System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system includes an input unit, a generation unit, a provision unit, and an accumulation unit. The input unit allows the parent and child to input daily events and emotions. The generation unit analyzes the information input by the input unit and generates a specific proposal or activity for promoting communication between a parent and a child. The providing unit provides the parent and child with the specific proposal or activity generated by the generating unit. The storage unit stores a communication history between a parent and a child to improve a long-term relationship.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not resolved the lack of communication between parents and children, and there is room for improvement.

[0005] The system according to the embodiment aims to promote communication between parents and children and improve their long-term relationship. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, a provision unit, and a storage unit. The input unit allows a parent and child to input daily events and emotions. The generation unit analyzes the information input by the input unit and generates specific suggestions or activities to promote communication between the parent and child. The provision unit provides the specific suggestions or activities generated by the generation unit to the parent and child. The storage unit stores a communication history between the parent and child to improve the long-term relationship. [Effects of the Invention]

[0007] The system according to the embodiment can promote communication between parents and children and improve their long-term relationship. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication promotion system according to an embodiment of the present invention is a system in which parents and children input daily events and emotions, which are analyzed by a generation AI to generate and provide suggestions and activities. The communication promotion system promotes communication between parents and children by having parents and children input daily events and emotions, which are analyzed by a generation AI to generate and provide suggestions and activities. For example, if a parent and child share a common hobby, the system suggests activities related to that hobby. The system also analyzes changes in emotions between the parent and child and generates messages to encourage communication at appropriate times. Furthermore, the system accumulates communication history to improve long-term relationships. For example, a parent and child install an app on their respective smartphones or tablets and input daily events and emotions. This information is analyzed by the generation AI to generate suggestions and activities to promote communication between the parent and child. For example, if a parent and child share a common hobby, the system suggests activities related to that hobby. The system also analyzes changes in emotions between the parent and child and generates messages to encourage communication at appropriate times. In this way, the communication promotion system naturally promotes communication between parents and children and deepens their relationships. The communication promotion system effectively promotes communication between parents and children and deepens their relationships. For example, if a parent and child share a common hobby, the system can encourage communication between them by suggesting activities related to that hobby.The system can also deepen the relationship between parent and child by analyzing changes in their emotions and generating messages that encourage communication at appropriate times.

[0029] A communication promotion system according to an embodiment includes an input unit, a generation unit, a provision unit, and a storage unit. The input unit allows parents and children to input daily events and emotions. Parents and children can input daily events and emotions using, for example, a smartphone or tablet. The input unit allows parents and children to input, for example, events at school, events at work, feelings, and thoughts. The generation unit uses a generation AI to analyze the information input by the input unit and generate suggestions and activities to promote communication between parents and children. For example, if a parent and child share a common hobby, the generation unit suggests activities related to that hobby. The generation unit can also analyze changes in emotions between parents and children and generate messages to encourage communication at appropriate times. For example, if both parents and children like cooking, the generation unit can provide recipes for cooking together or information about cooking classes. If a child is feeling stressed at school, the generation unit can send the parent a message such as, "Make time to relax with your child." The provision unit provides the suggestions and activities generated by the generation unit to the parent and child. The providing unit can notify the parent and child of suggestions and activities using, for example, a smartphone or tablet. For example, if the parent and child have a common hobby, the providing unit can notify them of activities related to that hobby. The providing unit can also analyze changes in emotions between the parent and child and notify them of messages encouraging communication at appropriate times. The storage unit stores the communication history between the parent and child to improve the long-term relationship. For example, the storage unit can analyze how the relationship between the parent and child is changing based on the content of past communication and suggest a direction for future communication. As a result, the communication promotion system according to the embodiment can effectively promote communication between the parent and child and deepen the relationship.

[0030] If a parent and child have a specific common hobby, the generation unit can suggest activities related to that hobby. For example, if both parent and child like cooking, the generation unit can provide information on recipes and cooking classes for cooking together. If both parent and child like sports, the generation unit can also provide information on events and clubs for playing sports together. If both parent and child like music, the generation unit can also provide information on concerts and music classes for enjoying music together. This can promote communication between parent and child through common hobbies.

[0031] The storage unit can analyze how the relationship between parent and child is changing based on the content of past communication and suggest specific communication directions. The storage unit can, for example, save the content of past communication as a text log or audio recording. The storage unit can, for example, analyze the frequency of communication and emotional tone to identify how the relationship between parent and child is changing. The storage unit can also suggest future communication directions based on the content of past communication. For example, it can provide suggestions for increasing positive topics and topics for problem-solving. This makes it possible to make suggestions for improving long-term relationships.

[0032] If a parent and child specifically like cooking, the providing unit can provide recipes for cooking together and information on cooking classes. For example, if both a parent and child like cooking, the providing unit can provide recipes for cooking together. The providing unit can provide recipes for Japanese food, Western food, desserts, etc. The providing unit can also provide information for a parent and child to participate in a cooking class together. For example, the providing unit can provide information such as the location and time of the cooking class and the participation fee. This can promote communication between parent and child through a common hobby.

[0033] The input unit can analyze the past input history of the parent and child and select a specific input method. For example, the input unit analyzes the past input history of the parent and child and selects the optimal input method. For example, if the parent has preferred voice input in the past, the input unit can preferentially suggest voice input. Also, if the child has frequently used text input in the past, the input unit can preferentially suggest text input. Also, the input unit can suggest the input method most used in a specific time period based on the past input history of the parent and child. This makes it possible to provide the optimal input method based on the past history.

[0034] The input unit can filter data based on the current living situation and areas of interest of the parent and child when inputting data. The input unit can filter data based on, for example, the current living situation and areas of interest of the parent and child. For example, when the parent is at work, the input unit can prioritize input of work-related events and emotions. Furthermore, when the child is at school, the input unit can prioritize input of school events and emotions. Furthermore, when the parent and child are spending a holiday together, the input unit can prioritize input of home events and emotions. This can promote appropriate input according to the living situation and areas of interest.

[0035] The input unit can select a specific input means according to the input method of the parent and child at the time of input. For example, the input unit selects a specific input means according to the input method of the parent and child. For example, if the parent prefers voice input, the input unit can provide voice input preferentially. Also, if the child prefers text input, the input unit can provide text input preferentially. Also, if the parent and child prefer image input, the input unit can provide image input preferentially. This makes it possible to provide the optimal means according to the input method.

