System

Abstract

An object of the system according to the embodiment is to propose an optimal communication method for maintaining a good parent-child relationship with an adolescent child.SOLUTION: A system according to an embodiment includes an emotion analysis unit, a communication proposal unit, and a history recording unit. The emotion analysis unit analyzes an emotion of the child. A communication proposal part proposes an optimum communication method on the basis of the feeling data analyzed by the feeling analysis part. The history recording unit records a communication history between a parent and a child.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it has been difficult to find the optimal communication method for maintaining a good parent-child relationship with adolescent children.

[0005] The system according to the embodiment aims to propose an optimal communication method for maintaining a good parent-child relationship with an adolescent child. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion analysis unit, a communication suggestion unit, and a history recording unit. The emotion analysis unit analyzes the child's emotions. The communication suggestion unit suggests an optimal communication method based on the emotion data analyzed by the emotion analysis unit. The history recording unit records the communication history between parent and child. [Effects of the Invention]

[0007] The system according to the embodiment can suggest an optimal communication method for maintaining a good parent-child relationship with an adolescent child. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The communication support system according to the embodiment of the present invention is a system that analyzes a child's emotions, proposes an optimal communication method, and records the communication history between parent and child. As a result, the communication support system can maintain a good parent-child relationship with adolescent children and facilitate smooth communication.

[0029] A communication support system according to an embodiment includes an emotion analysis unit, a communication suggestion unit, and a history recording unit. The emotion analysis unit analyzes a child's emotions. For example, it analyzes the child's facial expressions, tone of voice, and word choice to understand the child's current emotional state. The emotion analysis unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze emotions based on the child's facial expressions, voice data, and conversation content. For example, if a child says, "I had a bad day at school," the generation AI determines whether the child is sad or angry based on the tone of the words and facial expression. The communication suggestion unit suggests an optimal communication method based on the emotion data analyzed by the emotion analysis unit. For example, if a child is sad, it suggests saying kind words such as, "What's up today? Did something happen?" The communication suggestion unit uses the generation AI to make optimal suggestions based on the child's emotional data and the parent's communication history. The history recording unit records the communication history between parent and child. For example, it records the content of conversations and emotional data between parent and child to understand past communication patterns. The history recording unit uses a generation AI to perform analysis based on the content of conversations between parents and children and emotional data. For example, it records how a child reacted to certain situations in the past and serves as a reference when similar situations arise again. This allows the communication support system according to the embodiment to maintain a good parent-child relationship with adolescent children and facilitate smooth communication. For example, parents can gain their children's trust by using appropriate words when their children talk about events at school. Furthermore, improving parents' communication skills can also lead to improved parent-child relationships in the future.

[0030] The emotion analysis unit collects physiological data such as a child's body temperature and heart rate, allowing for a more accurate understanding of the child's emotional state. For example, the emotion analysis unit monitors the child's body temperature and heart rate in real time and inputs that data into the generation AI. For example, a wearable device can be used to record fluctuations in the child's body temperature and heart rate and use this data for emotion analysis. The emotion analysis unit can also measure electrodermal activity to understand the child's emotional state. For example, it can analyze the child's stress level based on changes in electrodermal activity. The emotion analysis unit can also analyze breathing patterns to understand the child's emotional state. For example, it can evaluate the child's level of relaxation based on the rhythm and depth of breathing. In this way, by collecting physiological data, the emotional state can be more accurately understood.

[0031] The emotion analysis unit can track changes in emotions over the long term by referring to past behavioral patterns and diary data. The emotion analysis unit, for example, analyzes a child's diary data to track past changes in emotions. For example, it performs text analysis of the contents of the diary to identify emotional trends. The emotion analysis unit also analyzes a child's behavioral patterns to track changes in emotions over the long term. For example, it analyzes the impact of specific actions on emotions based on daily behavior records. The emotion analysis unit can also analyze a child's social media posts to track changes in emotions. For example, it analyzes the content of posts and the tone of comments to identify changes in emotions. In this way, by referring to past behavioral patterns and diary data, it is possible to track changes in emotions over the long term.

