appropriate information by adjusting the level of detail based on the importance of the child's hobbies and interests. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can adjust the level of detail of the information using an AI model that takes the importance of the child's hobbies and interests as input and outputs the level of detail of the information.

[0047] The information provider can provide information while considering the parents' occupation and attribute information of the area where they live. For example, the information provider can suggest hobbies and interests related to the parents' occupation. The information provider can suggest region-specific hobbies and interests based on the characteristics of the area where the parents live. The information provider can suggest optimal hobbies and interests based on the parents' occupation and attribute information of the area where they live. In this way, appropriate information can be provided by considering the parents' occupation and attribute information of the area where they live. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the parents' occupation and attribute information of the area where they live as input and outputs information.

[0048] The information provider can provide optimal information by considering the parent's geographical location information at the time of provision. For example, the provider can provide information about events and activities held in the vicinity based on the parent's current location. The provider can provide information about hobbies and interests specific to the area where the parent lives based on the characteristics of the area where the parent lives. The provider can provide information about hobbies and interests in easily accessible locations based on the parent's geographical location information. In this way, by considering the parent's geographical location information, highly relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the parent's geographical location information as input and outputs optimal information.

[0049] The information provider can analyze the parent's social media activity and provide relevant information at the time of provision. For example, the information provider can analyze the content of the parent's social media posts and provide information on relevant hobbies and interests. The information provider can provide information on hobbies and interests based on information about accounts and groups that the parent follows. The information provider can analyze the parent's social media activity history and provide information on hobbies that the parent has shown interest in in the past. In this way, relevant information can be provided by analyzing the parent's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the parent's social media activity as input and outputs relevant information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can analyze a child's past hobbies and interests and suggest the optimal input method. For example, it can automatically display hobbies that the child has frequently shown interest in in the past as candidates. It can suggest related hobbies based on events and activities the child has participated in in the past. It can predict and suggest hobbies that the child may be interested in during specific seasons or periods based on the child's past hobby history. In this way, the optimal input method can be suggested by analyzing the child's past hobbies and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest the optimal input method using an AI model that takes the child's past hobbies and interests as input and outputs the optimal input method.

[0052] The reception desk can filter hobbies and interests based on the child's age and gender when the user inputs them. For example, it can automatically filter and display appropriate hobbies and interests according to the child's age. It can also suggest generally popular hobbies and interests based on the child's gender. It can suggest the most suitable hobbies and interests based on the combination of the child's age and gender. In this way, appropriate hobbies and interests can be suggested by filtering based on the child's age and gender. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can perform filtering using an AI model that takes the child's age and gender as input and outputs filtered hobbies and interests.

[0053] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of children's hobbies and interests. For example, if a child has multiple hobbies, the analysis can consider the relationships between those hobbies. If a child's hobbies depend on seasons or time of year, the analysis can consider these fluctuations. If a child's hobbies are related to specific events or activities, the analysis can consider that information. In this way, the accuracy of the analysis is improved by considering the interrelationships of children's hobbies and interests. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that takes the interrelationships of children's hobbies and interests as input and outputs the analysis results.

[0054] The analysis unit can perform analysis while considering attribute information such as the child's age and gender. For example, it can analyze appropriate hobbies and interests according to the child's age. It can analyze generally popular hobbies and interests based on the child's gender. It can analyze the optimal hobbies and interests based on the combination of the child's age and gender. In this way, appropriate analysis results can be obtained by considering attribute information such as the child's age and gender. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes attribute information such as the child's age and gender as input and outputs analysis results.