[0036] The input unit can prioritize input of highly relevant information in consideration of the geographical location information of the parent and child when inputting information. For example, the input unit prioritizes input of highly relevant information in consideration of the geographical location information of the parent and child. For example, when a parent is at work, the input unit can prioritize input of events and emotions related to the workplace. Furthermore, when a child is at school, the input unit can prioritize input of events and emotions at school. Furthermore, when a parent and child are traveling together, the input unit can prioritize input of events and emotions related to the trip. This makes it possible to promote appropriate input based on the geographical location information.

[0037] The input unit can analyze the social media activities of the parent and child at the time of input and input related information. The input unit can, for example, analyze the social media activities of the parent and child and input related information. The input unit can prompt input based on, for example, events shared by the parent on social media. The input unit can also prompt input based on emotions shared by the child on social media. The input unit can also prompt input based on events and emotions shared by both the parent and child on social media. This makes it possible to encourage appropriate input based on social media activities.

[0038] The input unit can customize the input method by reflecting past feedback from the parent and child when inputting. The input unit customizes the input method by reflecting, for example, past feedback from the parent and child. For example, if the parent has preferred voice input in the past, the input unit can provide voice input preferentially. Furthermore, if the child has preferred text input in the past, the input unit can provide text input preferentially. Furthermore, if the parent and child have preferred image input in the past, the input unit can provide image input preferentially. This makes it possible to provide the optimal input method based on past feedback.

[0039] The generation unit can adjust the level of detail of the proposals and activities based on the parent-child relationship when generating them. The generation unit adjusts the level of detail of the proposals and activities based on, for example, the parent-child relationship. For example, if the parent-child relationship is good, the generation unit generates detailed proposals and activities. Furthermore, if the parent-child relationship is distant, the generation unit can generate simple proposals and activities. Furthermore, if the parent-child relationship is improving, the generation unit can also generate proposals and activities to deepen the relationship. This makes it possible to provide proposals and activities with an appropriate level of detail according to the relationship.

[0040] The generation unit can apply different generation algorithms depending on the hobbies and interests of the parent and child during generation. For example, the generation unit applies different generation algorithms depending on the hobbies and interests of the parent and child. For example, if the parent's hobby is cooking, the generation unit can generate suggestions and activities related to cooking. Furthermore, if the child's hobby is sports, the generation unit can generate suggestions and activities related to sports. Furthermore, if both the parent and child are into music, the generation unit can generate suggestions and activities related to music. This makes it possible to provide appropriate suggestions and activities according to the hobbies and interests.

[0041] During generation, the generation unit can improve the accuracy of generation by referring to past generation results between the parent and child. The generation unit improves the accuracy of generation by referring to, for example, past generation results between the parent and child. The generation unit improves the accuracy of generation by referring to, for example, past generation results between the parent and child. The generation unit can also improve the accuracy of generation based on proposals and activities that the parent and child have liked in the past. The generation unit can also improve the accuracy of generation based on proposals and activities that the parent and child have avoided in the past. The generation unit can also improve the accuracy of generation based on past feedback between the parent and child. This makes it possible to provide highly accurate proposals and activities based on past generation results.

[0042] At the time of generation, the generation unit can determine the priority of suggestions and activities based on the parent-child lifestyle rhythm. The generation unit determines the priority of suggestions and activities based on, for example, the parent-child lifestyle rhythm. For example, if the parent is at work, the generation unit can prioritize suggestions and activities that can be done after work. Furthermore, if the child is at school, the generation unit can prioritize suggestions and activities that can be done after school. Furthermore, if the parent and child are both spending a holiday, the generation unit can prioritize suggestions and activities that can be done on the holiday. This makes it possible to provide appropriate suggestions and activities according to the lifestyle rhythm.

[0043] The generation unit can adjust the order of suggestions and activities based on the parent-child relationship at the time of generation. The generation unit adjusts the order of suggestions and activities based on, for example, the parent-child relationship. For example, if the parent-child relationship is good, the generation unit prioritizes suggestions and activities to deepen the relationship. Furthermore, if the parent-child relationship is distant, the generation unit can prioritize suggestions and activities to repair the relationship. Furthermore, if the parent-child relationship is improving, the generation unit can also prioritize suggestions and activities to maintain the relationship. This makes it possible to provide suggestions and activities in an appropriate order according to the relationship.

[0044] The generation unit can adjust the use of technical terms in the suggestions and activities according to the expertise levels of the parent and child during generation. The generation unit adjusts the use of technical terms in the suggestions and activities according to the expertise levels of the parent and child, for example. For example, if the parent has technical knowledge, the generation unit can generate suggestions and activities that use a lot of technical terms. Furthermore, if the child does not have technical knowledge, the generation unit can generate suggestions and activities that avoid technical terms. Furthermore, if both the parent and the child have technical knowledge, the generation unit can also generate suggestions and activities that use technical terms appropriately. This makes it possible to provide appropriate suggestions and activities according to the expertise levels.

[0045] At the time of provision, the provision unit can select the optimal provision method by referring to the past provision history of the parent and child. The provision unit selects the optimal provision method by referring to, for example, the past provision history of the parent and child. The provision unit selects the optimal provision method based on, for example, a provision method that the parent preferred in the past. The provision unit can also select the optimal provision method based on a provision method that the child preferred in the past. The provision unit can also select the optimal provision method based on the past provision history of the parent and child. This makes it possible to provide the optimal provision method based on the past provision history.

[0046] The providing unit can customize the content to be provided based on the current living situation of the parent and child at the time of providing. The providing unit customizes the content to be provided based on, for example, the current living situation of the parent and child. For example, if the parent is at work, the providing unit can provide suggestions and activities that can be done after work. Furthermore, if the child is at school, the providing unit can provide suggestions and activities that can be done after school. Furthermore, if the parent and child are spending a holiday together, the providing unit can provide suggestions and activities that can be done on the holiday. In this way, it is possible to provide content appropriate to the living situation.

[0047] The providing unit can improve the providing method by reflecting parent-child feedback when providing the information. The providing unit improves the providing method by reflecting parent-child feedback, for example. For example, if a parent provides feedback on a previously provided suggestion or activity, the providing unit improves the providing method based on that feedback. Furthermore, if a child provides feedback on a previously provided suggestion or activity, the providing unit can improve the providing method based on that feedback. Furthermore, if both a parent and a child provide feedback on a previously provided suggestion or activity, the providing unit can also improve the providing method based on that feedback. This makes it possible to provide an optimal providing method based on feedback.