[0032] The emotion analysis unit also collects emotional data of other family members in the household, enabling the emotional state of the entire family to be grasped. For example, the emotion analysis unit collects emotional data of other family members in the household and analyzes the emotional state of the entire family. For example, it monitors the facial expressions and tone of voice of all family members. The emotion analysis unit also integrates the emotional data of all family members and evaluates the emotional balance of the entire family. For example, it calculates an emotional score for all family members to grasp the emotional balance. The emotion analysis unit also analyzes the emotional dynamics within the household based on the emotional data of all family members. For example, it analyzes the impact that the emotions of a particular family member have on other family members. In this way, the emotional state of the entire family can be grasped, thereby maintaining emotional balance within the household.

[0033] The emotion analysis unit also collects data on school and friendships, and can perform emotion analysis that takes social factors into account. The emotion analysis unit collects, for example, data on events at school and friendships and uses it for emotion analysis. For example, it analyzes school grades and interactions with friends. The emotion analysis unit also performs emotion analysis that takes social factors into account based on the school and friendship data. For example, it analyzes the impact of stress at school and troubles with friends on emotions. The emotion analysis unit can also predict changes in emotions based on the school and friendship data. For example, it predicts the impact of specific events or occurrences on emotions. This improves the accuracy of emotion analysis by taking social factors into account.

[0034] The communication suggestion unit can analyze the parent's past communication history, learn successful communication patterns, and reflect them in its suggestions. The communication suggestion unit, for example, analyzes the parent's past communication history and extracts successful patterns. For example, if specific words or attitudes have had a positive impact on a child, it learns that pattern. The communication suggestion unit also makes optimal suggestions based on successful communication patterns. For example, it may suggest reusing words or attitudes that have been successful in the past. The communication suggestion unit also makes suggestions to avoid unsuccessful patterns based on the parent's communication history. For example, it may suggest avoiding words or attitudes that have rebelled against a child in the past. In this way, by learning successful communication patterns from the past and reflecting them in its suggestions, effective communication is possible.

[0035] The communication suggestion unit can consider a child's hobbies and interests and suggest a communication method based on them. For example, the communication suggestion unit analyzes a child's hobbies and interests and suggests a communication method based on them. For example, it suggests conversations that incorporate topics that the child likes. The communication suggestion unit also makes suggestions to find common topics based on the child's interests. For example, if a parent and child have a common hobby, it suggests topics related to that hobby. The communication suggestion unit can also suggest new activities for parents based on the child's hobbies and interests. For example, it suggests events or activities related to areas that the child is interested in. This enables more effective communication by taking the child's hobbies and interests into consideration.

[0036] The communication suggestion unit can analyze the parent's personality and communication style and make suggestions that are optimized accordingly. The communication suggestion unit, for example, analyzes the parent's personality and communication style and suggests a communication method that is optimized accordingly. For example, if the parent is introverted, it suggests a gentle way of speaking. The communication suggestion unit also suggests a communication method that suits the parent's personality. For example, if the parent is extroverted, it suggests active dialogue. The communication suggestion unit also suggests the optimal communication method based on the parent's communication style. For example, if the parent has an empathetic style, it suggests words that show empathy. In this way, by making suggestions that are optimized according to the parent's personality and communication style, effective communication is possible.

[0037] The communication suggestion unit can learn the communication styles of different cultures and regions and propose multicultural communication methods. For example, the communication suggestion unit learns the communication styles of different cultures and regions and proposes communication methods based on that. For example, it provides advice on points to note when communicating between different cultures. The communication suggestion unit also makes optimal suggestions based on the communication styles of specific cultures and regions. For example, it suggests etiquette and manners that are important in specific cultures. The communication suggestion unit also collects and analyzes data from different cultures and regions in order to propose multicultural communication methods. For example, it learns successful examples of intercultural communication. In this way, by proposing multicultural communication methods, effective communication is possible across different cultures and regions.

[0038] The history recording unit can analyze the communication history between parents and children to discover long-term trends and patterns. The history recording unit, for example, analyzes the communication history between parents and children to build a system that discovers long-term trends and patterns. For example, it analyzes patterns of repeated use of specific words or attitudes. The history recording unit also grasps changes in the parent-child relationship based on long-term trends. For example, it analyzes changes in emotions at specific times. The history recording unit also finds areas for improvement based on the communication history between parents and children. For example, it identifies unsuccessful communication patterns in the past and proposes improvement measures. In this way, by discovering long-term trends and patterns, it is possible to understand changes in the parent-child relationship and find areas for improvement.