[0055] The information provider can provide information while considering the parents' occupation and attribute information of the area where they live. For example, it can suggest hobbies and interests related to the parents' occupation. Based on the characteristics of the area where the parents live, it can suggest region-specific hobbies and interests. Based on the parents' occupation and attribute information of the area where they live, it can suggest the most suitable hobbies and interests. In this way, appropriate information can be provided by considering the parents' occupation and attribute information of the area where they live. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can provide information using an AI model that takes the parents' occupation and attribute information of the area where they live as input and outputs information.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives input from parents about their child's hobbies and interests. Parents can input hobbies such as soccer, painting, and reading using a web form or mobile application. The reception desk sends the entered information to the generating AI. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and search for children with common interests. The generation AI uses a text generation AI (e.g., LLM) to identify children with common interests based on data collected from other families. For example, it matches children who like soccer with each other. Step 3: The provisioning unit provides information about children with common interests identified by the analysis unit. The provisioning unit can also provide information such as the parents' occupations and the area where they live, to facilitate communication between parents. Processing in the provisioning unit may or may not be performed using AI.

[0058] (Example of form 2) The child playmate matching system according to an embodiment of the present invention is a system that allows children to easily find playmates with common interests. In this system, parents input their child's hobbies and interests, and a generating AI analyzes this information to search for children with similar interests and also provides information about the parents. For example, parents input their child's hobbies and interests, such as soccer, painting, or reading. This information is input into the generating AI. Next, the generating AI analyzes the input information and searches for children with similar interests. The generating AI identifies children with similar interests based on data collected from other families. For example, it matches children who like soccer. Furthermore, the generating AI also analyzes the parents' information and provides information about the parents. For example, it provides information such as the parents' occupation and the area where they live. This makes it easier for parents to contact each other. This system makes it easy to find children with similar interests and allows parents to understand each other's information, thus saving the effort of finding playmates. It also makes it easier to find playmates whose schedules match on holidays. For example, if a parent inputs "I'm looking for a child who likes soccer," the generating AI analyzes data collected from other families and identifies children who like soccer. Furthermore, since parental information is also provided, it becomes easier for parents to contact each other. This service targets all families with children and reduces the burden on parents by making it easy to find playmates for their children. In addition, playing with children who share common interests contributes to improving children's social skills and communication abilities. In short, this children's playmate matching system makes it easy to find playmates for children and reduces the burden on parents.

[0059] The child playmate matching system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives input from parents about their children's hobbies and interests. Parents can input their children's hobbies and interests using, for example, a web form or a mobile application. For example, parents can input hobbies such as soccer, painting, and reading. The reception unit transmits the input information to a generation AI. The analysis unit uses the generation AI to analyze the information input by the reception unit and search for children with common hobbies. The generation AI analyzes the input information using, for example, a text generation AI (e.g., LLM). The generation AI identifies children with common hobbies based on data collected from other families. For example, the generation AI matches children who like soccer with each other. The provision unit provides information about children with common hobbies identified by the analysis unit. The provision unit provides, for example, information such as the parents' occupation and the area where they live. The provision unit can also provide information about the parents to facilitate communication between them. For example, the provision unit provides information such as the parents' occupation and the area where they live. As a result, the child playmate matching system according to the embodiment allows parents to input their child's hobbies and interests and easily find children with similar interests. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can provide parent information using an AI model that takes parent information as input and outputs parent information.

[0060] The reception desk allows parents to input their children's hobbies and interests. Parents can use web forms or mobile applications to input this information. Specifically, parents access the web form or mobile application and enter detailed information about their child's hobbies and interests. For example, they can select hobbies such as soccer, painting, or reading from a list of options, but also enter specific activities, frequency, and past experiences. Furthermore, parents can also input information such as their child's age, gender, and personality. This allows the reception desk to collect detailed data about children's hobbies and interests and prepare it for transmission to the generating AI. The reception desk processes the entered information in real time and stores it in a database. The database is secure and uses encryption technology to protect parental privacy. Additionally, the reception desk has a feedback function to verify the accuracy of the entered information, allowing parents to review and correct their entries. This enables the reception desk to collect accurate and detailed information and transmit it to the generating AI, supporting accurate matching by the analysis department.