[0048] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the parent and child when providing the information. For example, the providing unit selects the optimal providing method by taking into consideration the geographical location information of the parent and child. For example, if the parent is at work, the providing unit can provide suggestions and activities related to the workplace. Furthermore, if the child is at school, the providing unit can provide suggestions and activities related to the school. Furthermore, if the parent and child are traveling together, the providing unit can also provide suggestions and activities related to travel. In this way, an appropriate providing method can be provided based on the geographical location information.

[0049] At the time of provision, the provision unit can analyze the social media activities of the parent and child to suggest content to be provided. For example, the provision unit analyzes the social media activities of the parent and child to suggest content to be provided. For example, the provision unit can provide suggestions and activities based on events shared by the parent on social media. The provision unit can also provide suggestions and activities based on emotions shared by the child on social media. The provision unit can also provide suggestions and activities based on events and emotions shared by both the parent and child on social media. This makes it possible to provide appropriate content to be provided based on social media activities.

[0050] The providing unit can customize the content to be provided by reflecting past feedback from the parent and child when providing the content. The providing unit customizes the content to be provided by reflecting, for example, past feedback from the parent and child. For example, if the parent provides feedback on suggestions or activities provided in the past, the providing unit customizes the content to be provided based on that feedback. Furthermore, if the child provides feedback on suggestions or activities provided in the past, the providing unit can customize the content to be provided based on that feedback. Furthermore, if both the parent and child provide feedback on suggestions or activities provided, the providing unit can also customize the content to be provided based on that feedback. This makes it possible to provide optimal content to be provided based on past feedback.

[0051] The storage unit can optimize the storage algorithm by referring to past storage data during storage. The storage unit, for example, optimizes the storage algorithm by referring to past storage data. The storage unit, for example, selects an optimal data storage algorithm based on the past storage data. The storage unit can also analyze trends in the past storage data and optimize the storage algorithm. The storage unit can also optimize the storage algorithm based on feedback of the past storage data. This makes it possible to provide an optimal storage algorithm based on past data.

[0052] The storage unit can update the stored data by reflecting parent-child feedback when storing the data. The storage unit updates the stored data by reflecting, for example, parent-child feedback. For example, if a parent provides feedback on a previously provided suggestion or activity, the storage unit updates the stored data based on that feedback. Furthermore, if a child provides feedback on a previously provided suggestion or activity, the storage unit can update the stored data based on that feedback. Furthermore, if both a parent and a child provide feedback on a previously provided suggestion or activity, the storage unit can also update the stored data based on that feedback. This makes it possible to provide optimal stored data based on feedback.

[0053] The storage unit can optimize the stored data by referring to the past communication history between the parent and child when storing data. The storage unit, for example, optimizes the stored data by referring to the past communication history between the parent and child. The storage unit, for example, selects an optimal data storage algorithm based on the past communication history between the parent and child. The storage unit can also analyze trends in the past communication history between the parent and child and optimize the stored data. The storage unit can also optimize the stored data based on feedback of the past communication history between the parent and child. This makes it possible to provide optimal stored data based on the past communication history.

[0054] The storage unit can weight the stored data based on the parent-child lifestyle rhythms when storing the data. The storage unit weights the stored data based on the parent-child lifestyle rhythms, for example. For example, if the parent is at work, the storage unit weights work-related data. Furthermore, if the child is at school, the storage unit can weight school-related data. Furthermore, if the parent and child are spending a holiday together, the storage unit can weight holiday-related data. This allows data to be stored with appropriate weighting according to the lifestyle rhythms.

[0055] The storage unit can integrate information from different data sources to enrich the stored data during storage. For example, the storage unit integrates information from different data sources to enrich the stored data. For example, the storage unit integrates and stores data from a parent's social media activities. The storage unit can also integrate and store data from a child's social media activities. The storage unit can also integrate and store data from common social media activities between a parent and child. This allows the data to be enriched by integrating information from different data sources.

[0056] The storage unit can optimize the stored data by reflecting past feedback from the parent and child when storing the data. The storage unit optimizes the stored data by reflecting, for example, past feedback from the parent and child. For example, if a parent provides feedback on a proposal or activity provided in the past, the storage unit optimizes the stored data based on that feedback. Furthermore, if a child provides feedback on a proposal or activity provided in the past, the storage unit can optimize the stored data based on that feedback. Furthermore, if both a parent and a child provide feedback on a proposal or activity provided in the past, the storage unit can also optimize the stored data based on that feedback. This makes it possible to provide optimal stored data based on past feedback.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The communication promotion system can further include a health management unit that acquires parent-child health data. The health management unit monitors the health status of parent and child and generates health-related activities and suggestions. For example, if both parent and child are not getting enough exercise, the health management unit can suggest a plan for them to exercise together. It can also analyze the dietary habits of parent and child and suggest balanced meal menus. It can also analyze parent-child sleep data and provide advice on improving sleep quality. This makes it possible to promote communication while supporting the health of parent and child.

[0059] The communication promotion system can further include a learning support unit that acquires learning data of parents and children. The learning support unit monitors the learning progress of parents and children and generates learning activities and suggestions. For example, if a child is weak in a particular subject, the learning support unit can suggest learning activities related to that subject. The system can also provide methods for parents to support their children's learning. It can also suggest online courses and learning materials for parents and children to study together. This makes it possible to promote communication between parents and children while supporting their learning.

[0060] The communication promotion system can further include a travel support unit that proposes a customized travel plan based on the hobbies and interests of parent and child. The travel support unit analyzes the hobbies and interests of parent and child and proposes a travel plan based on the results. For example, if parent and child both like nature, the travel support unit can propose a travel plan to enjoy nature. Also, if parent and child are interested in history, the travel support unit can propose a travel plan to visit historical places. It can also propose a hot spring trip or resort plan where parent and child can relax together. This makes it possible to promote communication through travel that suits the hobbies and interests of parent and child.