[0039] The history recording unit can propose specific action plans that are useful for strengthening the parent-child relationship based on the communication history. The history recording unit, for example, analyzes the communication history and builds a system that proposes specific action plans that are useful for strengthening the parent-child relationship. For example, it suggests the timing to use specific words or attitudes. The history recording unit also suggests setting goals for strengthening the parent-child relationship. For example, it suggests setting aside time for regular communication. The history recording unit also suggests specific action steps for strengthening the parent-child relationship. For example, it suggests deepening the relationship through common hobbies or activities. In this way, it is possible to strengthen the parent-child relationship by proposing specific action plans.

[0040] The history recording unit can compare the communication history between parents and children with the communication history with other family members and friends to analyze differences in interactions. The history recording unit, for example, builds a system that compares the communication history between parents and children with the communication history with other family members and friends. For example, it analyzes communication patterns in different relationships. The history recording unit also compares the communication history between parents and children with the history with other family members and friends to understand differences in interactions. For example, it analyzes differences between communication between parents and children and communication between friends. The history recording unit also finds areas for improvement in the parent-child relationship based on differences in interactions. For example, it suggests applying communication patterns that have been successful in other relationships to the parent-child relationship. In this way, by comparing the communication history between parents and children with the history with other family members and friends, it is possible to understand differences in interactions and find areas for improvement.

[0041] The history recording unit can suggest workshops and seminars that are useful for improving parent-child relationships based on the communication history. The history recording unit, for example, analyzes the communication history and builds a system that suggests workshops and seminars that are useful for improving parent-child relationships. For example, it suggests workshops based on a specific theme. The history recording unit also suggests seminars aimed at improving parent-child relationships. For example, it suggests seminars to improve communication skills. The history recording unit also suggests specific activities aimed at improving parent-child relationships. For example, it suggests events and activities that parents and children can participate in. In this way, it is possible to improve parent-child relationships by suggesting workshops and seminars.

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

[0043] The communication suggestion unit can analyze the parent's past communication history, learn successful communication patterns, and reflect them in its suggestions. For example, it can analyze the parent's past communication history and extract successful patterns. For example, if a particular word or attitude has had a positive impact on a child, it can learn that pattern. The communication suggestion unit also makes optimal suggestions based on successful communication patterns. For example, it can suggest reusing words or attitudes that have been successful in the past. The communication suggestion unit also makes suggestions to avoid unsuccessful patterns based on the parent's communication history. For example, it can suggest avoiding words or attitudes that have rebelled against a child in the past. In this way, by learning successful communication patterns from the past and reflecting them in its suggestions, effective communication becomes possible.

[0044] The communication suggestion unit can consider a child's hobbies and interests and suggest a communication method based on them. For example, it can analyze a child's hobbies and interests and suggest a communication method based on them. For example, it can suggest conversations that incorporate topics that the child likes. The communication suggestion unit also makes suggestions to find common topics based on the child's interests. For example, if a parent and child have a common hobby, it can suggest topics related to that hobby. The communication suggestion unit can also suggest new activities for parents based on the child's hobbies and interests. For example, it can suggest events or activities related to areas that the child is interested in. This enables more effective communication by taking a child's hobbies and interests into consideration.

[0045] The communication suggestion unit can analyze the parent's personality and communication style and make suggestions that are optimized accordingly. For example, it can analyze the parent's personality and communication style and suggest a communication method that is optimized accordingly. For example, if the parent is introverted, it can suggest a gentle way of speaking. The communication suggestion unit also suggests a communication method that suits the parent's personality. For example, if the parent is extroverted, it can suggest active dialogue. The communication suggestion unit also suggests the optimal communication method based on the parent's communication style. For example, if the parent has an empathetic style, it can suggest words that show empathy. In this way, suggestions that are optimized for the parent's personality and communication style can be made, enabling effective communication.

[0046] The history recording unit can compare the communication history between parents and children with the communication history with other family members and friends to analyze differences in interactions. For example, a system can be constructed that compares the communication history between parents and children with the communication history with other family members and friends. For example, the communication patterns in different relationships can be analyzed. The history recording unit also compares the communication history between parents and children with the history with other family members and friends to understand differences in interactions. For example, it analyzes the differences between communication between parents and children and communication between friends. The history recording unit also finds areas for improvement in the parent-child relationship based on differences in interactions. For example, it suggests applying communication patterns that have been successful in other relationships to the parent-child relationship. In this way, by comparing the communication history between parents and children with the history with other family members and friends, differences in interactions can be understood and areas for improvement can be found.