[0061] The analysis unit uses a generative AI to analyze the information entered by the reception unit and search for children with shared hobbies. The generative AI analyzes the entered information using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI uses natural language processing technology to analyze text data about children's hobbies and interests entered by parents to identify children with shared hobbies. The generative AI identifies children with shared hobbies based on data collected from other families. For example, to match children who like soccer, the generative AI analyzes keywords and phrases related to soccer to find common ground. The generative AI also considers information such as the child's age, gender, and personality to perform the optimal match. Furthermore, the generative AI learns from past matching data and successful cases to improve the accuracy of the matching. Based on the analysis results by the generative AI, the analysis unit creates a list of children with shared hobbies and sends it to the provision unit. This allows the analysis unit to efficiently analyze the information entered by parents and find the most suitable playmates.

[0062] The information provider will provide information about children with shared hobbies, identified by the analysis unit. Specifically, it will provide information such as the parents' occupation and place of residence. The information provider can also provide information about the parents to facilitate communication between them. For example, it will provide information such as the parents' occupation and place of residence. Based on the list of children with shared hobbies received from the analysis unit, the information provider will provide appropriate information to the parents. For example, it will provide parents with the names, ages, and detailed information about the hobbies of children with shared hobbies. It can also provide parents' contact information and social media accounts to facilitate communication between them. Furthermore, the information provider can provide a platform to facilitate communication between parents. For example, it can enable parents to communicate directly with each other through a dedicated chat room or messaging app. This allows the information provider to provide information that makes it easy for parents to find and contact children with shared hobbies. In addition, the information provider can collect parent feedback and use it to improve the system. For example, it can allow parents to leave ratings and comments on the information provided, and use that feedback to improve the accuracy and usability of the system. This allows the service provider to offer an environment where parents can confidently find playmates for their children.

[0063] The information provider can provide information about parents. For example, the information provider can provide information such as the parents' names, contact information, and occupations. The information provider can also provide information about parents to facilitate communication between them. For example, the information provider can provide information such as the parents' occupations and the areas where they live. By providing information about parents, it becomes easier for them to communicate with each other. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information about parents using an AI model that takes parent information as input and outputs parent information.

[0064] The information provider can provide information with the consent of the parents. The information provider can obtain, for example, written or electronic consent from the parents. The information provider can protect privacy by providing information with the consent of the parents. For example, the information provider can provide the parents' information with the consent of the parents. This protects privacy by providing information with the consent of the parents. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can obtain parental consent using an AI model that takes parental consent as input and outputs parental consent.

[0065] The analysis unit can identify children with common hobbies based on data collected from other households. The analysis unit collects data such as survey results and online registration information, and analyzes it using a generative AI. The generative AI analyzes the collected data using, for example, a text generation AI (e.g., LLM). The generative AI identifies children with common hobbies based on data collected from other households. For example, the generative AI matches children who like soccer. This improves the accuracy of matching by identifying children with common hobbies based on data collected from other households. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can identify children with common hobbies using an AI model that takes collected data as input and outputs children with common hobbies.

[0066] The reception area allows users to input their child's hobbies and interests. For example, parents can input their child's hobbies and interests using a web form or mobile application. For instance, parents can input hobbies such as soccer, painting, or reading. This makes it easier to find children with similar interests. Some or all of the above processing in the reception area may be performed using AI, or not. For example, the reception area can input the hobbies and interests entered by parents into a generating AI, providing the AI ​​with data for analysis.

[0067] The information provider can provide information such as the parents' occupation and the area where they live. The information provider can provide information about parents to make it easier for parents to contact each other. For example, the information provider can provide information such as the parents' occupation and the area where they live. By providing information such as the parents' occupation and the area where they live, it becomes easier for parents to contact each other. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can provide information about parents using an AI model that takes parent information as input and outputs parent information.

[0068] The reception unit can estimate the parent's emotions and adjust the input method for hobbies and interests based on the estimated emotions. For example, if the parent is stressed, the reception unit can provide a simple interface and minimize the input steps. If the parent is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the parent is in a hurry, the reception unit can prioritize voice input to allow for quick input of hobbies and interests. This allows parents to input their hobbies and interests without feeling stressed by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0069] The reception desk can analyze a child's past hobbies and interests and suggest the optimal input method. For example, the reception desk can automatically display hobbies that the child has frequently shown interest in in the past as candidates. The reception desk can suggest relevant hobbies based on events and activities the child has participated in in the past. The reception desk can predict and suggest hobbies that the child may be interested in during specific seasons or periods based on the child's past hobby history. In this way, the reception desk can suggest the optimal input method by analyzing the child's past hobbies and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest the optimal input method using an AI model that takes the child's past hobbies and interests as input and outputs the optimal input method.