[0061] The communication promotion system may further include a DIY support unit that suggests DIY projects based on the hobbies and interests of parents and children. The DIY support unit analyzes the hobbies and interests of parents and children and suggests DIY projects based on the results. For example, if both parents and children like handicrafts, a handicraft project may be suggested. If both parents and children like woodworking, a woodworking project may be suggested. Furthermore, if both parents and children like gardening, a gardening project may be suggested. This makes it possible to promote communication through DIY projects that match the hobbies and interests of parents and children.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: Parents and children input their daily events and emotions into the input unit. To input their daily events and emotions, parents and children can use, for example, a smartphone or tablet. For example, the input unit allows parents and children to input events at school, events at work, their feelings, thoughts, etc. Step 2: The generation unit uses a generation AI to analyze the information input by the input unit and generate suggestions and activities to promote communication between parent and child. For example, if parent and child have a common hobby, the generation unit will suggest activities related to that hobby. The generation unit can also analyze changes in emotions between parent and child and generate messages to encourage communication at appropriate times. For example, if both parent and child like cooking, the generation unit can provide recipes for cooking together or information about cooking classes. Furthermore, if a child is feeling stressed at school, the generation unit can send the parent a message such as, "Make time to relax with your child." Step 3: The providing unit provides the parent and child with the suggestions and activities generated by the generating unit. The providing unit can notify the parent and child of the suggestions and activities using, for example, a smartphone or tablet. For example, if the parent and child have a common hobby, the providing unit can notify them of activities related to that hobby. The providing unit can also analyze changes in emotions between the parent and child and notify them of messages encouraging communication at appropriate times. Step 4: The storage unit accumulates the communication history between parent and child and aims to improve the relationship over the long term. For example, the storage unit can analyze how the relationship between parent and child is changing based on the content of past communication and suggest future directions for communication.

[0064] (Example 2) A communication promotion system according to an embodiment of the present invention is a system in which parents and children input daily events and emotions, which are analyzed by a generation AI to generate and provide suggestions and activities. The communication promotion system promotes communication between parents and children by having parents and children input daily events and emotions, which are analyzed by a generation AI to generate and provide suggestions and activities. For example, if a parent and child share a common hobby, the system suggests activities related to that hobby. The system also analyzes changes in emotions between the parent and child and generates messages to encourage communication at appropriate times. Furthermore, the system accumulates communication history to improve long-term relationships. For example, a parent and child install an app on their respective smartphones or tablets and input daily events and emotions. This information is analyzed by the generation AI to generate suggestions and activities to promote communication between the parent and child. For example, if a parent and child share a common hobby, the system suggests activities related to that hobby. The system also analyzes changes in emotions between the parent and child and generates messages to encourage communication at appropriate times. In this way, the communication promotion system naturally promotes communication between parents and children and deepens their relationships. The communication promotion system effectively promotes communication between parents and children and deepens their relationships. For example, if a parent and child share a common hobby, the system can encourage communication between them by suggesting activities related to that hobby.The system can also deepen the relationship between parent and child by analyzing changes in their emotions and generating messages that encourage communication at appropriate times.

[0065] A communication promotion system according to an embodiment includes an input unit, a generation unit, a provision unit, and a storage unit. The input unit allows parents and children to input daily events and emotions. Parents and children can input daily events and emotions using, for example, a smartphone or tablet. The input unit allows parents and children to input, for example, events at school, events at work, feelings, and thoughts. The generation unit uses a generation AI to analyze the information input by the input unit and generate suggestions and activities to promote communication between parents and children. For example, if a parent and child share a common hobby, the generation unit suggests activities related to that hobby. The generation unit can also analyze changes in emotions between parents and children and generate messages to encourage communication at appropriate times. For example, if both parents and children like cooking, the generation unit can provide recipes for cooking together or information about cooking classes. If a child is feeling stressed at school, the generation unit can send the parent a message such as, "Make time to relax with your child." The provision unit provides the suggestions and activities generated by the generation unit to the parent and child. The providing unit can notify the parent and child of suggestions and activities using, for example, a smartphone or tablet. For example, if the parent and child have a common hobby, the providing unit can notify them of activities related to that hobby. The providing unit can also analyze changes in emotions between the parent and child and notify them of messages encouraging communication at appropriate times. The storage unit stores the communication history between the parent and child to improve the long-term relationship. For example, the storage unit can analyze how the relationship between the parent and child is changing based on the content of past communication and suggest a direction for future communication. As a result, the communication promotion system according to the embodiment can effectively promote communication between the parent and child and deepen the relationship.

[0066] If a parent and child have a specific common hobby, the generation unit can suggest activities related to that hobby. For example, if both parent and child like cooking, the generation unit can provide information on recipes and cooking classes for cooking together. If both parent and child like sports, the generation unit can also provide information on events and clubs for playing sports together. If both parent and child like music, the generation unit can also provide information on concerts and music classes for enjoying music together. This can promote communication between parent and child through common hobbies.

[0067] The generation unit can analyze changes in emotions between parents and children and generate messages that encourage communication at specific times. The generation unit can detect changes in emotions between parents and children using, for example, text analysis or facial expression recognition. For example, if a child is feeling stressed at school, the generation unit can send a message to the parent saying, "Let's make time to relax together." If a parent is tired from work, the generation unit can also send a message to the child saying, "Let's make time for Dad (Mom) to relax." The generation unit can also detect times when both parents and children are relaxing and send a message like, "Let's have some fun together" at that time. This makes it possible to promote communication at appropriate times according to changes in emotions.

[0068] The storage unit can analyze how the relationship between parent and child is changing based on the content of past communication and suggest specific communication directions. The storage unit can, for example, save the content of past communication as a text log or audio recording. The storage unit can, for example, analyze the frequency of communication and emotional tone to identify how the relationship between parent and child is changing. The storage unit can also suggest future communication directions based on the content of past communication. For example, it can provide suggestions for increasing positive topics and topics for problem-solving. This makes it possible to make suggestions for improving long-term relationships.

[0069] If a parent and child specifically like cooking, the providing unit can provide recipes for cooking together and information on cooking classes. For example, if both a parent and child like cooking, the providing unit can provide recipes for cooking together. The providing unit can provide recipes for Japanese food, Western food, desserts, etc. The providing unit can also provide information for a parent and child to participate in a cooking class together. For example, the providing unit can provide information such as the location and time of the cooking class and the participation fee. This can promote communication between parent and child through a common hobby.

[0070] If a child is specifically feeling stressed at school, the providing unit can send a message to the parent such as, "Make time to relax with your child." For example, if a child is feeling stressed at school, the providing unit can send a message to the parent such as, "Make time to relax with your child." The providing unit can determine stress based on specific situations, such as a drop in grades or problems with friendships. The providing unit can also suggest specific activities for relaxation. For example, the providing unit can suggest activities such as taking a walk, reading, or listening to music. This can reduce the child's stress and promote communication between parent and child.