[0047] The history recording unit can suggest workshops and seminars that are useful for improving parent-child relationships based on the communication history. For example, a system can be constructed that analyzes the communication history and suggests workshops and seminars that are useful for improving parent-child relationships. For example, a workshop based on a specific theme can be suggested. The history recording unit can also suggest seminars aimed at improving parent-child relationships. For example, a seminar for improving communication skills can be suggested. The history recording unit can also suggest specific activities aimed at improving parent-child relationships. For example, an event or activity that parents and children can participate in can be suggested. In this way, it is possible to improve parent-child relationships by suggesting workshops and seminars.

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

[0049] Step 1: The emotion analysis unit analyzes the child's emotions. For example, it analyzes the child's facial expressions, tone of voice, and choice of words to understand their current emotional state. The emotion analysis unit uses generative AI (such as text generation AI or multimodal generation AI) to analyze emotions based on the child's facial expressions, voice data, and the content of the conversation. For example, if a child says, "Something bad happened at school today," the generative AI will determine whether the child is sad or angry based on the tone of their words and facial expression. Step 2: The communication suggestion unit proposes the optimal communication method based on the emotional data analyzed by the emotion analysis unit. For example, if a child is sad, it suggests saying kind words such as, "How are you today? Did something happen?" The communication suggestion unit uses a generative AI to make optimal suggestions based on the child's emotional data and the parent's communication history. Step 3: The history recording unit records the communication history between parent and child. For example, it records the content of conversations between parent and child and emotional data to understand past communication patterns. The history recording unit uses the generation AI to analyze the content of conversations between parent and child and emotional data. For example, it records how the child reacted in certain situations in the past, and can use this information as a reference if a similar situation occurs again.

[0050] (Example 2) The communication support system according to the embodiment of the present invention is a system that analyzes a child's emotions, proposes an optimal communication method, and records the communication history between parent and child. As a result, the communication support system can maintain a good parent-child relationship with adolescent children and facilitate smooth communication.

[0051] A communication support system according to an embodiment includes an emotion analysis unit, a communication suggestion unit, and a history recording unit. The emotion analysis unit analyzes a child's emotions. For example, it analyzes the child's facial expressions, tone of voice, and word choice to understand the child's current emotional state. The emotion analysis unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze emotions based on the child's facial expressions, voice data, and conversation content. For example, if a child says, "I had a bad day at school," the generation AI determines whether the child is sad or angry based on the tone of the words and facial expression. The communication suggestion unit suggests an optimal communication method based on the emotion data analyzed by the emotion analysis unit. For example, if a child is sad, it suggests saying kind words such as, "What's up today? Did something happen?" The communication suggestion unit uses the generation AI to make optimal suggestions based on the child's emotional data and the parent's communication history. The history recording unit records the communication history between parent and child. For example, it records the content of conversations and emotional data between parent and child to understand past communication patterns. The history recording unit uses a generation AI to perform analysis based on the content of conversations between parents and children and emotional data. For example, it records how a child reacted to certain situations in the past and serves as a reference when similar situations arise again. This allows the communication support system according to the embodiment to maintain a good parent-child relationship with adolescent children and facilitate smooth communication. For example, parents can gain their children's trust by using appropriate words when their children talk about events at school. Furthermore, improving parents' communication skills can also lead to improved parent-child relationships in the future.

[0052] The emotion analysis unit collects physiological data such as a child's body temperature and heart rate, allowing for a more accurate understanding of the child's emotional state. For example, the emotion analysis unit monitors the child's body temperature and heart rate in real time and inputs that data into the generation AI. For example, a wearable device can be used to record fluctuations in the child's body temperature and heart rate and use this data for emotion analysis. The emotion analysis unit can also measure electrodermal activity to understand the child's emotional state. For example, it can analyze the child's stress level based on changes in electrodermal activity. The emotion analysis unit can also analyze breathing patterns to understand the child's emotional state. For example, it can evaluate the child's level of relaxation based on the rhythm and depth of breathing. In this way, by collecting physiological data, the emotional state can be more accurately understood.