[0070] The reception unit can filter the input of hobbies and interests based on the child's age and gender. For example, the reception unit can automatically filter and display appropriate hobbies and interests according to the child's age. The reception unit can suggest generally popular hobbies and interests based on the child's gender. The reception unit can suggest the most suitable hobbies and interests based on the combination of the child's age and gender. In this way, appropriate hobbies and interests can be suggested by filtering based on the child's age and gender. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can perform filtering using an AI model that takes the child's age and gender as input and outputs filtered hobbies and interests.

[0071] The reception unit can estimate the parent's emotions and, based on the estimated emotions, determine the priority of hobbies and interests to be entered. For example, if the parent is stressed, the reception unit will prioritize suggesting hobbies that help the child relax. If the parent is enjoying themselves, the reception unit can prioritize suggesting hobbies that can be enjoyed together as a family. If the parent is in a hurry, the reception unit can prioritize suggesting hobbies that can be enjoyed in a short amount of time. In this way, by determining the priority of hobbies and interests according to the parent's emotions, parents can enter their hobbies and interests without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0072] The reception system can prioritize inputting highly relevant hobbies and interests by considering the parent's geographical location when inputting hobbies and interests. For example, the reception system can suggest events and activities held in the vicinity based on the parent's current location. The reception system can suggest region-specific hobbies and interests based on the characteristics of the area where the parent lives. The reception system can suggest hobbies and interests in easily accessible locations based on the parent's geographical location. In this way, by considering the parent's geographical location, highly relevant hobbies and interests can be prioritized. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can prioritize inputting highly relevant hobbies and interests by using an AI model that takes the parent's geographical location as input and outputs highly relevant hobbies and interests.

[0073] The reception desk can analyze the parent's social media activity when inputting hobbies and interests, and input related hobbies and interests. For example, the reception desk can analyze the content of the parent's social media posts and suggest related hobbies and interests. The reception desk can suggest hobbies and interests based on information about accounts and groups that the parent follows. The reception desk can analyze the parent's social media activity history and suggest hobbies that the parent has shown interest in in the past. In this way, related hobbies and interests can be input by analyzing the parent's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input related hobbies and interests using an AI model that takes the parent's social media activity as input and outputs related hobbies and interests.

[0074] The analysis unit can estimate the parent's emotions and adjust the analysis criteria based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit will prioritize analyzing relaxing hobbies. If the parent is enjoying themselves, the analysis unit can prioritize analyzing hobbies that can be enjoyed by both parent and child. If the parent is in a hurry, the analysis unit can prioritize analyzing hobbies that can be enjoyed in a short amount of time. In this way, by adjusting the analysis criteria according to the parent's emotions, the parent can obtain analysis results without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0075] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of children's hobbies and interests during the analysis process. For example, if a child has multiple hobbies, the analysis unit will consider the relationships between those hobbies during the analysis. If a child's hobbies depend on seasons or time of year, the analysis unit can consider these fluctuations during the analysis. If a child's hobbies are related to specific events or activities, the analysis unit can consider that information during the analysis. In this way, the accuracy of the analysis is improved by considering the interrelationships of children's hobbies and interests. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that takes the interrelationships of children's hobbies and interests as input and outputs the analysis results.

[0076] The analysis unit can perform analysis while considering attribute information such as the child's age and gender. For example, the analysis unit can analyze appropriate hobbies and interests according to the child's age. The analysis unit can analyze generally popular hobbies and interests based on the child's gender. The analysis unit can analyze the optimal hobbies and interests based on the combination of the child's age and gender. In this way, appropriate analysis results can be obtained by considering attribute information such as the child's age and gender. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes attribute information such as the child's age and gender as input and outputs analysis results.