[0071] The input unit can estimate the emotions of the parent and child and adjust the timing of specific inputs based on the estimated emotions. The input unit can estimate the emotions of the parent and child using, for example, text analysis or voice analysis. For example, if the parent is feeling stressed, the input unit can reduce the notification prompting input and send the notification at a time when the parent is relaxed. Furthermore, if the child is excited, the input unit can immediately send a notification prompting input and prompt input while the child's emotions are heightened. Furthermore, the input unit can detect a time period when both the parent and child are relaxed and send a notification prompting input at that time. This makes it possible to encourage input at an appropriate time according to the parent's emotions.

[0072] The input unit can analyze the past input history of the parent and child and select a specific input method. For example, the input unit analyzes the past input history of the parent and child and selects the optimal input method. For example, if the parent has preferred voice input in the past, the input unit can preferentially suggest voice input. Also, if the child has frequently used text input in the past, the input unit can preferentially suggest text input. Also, the input unit can suggest the input method most used in a specific time period based on the past input history of the parent and child. This makes it possible to provide the optimal input method based on the past history.

[0073] The input unit can filter data based on the current living situation and areas of interest of the parent and child when inputting data. The input unit can filter data based on, for example, the current living situation and areas of interest of the parent and child. For example, when the parent is at work, the input unit can prioritize input of work-related events and emotions. Furthermore, when the child is at school, the input unit can prioritize input of school events and emotions. Furthermore, when the parent and child are spending a holiday together, the input unit can prioritize input of home events and emotions. This can promote appropriate input according to the living situation and areas of interest.

[0074] The input unit can select a specific input means according to the input method of the parent and child at the time of input. For example, the input unit selects a specific input means according to the input method of the parent and child. For example, if the parent prefers voice input, the input unit can provide voice input preferentially. Also, if the child prefers text input, the input unit can provide text input preferentially. Also, if the parent and child prefer image input, the input unit can provide image input preferentially. This makes it possible to provide the optimal means according to the input method.

[0075] The input unit can estimate the emotions of the parent and child and determine the priority of the content to be input based on the estimated emotions. The input unit can estimate the emotions of the parent and child using text analysis or voice analysis, for example. For example, if the parent is feeling stressed, the input unit can preferentially prompt the input of events and emotions related to stress. Furthermore, if the child is happy, the input unit can preferentially prompt the input of events and emotions related to joy. Furthermore, if both the parent and child are relaxed, the input unit can preferentially prompt the input of events and emotions related to relaxation. This makes it possible to encourage the input of appropriate content according to emotions.

[0076] The input unit can prioritize input of highly relevant information in consideration of the geographical location information of the parent and child when inputting information. For example, the input unit prioritizes input of highly relevant information in consideration of the geographical location information of the parent and child. For example, when a parent is at work, the input unit can prioritize input of events and emotions related to the workplace. Furthermore, when a child is at school, the input unit can prioritize input of events and emotions at school. Furthermore, when a parent and child are traveling together, the input unit can prioritize input of events and emotions related to the trip. This makes it possible to promote appropriate input based on the geographical location information.

[0077] The input unit can analyze the social media activities of the parent and child at the time of input and input related information. The input unit can, for example, analyze the social media activities of the parent and child and input related information. The input unit can prompt input based on, for example, events shared by the parent on social media. The input unit can also prompt input based on emotions shared by the child on social media. The input unit can also prompt input based on events and emotions shared by both the parent and child on social media. This makes it possible to encourage appropriate input based on social media activities.

[0078] The input unit can customize the input method by reflecting past feedback from the parent and child when inputting. The input unit customizes the input method by reflecting, for example, past feedback from the parent and child. For example, if the parent has preferred voice input in the past, the input unit can provide voice input preferentially. Furthermore, if the child has preferred text input in the past, the input unit can provide text input preferentially. Furthermore, if the parent and child have preferred image input in the past, the input unit can provide image input preferentially. This makes it possible to provide the optimal input method based on past feedback.

[0079] The generation unit can estimate the emotions of the parent and child and adjust the way in which suggestions and activities are expressed based on the estimated emotions. The generation unit can estimate the emotions of the parent and child using text analysis or voice analysis, for example. For example, if the parent is relaxed, the generation unit can generate suggestions and activities with a relaxed atmosphere. Furthermore, if the child is excited, the generation unit can generate suggestions and activities that enhance the excitement. Furthermore, if both the parent and child are feeling stressed, the generation unit can also generate suggestions and activities that reduce stress. This makes it possible to provide suggestions and activities in an appropriate expression method according to the emotions.

[0080] The generation unit can adjust the level of detail of the proposals and activities based on the parent-child relationship when generating them. The generation unit adjusts the level of detail of the proposals and activities based on, for example, the parent-child relationship. For example, if the parent-child relationship is good, the generation unit generates detailed proposals and activities. Furthermore, if the parent-child relationship is distant, the generation unit can generate simple proposals and activities. Furthermore, if the parent-child relationship is improving, the generation unit can also generate proposals and activities to deepen the relationship. This makes it possible to provide proposals and activities with an appropriate level of detail according to the relationship.

[0081] The generation unit can apply different generation algorithms depending on the hobbies and interests of the parent and child during generation. For example, the generation unit applies different generation algorithms depending on the hobbies and interests of the parent and child. For example, if the parent's hobby is cooking, the generation unit can generate suggestions and activities related to cooking. Furthermore, if the child's hobby is sports, the generation unit can generate suggestions and activities related to sports. Furthermore, if both the parent and child are into music, the generation unit can generate suggestions and activities related to music. This makes it possible to provide appropriate suggestions and activities according to the hobbies and interests.

[0082] During generation, the generation unit can improve the accuracy of generation by referring to past generation results between the parent and child. The generation unit improves the accuracy of generation by referring to, for example, past generation results between the parent and child. The generation unit improves the accuracy of generation by referring to, for example, past generation results between the parent and child. The generation unit can also improve the accuracy of generation based on proposals and activities that the parent and child have liked in the past. The generation unit can also improve the accuracy of generation based on proposals and activities that the parent and child have avoided in the past. The generation unit can also improve the accuracy of generation based on past feedback between the parent and child. This makes it possible to provide highly accurate proposals and activities based on past generation results.

[0083] The generation unit can estimate the emotions of the parent and child and adjust the length of the suggestions and activities based on the estimated emotions. The generation unit can estimate the emotions of the parent and child using text analysis or voice analysis, for example. The generation unit can generate longer suggestions and activities if the parent is relaxed, for example. The generation unit can also generate shorter suggestions and activities if the child is in a hurry, for example. The generation unit can also generate suggestions and activities of an appropriate length if both the parent and child are having fun. This makes it possible to provide suggestions and activities of an appropriate length according to the emotions.