[0053] The emotion analysis unit can track changes in emotions over the long term by referring to past behavioral patterns and diary data. The emotion analysis unit, for example, analyzes a child's diary data to track past changes in emotions. For example, it performs text analysis of the contents of the diary to identify emotional trends. The emotion analysis unit also analyzes a child's behavioral patterns to track changes in emotions over the long term. For example, it analyzes the impact of specific actions on emotions based on daily behavior records. The emotion analysis unit can also analyze a child's social media posts to track changes in emotions. For example, it analyzes the content of posts and the tone of comments to identify changes in emotions. In this way, by referring to past behavioral patterns and diary data, it is possible to track changes in emotions over the long term.

[0054] The emotion analysis unit uses an emotion estimation function to analyze a child's emotions in real time and provide instant feedback to parents. The emotion analysis unit, for example, analyzes a child's facial expressions and tone of voice in real time to instantly grasp their emotional state. For example, it uses a camera or microphone to detect changes in the child's facial expressions and tone of voice. The emotion analysis unit also analyzes emotions using an emotion estimation algorithm based on the data collected in real time. For example, it calculates an emotion score based on changes in facial expressions and tone of voice. The emotion analysis unit also immediately provides feedback to parents on the emotional data analyzed in real time. For example, it notifies parents of their emotional state via a smartphone app. This makes it possible to analyze emotions in real time and provide instant feedback.

[0055] The emotion analysis unit also collects emotional data of other family members in the household, enabling the emotional state of the entire family to be grasped. For example, the emotion analysis unit collects emotional data of other family members in the household and analyzes the emotional state of the entire family. For example, it monitors the facial expressions and tone of voice of all family members. The emotion analysis unit also integrates the emotional data of all family members and evaluates the emotional balance of the entire family. For example, it calculates an emotional score for all family members to grasp the emotional balance. The emotion analysis unit also analyzes the emotional dynamics within the household based on the emotional data of all family members. For example, it analyzes the impact that the emotions of a particular family member have on other family members. In this way, the emotional state of the entire family can be grasped, thereby maintaining emotional balance within the household.

[0056] The emotion analysis unit also collects data on school and friendships, and can perform emotion analysis that takes social factors into account. The emotion analysis unit collects, for example, data on events at school and friendships and uses it for emotion analysis. For example, it analyzes school grades and interactions with friends. The emotion analysis unit also performs emotion analysis that takes social factors into account based on the school and friendship data. For example, it analyzes the impact of stress at school and troubles with friends on emotions. The emotion analysis unit can also predict changes in emotions based on the school and friendship data. For example, it predicts the impact of specific events or occurrences on emotions. This improves the accuracy of emotion analysis by taking social factors into account.

[0057] The emotion analysis unit uses the emotion estimation function to share the results of a child's emotion analysis not only with parents but also with school counselors and medical professionals, allowing for comprehensive support. The emotion analysis unit, for example, builds a system to share the results of emotion analysis with school counselors and medical professionals. For example, emotional data can be stored in the cloud so that experts can access it. The emotion analysis unit also provides support in cooperation with school counselors and medical professionals based on the emotion analysis results. For example, it can suggest regular counseling or medical support. The emotion analysis unit also creates a support plan for the child in cooperation with experts based on the emotion analysis results. For example, emotional data can be shared to consider the optimal support method. In this way, comprehensive support can be provided by sharing the emotion analysis results.

[0058] The communication suggestion unit can analyze the parent's past communication history, learn successful communication patterns, and reflect them in its suggestions. The communication suggestion unit, for example, analyzes the parent's past communication history and extracts successful patterns. For example, if specific words or attitudes have had a positive impact on a child, it learns that pattern. The communication suggestion unit also makes optimal suggestions based on successful communication patterns. For example, it may suggest reusing words or attitudes that have been successful in the past. The communication suggestion unit also makes suggestions to avoid unsuccessful patterns based on the parent's communication history. For example, it may suggest avoiding words or attitudes that have rebelled against a child in the past. In this way, by learning successful communication patterns from the past and reflecting them in its suggestions, effective communication is possible.