[0077] The analysis unit can estimate the parent's emotions and adjust the display order of the analysis results based on the estimated parent's emotions. For example, if the parent is feeling stressed, the analysis unit can prioritize displaying relaxing hobbies. If the parent is enjoying themselves, the analysis unit can prioritize displaying hobbies that can be enjoyed by both parent and child. If the parent is in a hurry, the analysis unit can prioritize displaying hobbies that can be enjoyed in a short amount of time. In this way, by adjusting the display order of the analysis results according to the parent's emotions, the parent can review the analysis results without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0078] The analysis unit can perform analysis while considering the geographical distribution of children. For example, the analysis unit can analyze children who share common hobbies in their neighborhood based on the area in which they live. The analysis unit can analyze region-specific hobbies and interests while considering the geographical distribution of children. The analysis unit can analyze hobbies and interests in easily accessible locations based on the geographical distribution of children. In this way, appropriate analysis results can be obtained by considering the geographical distribution of children. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes the geographical distribution of children as input and outputs analysis results.

[0079] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on children during the analysis process. For example, the analysis unit can perform analysis by referring to academic papers related to children's hobbies. The analysis unit can perform analysis based on books and articles related to children's interests. The analysis unit can perform analysis by referring to the latest research findings on children's hobbies. As a result, the accuracy of the analysis is improved by referring to relevant literature on children. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that takes relevant literature on children as input and outputs analysis results.

[0080] The information provider can estimate the parent's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the parent is stressed, the provider can provide simple and easily understandable information. If the parent is having fun, the provider can provide information that includes details. If the parent is in a hurry, the provider can provide concise information. By adjusting the way the information is presented according to the parent's emotions, the parent can receive the information without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0081] The information provider can adjust the level of detail of the information provided based on the importance of the child's hobbies and interests. For example, the provider can provide detailed information about hobbies that the child is particularly interested in. If the child has multiple hobbies, the provider can provide information about each hobby in a balanced manner. The provider can adjust the level of detail of the information provided in accordance with changes in the child's interests. This allows the provider to provide appropriate information by adjusting the level of detail based on the importance of the child's hobbies and interests. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can adjust the level of detail of the information using an AI model that takes the importance of the child's hobbies and interests as input and outputs the level of detail of the information.

[0082] The information provider can provide information while considering the parents' occupation and attribute information of the area where they live. For example, the information provider can suggest hobbies and interests related to the parents' occupation. The information provider can suggest region-specific hobbies and interests based on the characteristics of the area where the parents live. The information provider can suggest optimal hobbies and interests based on the parents' occupation and attribute information of the area where they live. In this way, appropriate information can be provided by considering the parents' occupation and attribute information of the area where they live. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the parents' occupation and attribute information of the area where they live as input and outputs information.

[0083] The information provider can estimate the parent's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the parent is stressed, the provider will prioritize providing information about relaxing hobbies. If the parent is having fun, the provider can prioritize providing information about hobbies that can be enjoyed by both parent and child. If the parent is in a hurry, the provider can prioritize providing information about hobbies that can be enjoyed in a short amount of time. In this way, by prioritizing information according to the parent's emotions, the parent can receive information without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0084] The information provider can provide optimal information by considering the parent's geographical location information at the time of provision. For example, the provider can provide information about events and activities held in the vicinity based on the parent's current location. The provider can provide information about hobbies and interests specific to the area where the parent lives based on the characteristics of the area where the parent lives. The provider can provide information about hobbies and interests in easily accessible locations based on the parent's geographical location information. In this way, by considering the parent's geographical location information, highly relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the parent's geographical location information as input and outputs optimal information.