[0084] At the time of generation, the generation unit can determine the priority of suggestions and activities based on the parent-child lifestyle rhythm. The generation unit determines the priority of suggestions and activities based on, for example, the parent-child lifestyle rhythm. For example, if the parent is at work, the generation unit can prioritize suggestions and activities that can be done after work. Furthermore, if the child is at school, the generation unit can prioritize suggestions and activities that can be done after school. Furthermore, if the parent and child are both spending a holiday, the generation unit can prioritize suggestions and activities that can be done on the holiday. This makes it possible to provide appropriate suggestions and activities according to the lifestyle rhythm.

[0085] The generation unit can adjust the order of suggestions and activities based on the parent-child relationship at the time of generation. The generation unit adjusts the order of suggestions and activities based on, for example, the parent-child relationship. For example, if the parent-child relationship is good, the generation unit prioritizes suggestions and activities to deepen the relationship. Furthermore, if the parent-child relationship is distant, the generation unit can prioritize suggestions and activities to repair the relationship. Furthermore, if the parent-child relationship is improving, the generation unit can also prioritize suggestions and activities to maintain the relationship. This makes it possible to provide suggestions and activities in an appropriate order according to the relationship.

[0086] The generation unit can adjust the use of technical terms in the suggestions and activities according to the expertise levels of the parent and child during generation. The generation unit adjusts the use of technical terms in the suggestions and activities according to the expertise levels of the parent and child, for example. For example, if the parent has technical knowledge, the generation unit can generate suggestions and activities that use a lot of technical terms. Furthermore, if the child does not have technical knowledge, the generation unit can generate suggestions and activities that avoid technical terms. Furthermore, if both the parent and the child have technical knowledge, the generation unit can also generate suggestions and activities that use technical terms appropriately. This makes it possible to provide appropriate suggestions and activities according to the expertise levels.

[0087] The providing unit can estimate the emotions of the parent and child and adjust the method of providing suggestions and activities based on the estimated emotions. The providing unit can estimate the emotions of the parent and child using, for example, text analysis or voice analysis. For example, if the parent is relaxed, the providing unit can provide suggestions and activities in a relaxed atmosphere. Furthermore, if the child is excited, the providing unit can provide suggestions and activities in a way that enhances the excitement. Furthermore, if both the parent and child are feeling stressed, the providing unit can provide suggestions and activities in a way that reduces stress. In this way, suggestions and activities can be provided in an appropriate manner according to the emotions.

[0088] At the time of provision, the provision unit can select the optimal provision method by referring to the past provision history of the parent and child. The provision unit selects the optimal provision method by referring to, for example, the past provision history of the parent and child. The provision unit selects the optimal provision method based on, for example, a provision method that the parent preferred in the past. The provision unit can also select the optimal provision method based on a provision method that the child preferred in the past. The provision unit can also select the optimal provision method based on the past provision history of the parent and child. This makes it possible to provide the optimal provision method based on the past provision history.

[0089] The providing unit can customize the content to be provided based on the current living situation of the parent and child at the time of providing. The providing unit customizes the content to be provided based on, for example, the current living situation of the parent and child. For example, if the parent is at work, the providing unit can provide suggestions and activities that can be done after work. Furthermore, if the child is at school, the providing unit can provide suggestions and activities that can be done after school. Furthermore, if the parent and child are spending a holiday together, the providing unit can provide suggestions and activities that can be done on the holiday. In this way, it is possible to provide content appropriate to the living situation.

[0090] The providing unit can improve the providing method by reflecting parent-child feedback when providing the information. The providing unit improves the providing method by reflecting parent-child feedback, for example. For example, if a parent provides feedback on a previously provided suggestion or activity, the providing unit improves the providing method based on that feedback. Furthermore, if a child provides feedback on a previously provided suggestion or activity, the providing unit can improve the providing method based on that feedback. Furthermore, if both a parent and a child provide feedback on a previously provided suggestion or activity, the providing unit can also improve the providing method based on that feedback. This makes it possible to provide an optimal providing method based on feedback.

[0091] The providing unit can estimate the emotions of the parent and child and adjust the order in which suggestions and activities are provided based on the estimated emotions. The providing unit can estimate the emotions of the parent and child using, for example, text analysis or voice analysis. For example, if the parent is relaxed, the providing unit can provide suggestions and activities in a relaxed atmosphere. Furthermore, if the child is excited, the providing unit can provide suggestions and activities in a manner that enhances the excitement. Furthermore, if both the parent and child are feeling stressed, the providing unit can provide suggestions and activities in a manner that reduces stress. In this way, suggestions and activities can be provided in an appropriate order according to the emotions.

[0092] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the parent and child when providing the information. For example, the providing unit selects the optimal providing method by taking into consideration the geographical location information of the parent and child. For example, if the parent is at work, the providing unit can provide suggestions and activities related to the workplace. Furthermore, if the child is at school, the providing unit can provide suggestions and activities related to the school. Furthermore, if the parent and child are traveling together, the providing unit can also provide suggestions and activities related to travel. In this way, an appropriate providing method can be provided based on the geographical location information.

[0093] At the time of provision, the provision unit can analyze the social media activities of the parent and child to suggest content to be provided. For example, the provision unit analyzes the social media activities of the parent and child to suggest content to be provided. For example, the provision unit can provide suggestions and activities based on events shared by the parent on social media. The provision unit can also provide suggestions and activities based on emotions shared by the child on social media. The provision unit can also provide suggestions and activities based on events and emotions shared by both the parent and child on social media. This makes it possible to provide appropriate content to be provided based on social media activities.

[0094] The providing unit can customize the content to be provided by reflecting past feedback from the parent and child when providing the content. The providing unit customizes the content to be provided by reflecting, for example, past feedback from the parent and child. For example, if the parent provides feedback on suggestions or activities provided in the past, the providing unit customizes the content to be provided based on that feedback. Furthermore, if the child provides feedback on suggestions or activities provided in the past, the providing unit can customize the content to be provided based on that feedback. Furthermore, if both the parent and child provide feedback on suggestions or activities provided, the providing unit can also customize the content to be provided based on that feedback. This makes it possible to provide optimal content to be provided based on past feedback.

[0095] The storage unit can estimate the emotions of the parent and child and select stored data based on the estimated emotions. The storage unit can estimate the emotions of the parent and child using text analysis or voice analysis, for example. For example, if the parent is relaxed, the storage unit can preferentially store data related to the relaxed emotion. Furthermore, if the child is excited, the storage unit can preferentially store data related to the excited emotion. Furthermore, if both the parent and child are feeling stressed, the storage unit can preferentially store data related to stress. This makes it possible to store appropriate data according to the emotions.