[0085] The information provider can analyze the parent's social media activity and provide relevant information at the time of provision. For example, the information provider can analyze the content of the parent's social media posts and provide information on relevant hobbies and interests. The information provider can provide information on hobbies and interests based on information about accounts and groups that the parent follows. The information provider can analyze the parent's social media activity history and provide information on hobbies that the parent has shown interest in in the past. In this way, relevant information can be provided by analyzing the parent's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the parent's social media activity as input and outputs relevant information.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception unit can estimate the parent's emotions and adjust the input method for hobbies and interests based on the estimated emotions. For example, if the parent is stressed, it can provide a simple interface and minimize the input steps. If the parent is relaxed, it can provide detailed input options and suggest a customizable input method. If the parent is in a hurry, it can prioritize voice input to allow for quick input of hobbies and interests. This allows parents to input their hobbies and interests without feeling stressed by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0088] The analysis unit can estimate the parent's emotions and adjust the analysis criteria based on the estimated parent's emotions. For example, if the parent is stressed, it can prioritize analyzing relaxing hobbies. If the parent is enjoying themselves, it can prioritize analyzing hobbies that can be enjoyed by both parent and child. If the parent is in a hurry, it can prioritize analyzing hobbies that can be enjoyed in a short amount of time. In this way, by adjusting the analysis criteria according to the parent's emotions, the parent can obtain analysis results without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0089] The information provider can estimate the parent's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the parent is stressed, it can provide simple and easily understandable information. If the parent is having fun, it can provide information that includes details. If the parent is in a hurry, it can provide information that gets straight to the point. By adjusting the way the information is presented according to the parent's emotions, the parent can receive the information without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0090] The information provider can estimate the parent's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the parent is stressed, it can prioritize providing information about relaxing hobbies. If the parent is having fun, it can prioritize providing information about hobbies that can be enjoyed by both parent and child. If the parent is in a hurry, it can prioritize providing information about hobbies that can be enjoyed in a short amount of time. In this way, by prioritizing information according to the parent's emotions, the parent can receive information without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0091] The analysis unit can estimate the parent's emotions and adjust the display order of the analysis results based on the estimated parent's emotions. For example, if the parent is stressed, relaxing hobbies can be displayed preferentially. If the parent is enjoying themselves, hobbies that can be enjoyed by both parent and child can be displayed preferentially. If the parent is in a hurry, hobbies that can be enjoyed in a short time can be displayed preferentially. In this way, by adjusting the display order of the analysis results according to the parent's emotions, the parent can review the analysis results without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can estimate the parent's emotions using an AI model that takes the parent's emotions as input and outputs the parent's emotions.

[0092] The reception desk can analyze a child's past hobbies and interests and suggest the optimal input method. For example, it can automatically display hobbies that the child has frequently shown interest in in the past as candidates. It can suggest related hobbies based on events and activities the child has participated in in the past. It can predict and suggest hobbies that the child may be interested in during specific seasons or periods based on the child's past hobby history. In this way, the optimal input method can be suggested by analyzing the child's past hobbies and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can suggest the optimal input method using an AI model that takes the child's past hobbies and interests as input and outputs the optimal input method.

[0093] The reception desk can filter hobbies and interests based on the child's age and gender when the user inputs them. For example, it can automatically filter and display appropriate hobbies and interests according to the child's age. It can also suggest generally popular hobbies and interests based on the child's gender. It can suggest the most suitable hobbies and interests based on the combination of the child's age and gender. In this way, appropriate hobbies and interests can be suggested by filtering based on the child's age and gender. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can perform filtering using an AI model that takes the child's age and gender as input and outputs filtered hobbies and interests.

[0094] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of children's hobbies and interests. For example, if a child has multiple hobbies, the analysis can consider the relationships between those hobbies. If a child's hobbies depend on seasons or time of year, the analysis can consider these fluctuations. If a child's hobbies are related to specific events or activities, the analysis can consider that information. In this way, the accuracy of the analysis is improved by considering the interrelationships of children's hobbies and interests. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can improve the accuracy of its analysis by using a generative AI model that takes the interrelationships of children's hobbies and interests as input and outputs the analysis results.

[0095] The analysis unit can perform analysis while considering attribute information such as the child's age and gender. For example, it can analyze appropriate hobbies and interests according to the child's age. It can analyze generally popular hobbies and interests based on the child's gender. It can analyze the optimal hobbies and interests based on the combination of the child's age and gender. In this way, appropriate analysis results can be obtained by considering attribute information such as the child's age and gender. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes attribute information such as the child's age and gender as input and outputs analysis results.

[0096] The information provider can provide information while considering the parents' occupation and attribute information of the area where they live. For example, it can suggest hobbies and interests related to the parents' occupation. Based on the characteristics of the area where the parents live, it can suggest region-specific hobbies and interests. Based on the parents' occupation and attribute information of the area where they live, it can suggest the most suitable hobbies and interests. In this way, appropriate information can be provided by considering the parents' occupation and attribute information of the area where they live. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can provide information using an AI model that takes the parents' occupation and attribute information of the area where they live as input and outputs information.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives input from parents about their child's hobbies and interests. Parents can input hobbies such as soccer, painting, and reading using a web form or mobile application. The reception desk sends the entered information to the generating AI. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and search for children with common interests. The generation AI uses a text generation AI (e.g., LLM) to identify children with common interests based on data collected from other families. For example, it matches children who like soccer with each other. Step 3: The provisioning unit provides information about children with common interests identified by the analysis unit. The provisioning unit can also provide information such as the parents' occupations and the area where they live, to facilitate communication between parents. Processing in the provisioning unit may or may not be performed using AI.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for parents to input their child's hobbies and interests. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI to search for children with common hobbies. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the analysis results to the parents. The provision unit may also be implemented, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for parents to input their child's hobbies and interests by voice. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI to search for children with common hobbies. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the analysis results to the parents. The provision unit may also be implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for parents to input their child's hobbies and interests by voice. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI to search for children with common hobbies. The provision unit is implemented, for example, by the display 343 of the headset terminal 314 and provides the analysis results to the parents. The provision unit may also be implemented, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for parents to input their child's hobbies and interests by voice. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI to search for children with common hobbies. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the analysis results to the parents. The provision unit may also be implemented by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception desk where parents enter their child's hobbies and interests, An analysis unit analyzes the information entered by the reception unit and searches for children with common hobbies, The system includes a providing unit that provides information on children who share common hobbies as identified by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide parental information The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Information will be provided with parental consent. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Identifying children with shared hobbies based on data collected from other households The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Enter your child's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide information such as the parents' occupation and the area where they live. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the parent's emotions and adjusts the input method for hobbies and interests based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze a child's past hobbies and interests and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering hobbies and interests, filtering is performed based on the child's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the parents' emotions and determines the priority of hobbies and interests to input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering hobbies and interests, the system prioritizes highly relevant hobbies and interests by considering the parent's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their hobbies and interests, the system analyzes their parents' social media activity and inputs relevant hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the parents' emotions and adjust the analysis criteria based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we consider the interrelationships between children's hobbies and interests to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the child's age and gender attributes will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the parent's emotions and adjusts the display order of the analysis results based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of children will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature on children to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the parent's emotions and adjusts how the information provided is presented based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of the child's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, we will consider the parents' occupation and the attribute information of the area where they live. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the parents' emotions and prioritizes the information to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we will consider the parent's geographical location to provide the most appropriate information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the parents' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where parents enter their child's hobbies and interests, An analysis unit analyzes the information entered by the reception unit and searches for children with common hobbies, The system includes a providing unit that provides information on children who share common hobbies as identified by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provide parental information The system according to feature 1.

3. The aforementioned supply unit is, Information will be provided with parental consent. The system according to feature 1.

4. The aforementioned analysis unit, Identifying children with shared hobbies based on data collected from other households The system according to feature 1.

5. The aforementioned reception unit is Enter your child's hobbies and interests. The system according to feature 1.

6. The aforementioned supply unit is, Provide information such as the parents' occupation and the area where they live. The system according to feature 1.

7. The aforementioned reception unit is It estimates the parent's emotions and adjusts the input method for hobbies and interests based on the estimated parent's emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze a child's past hobbies and interests and suggest the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering hobbies and interests, filtering is performed based on the child's age and gender. The system according to feature 1.

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