[0096] The storage unit can optimize the storage algorithm by referring to past storage data during storage. The storage unit, for example, optimizes the storage algorithm by referring to past storage data. The storage unit, for example, selects an optimal data storage algorithm based on the past storage data. The storage unit can also analyze trends in the past storage data and optimize the storage algorithm. The storage unit can also optimize the storage algorithm based on feedback of the past storage data. This makes it possible to provide an optimal storage algorithm based on past data.

[0097] The storage unit can update the stored data by reflecting parent-child feedback when storing the data. The storage unit updates the stored data by reflecting, for example, parent-child feedback. For example, if a parent provides feedback on a previously provided suggestion or activity, the storage unit updates the stored data based on that feedback. Furthermore, if a child provides feedback on a previously provided suggestion or activity, the storage unit can update the stored data based on that feedback. Furthermore, if both a parent and a child provide feedback on a previously provided suggestion or activity, the storage unit can also update the stored data based on that feedback. This makes it possible to provide optimal stored data based on feedback.

[0098] The storage unit can optimize the stored data by referring to the past communication history between the parent and child when storing data. The storage unit, for example, optimizes the stored data by referring to the past communication history between the parent and child. The storage unit, for example, selects an optimal data storage algorithm based on the past communication history between the parent and child. The storage unit can also analyze trends in the past communication history between the parent and child and optimize the stored data. The storage unit can also optimize the stored data based on feedback of the past communication history between the parent and child. This makes it possible to provide optimal stored data based on the past communication history.

[0099] The storage unit can estimate the emotions of the parent and child and adjust the frequency of storage based on the estimated emotions. The storage unit can estimate the emotions of the parent and child using text analysis or voice analysis, for example. For example, if the parent is relaxed, the storage unit can increase the frequency of storage of data related to relaxed emotions. Furthermore, if the child is excited, the storage unit can increase the frequency of storage of data related to excited emotions. Furthermore, if both the parent and child are feeling stressed, the storage unit can also increase the frequency of storage of data related to stress. This makes it possible to store data at an appropriate frequency according to the emotions.

[0100] The storage unit can weight the stored data based on the parent-child lifestyle rhythms when storing the data. The storage unit weights the stored data based on the parent-child lifestyle rhythms, for example. For example, if the parent is at work, the storage unit weights work-related data. Furthermore, if the child is at school, the storage unit can weight school-related data. Furthermore, if the parent and child are spending a holiday together, the storage unit can weight holiday-related data. This allows data to be stored with appropriate weighting according to the lifestyle rhythms.

[0101] The storage unit can integrate information from different data sources to enrich the stored data during storage. For example, the storage unit integrates information from different data sources to enrich the stored data. For example, the storage unit integrates and stores data from a parent's social media activities. The storage unit can also integrate and store data from a child's social media activities. The storage unit can also integrate and store data from common social media activities between a parent and child. This allows the data to be enriched by integrating information from different data sources.

[0102] The storage unit can optimize the stored data by reflecting past feedback from the parent and child when storing the data. The storage unit optimizes the stored data by reflecting, for example, past feedback from the parent and child. For example, if a parent provides feedback on a proposal or activity provided in the past, the storage unit optimizes the stored data based on that feedback. Furthermore, if a child provides feedback on a proposal or activity provided in the past, the storage unit can optimize the stored data based on that feedback. Furthermore, if both a parent and a child provide feedback on a proposal or activity provided in the past, the storage unit can also optimize the stored data based on that feedback. This makes it possible to provide optimal stored data based on past feedback. === Hard Collateral 1-1 === For example, the input unit allows a parent and child to input daily events and emotions using the reception device 38 of the smart device 14. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the input unit using a generation AI to generate suggestions and activities to promote communication between the parent and child. For example, the provision unit can provide the generated suggestions and activities to the parent and child using the output device 40 of the smart device 14. For example, the accumulation unit accumulates communication history in the database 24 of the data processing device 12 to improve long-term relationships. === Hard Collateral 1-2 === For example, the input unit allows a parent and child to input daily events and emotions using the microphone 238 of the smart glasses 214. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the input unit using a generation AI to generate suggestions and activities to promote communication between the parent and child. For example, the provision unit can provide the generated suggestions and activities to the parent and child using the speaker 240 of the smart glasses 214. For example, the accumulation unit accumulates communication history in the database 24 of the data processing device 12 to improve long-term relationships. === Hard Collateral 1-3 === For example, the input unit allows a parent and child to input daily events and emotions using the microphone 238 of the headset-type terminal 314. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the input unit using a generation AI to generate suggestions and activities to promote communication between the parent and child. For example, the provision unit can provide the generated suggestions and activities to the parent and child using the speaker 240 of the headset-type terminal 314. For example, the accumulation unit accumulates communication history in the database 24 of the data processing device 12 to improve long-term relationships. === Hard Collateral 1-4 === For example, the input unit allows a parent and child to input daily events and emotions using the microphone 238 of the robot 414. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the input unit using a generation AI to generate suggestions and activities to promote communication between the parent and child. For example, the provision unit can provide the generated suggestions and activities to the parent and child using the speaker 240 of the robot 414. For example, the accumulation unit accumulates the communication history in the database 24 of the data processing device 12 to improve the long-term relationship.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The communication promotion system can further include a health management unit that acquires parent-child health data. The health management unit monitors the health status of parent and child and generates health-related activities and suggestions. For example, if both parent and child are not getting enough exercise, the health management unit can suggest a plan for them to exercise together. It can also analyze the dietary habits of parent and child and suggest balanced meal menus. It can also analyze parent-child sleep data and provide advice on improving sleep quality. This makes it possible to promote communication while supporting the health of parent and child.

[0105] The communication promotion system can further include a learning support unit that acquires learning data of parents and children. The learning support unit monitors the learning progress of parents and children and generates learning activities and suggestions. For example, if a child is weak in a particular subject, the learning support unit can suggest learning activities related to that subject. The system can also provide methods for parents to support their children's learning. It can also suggest online courses and learning materials for parents and children to study together. This makes it possible to promote communication between parents and children while supporting their learning.

[0106] The communication promotion system can further include a relaxation unit that estimates the emotions of the parent and child and suggests relaxation activities based on the estimated emotions. The relaxation unit monitors the stress levels of the parent and child and suggests activities for relaxation. For example, if both the parent and child are feeling stressed, it can suggest a yoga or meditation session where they can relax together. It can also provide music or natural sounds that help the parent and child relax. It can also provide advice on creating an environment where the parent and child can relax. This makes it possible to promote communication while reducing stress between the parent and child.

[0107] The communication promotion system can further include a travel support unit that proposes a customized travel plan based on the hobbies and interests of parent and child. The travel support unit analyzes the hobbies and interests of parent and child and proposes a travel plan based on the results. For example, if parent and child both like nature, the travel support unit can propose a travel plan to enjoy nature. Also, if parent and child are interested in history, the travel support unit can propose a travel plan to visit historical places. It can also propose a hot spring trip or resort plan where parent and child can relax together. This makes it possible to promote communication through travel that suits the hobbies and interests of parent and child.

[0108] The communication promotion system may further include an entertainment unit that estimates the emotions of the parent and child and provides entertainment content based on the estimated emotions. The entertainment unit monitors the emotions of the parent and child and provides entertainment content according to the emotions. For example, if both the parent and child are relaxed, relaxing movies and music may be provided. If the parent and child are excited, action movies and games may be provided. It may also be possible to suggest board games and puzzles that the parent and child can enjoy together. This makes it possible to promote communication through entertainment according to the emotions of the parent and child.

[0109] The communication promotion system can further include a feedback unit that estimates the emotions of the parent and child and provides feedback based on the estimated emotions. The feedback unit monitors the emotions of the parent and child and provides feedback according to the emotions. For example, if both the parent and child are relaxed, feedback to maintain a relaxed state can be provided. Also, if the parent and child are feeling stressed, feedback to reduce stress can be provided. Furthermore, if the parent and child are having fun together, feedback to share that enjoyment can be provided. In this way, communication between the parent and child can be promoted through feedback according to their emotions.

[0110] The communication promotion system can further include a learning emotion unit that estimates the emotions of the parent and child and suggests learning activities based on the estimated emotions. The learning emotion unit monitors the emotions of the parent and child and suggests learning activities according to the emotions. For example, if both the parent and child are relaxed, it can suggest an activity that allows them to study in a relaxed state. Also, if the parent and child are excited, it can suggest a learning activity that makes use of their excitement. Furthermore, if both the parent and child are feeling stressed, it can suggest an activity that allows them to study while reducing stress. In this way, communication can be promoted through learning that is according to the emotions of the parent and child.

[0111] The communication promotion system can further include an exercise and emotion unit that estimates the emotions of the parent and child and suggests an exercise activity based on the estimated emotions. The exercise and emotion unit monitors the emotions of the parent and child and suggests an exercise activity according to the emotions. For example, if both the parent and child are relaxed, the unit suggests yoga or stretching that can be done in a relaxed state. If the parent and child are excited, the unit can also suggest an energetic exercise activity. Furthermore, if both the parent and child are feeling stressed, the unit can also suggest an exercise activity to relieve stress. This makes it possible to promote communication through exercise according to the emotions of the parent and child.

[0112] The communication promotion system can further include a meal emotion unit that estimates the emotions of the parent and child and suggests a meal activity based on the estimated emotions. The meal emotion unit monitors the emotions of the parent and child and suggests a meal activity according to the emotions. For example, if both the parent and child are relaxed, it can suggest a meal menu that can be enjoyed in a relaxed state. Also, if the parent and child are excited, it can suggest an energetic meal menu. Furthermore, if both the parent and child are feeling stressed, it can also suggest a meal menu to reduce stress. This makes it possible to promote communication through meals that correspond to the emotions of the parent and child.

[0113] The communication promotion system may further include a DIY support unit that suggests DIY projects based on the hobbies and interests of parents and children. The DIY support unit analyzes the hobbies and interests of parents and children and suggests DIY projects based on the results. For example, if both parents and children like handicrafts, a handicraft project may be suggested. If both parents and children like woodworking, a woodworking project may be suggested. Furthermore, if both parents and children like gardening, a gardening project may be suggested. This makes it possible to promote communication through DIY projects that match the hobbies and interests of parents and children.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: Parents and children input their daily events and emotions into the input unit. To input their daily events and emotions, parents and children can use, for example, a smartphone or tablet. For example, the input unit allows parents and children to input events at school, events at work, their feelings, thoughts, etc. Step 2: The generation unit uses a generation AI to analyze the information input by the input unit and generate suggestions and activities to promote communication between parent and child. For example, if parent and child have a common hobby, the generation unit will suggest activities related to that hobby. The generation unit can also analyze changes in emotions between parent and child and generate messages to encourage communication at appropriate times. For example, if both parent and child like cooking, the generation unit can provide recipes for cooking together or information about cooking classes. Furthermore, if a child is feeling stressed at school, the generation unit can send the parent a message such as, "Make time to relax with your child." Step 3: The providing unit provides the parent and child with the suggestions and activities generated by the generating unit. The providing unit can notify the parent and child of the suggestions and activities using, for example, a smartphone or tablet. For example, if the parent and child have a common hobby, the providing unit can notify them of activities related to that hobby. The providing unit can also analyze changes in emotions between the parent and child and notify them of messages encouraging communication at appropriate times. Step 4: The storage unit accumulates the communication history between parent and child and aims to improve the relationship over the long term. For example, the storage unit can analyze how the relationship between parent and child is changing based on the content of past communication and suggest future directions for communication.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section where parents and children input their daily events and emotions; a generation unit that analyzes the information input by the input unit and generates specific suggestions or activities to promote communication between parent and child; a providing unit that provides the parent and child with the specific proposal or activity generated by the generating unit; and an accumulation unit that accumulates communication history between parents and children and aims to improve long-term relationships. A system characterized by:

2. The generation unit If you and your child have a specific hobby in common, suggest activities related to that hobby.

2. The system of claim 1.

3. The generation unit Analyzing changes in emotions between parents and children and generating messages to encourage communication at specific times 2. The system of claim 1.

4. The storage unit is Based on the content of past communication, we analyze how the relationship between parent and child is changing and propose specific directions for communication.

2. The system of claim 1.

5. The providing unit If parents and children specifically like cooking, provide them with recipes and information about cooking classes to cook together.

2. The system of claim 1.

6. The input unit Estimate the emotions of parents and children and adjust the timing of specific inputs based on the estimated emotions 2. The system of claim 1.

7. The input unit Analyze the parent and child's past input history and select a specific input method 2. The system of claim 1.

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