[0059] The communication suggestion unit can consider a child's hobbies and interests and suggest a communication method based on them. For example, the communication suggestion unit analyzes a child's hobbies and interests and suggests a communication method based on them. For example, it suggests conversations that incorporate topics that the child likes. The communication suggestion unit also makes suggestions to find common topics based on the child's interests. For example, if a parent and child have a common hobby, it suggests topics related to that hobby. The communication suggestion unit can also suggest new activities for parents based on the child's hobbies and interests. For example, it suggests events or activities related to areas that the child is interested in. This enables more effective communication by taking the child's hobbies and interests into consideration.

[0060] The communication suggestion unit can analyze the parent's personality and communication style and make suggestions that are optimized accordingly. The communication suggestion unit, for example, analyzes the parent's personality and communication style and suggests a communication method that is optimized accordingly. For example, if the parent is introverted, it suggests a gentle way of speaking. The communication suggestion unit also suggests a communication method that suits the parent's personality. For example, if the parent is extroverted, it suggests active dialogue. The communication suggestion unit also suggests the optimal communication method based on the parent's communication style. For example, if the parent has an empathetic style, it suggests words that show empathy. In this way, by making suggestions that are optimized according to the parent's personality and communication style, effective communication is possible.

[0061] The communication suggestion unit can learn the communication styles of different cultures and regions and propose multicultural communication methods. For example, the communication suggestion unit learns the communication styles of different cultures and regions and proposes communication methods based on that. For example, it provides advice on points to note when communicating between different cultures. The communication suggestion unit also makes optimal suggestions based on the communication styles of specific cultures and regions. For example, it suggests etiquette and manners that are important in specific cultures. The communication suggestion unit also collects and analyzes data from different cultures and regions in order to propose multicultural communication methods. For example, it learns successful examples of intercultural communication. In this way, by proposing multicultural communication methods, effective communication is possible across different cultures and regions.

[0062] The communication suggestion unit uses the emotion estimation function to monitor in real time the child's emotional reaction when the parent implements the proposed communication method, and can modify the suggestion as necessary. The communication suggestion unit, for example, builds a system that monitors in real time the child's emotional reaction when the parent implements the proposed communication method. For example, it analyzes the child's facial expressions and tone of voice. The communication suggestion unit also modifies the suggestion based on the emotional data collected in real time. For example, if the child has a negative reaction to the proposed method, it proposes an alternative method. The communication suggestion unit also uses the emotion estimation function to evaluate the effectiveness of the communication method implemented by the parent. For example, it evaluates the effectiveness of the suggestion based on the child's emotion score. This enables effective communication by evaluating the effectiveness of the communication method implemented by the parent in real time and modifying the suggestion as necessary.

[0063] The history recording unit can analyze the communication history between parents and children to discover long-term trends and patterns. The history recording unit, for example, analyzes the communication history between parents and children to build a system that discovers long-term trends and patterns. For example, it analyzes patterns of repeated use of specific words or attitudes. The history recording unit also grasps changes in the parent-child relationship based on long-term trends. For example, it analyzes changes in emotions at specific times. The history recording unit also finds areas for improvement based on the communication history between parents and children. For example, it identifies unsuccessful communication patterns in the past and proposes improvement measures. In this way, by discovering long-term trends and patterns, it is possible to understand changes in the parent-child relationship and find areas for improvement.

[0064] The history recording unit can propose specific action plans that are useful for strengthening the parent-child relationship based on the communication history. The history recording unit, for example, analyzes the communication history and builds a system that proposes specific action plans that are useful for strengthening the parent-child relationship. For example, it suggests the timing to use specific words or attitudes. The history recording unit also suggests setting goals for strengthening the parent-child relationship. For example, it suggests setting aside time for regular communication. The history recording unit also suggests specific action steps for strengthening the parent-child relationship. For example, it suggests deepening the relationship through common hobbies or activities. In this way, it is possible to strengthen the parent-child relationship by proposing specific action plans.

[0065] The history recording unit can use the emotion estimation function to identify scenes in the communication history that caused a particularly strong emotional response and analyze the causes. The history recording unit, for example, builds a system that identifies scenes in the communication history that caused a particularly strong emotional response. For example, it extracts scenes with a high emotion score. The history recording unit also analyzes the causes of the identified scenes. For example, it analyzes the causes of specific words or behaviors that caused the emotional response. The history recording unit also uses the emotion estimation function to evaluate the strength of the emotional response. For example, it quantifies the strength of the emotional response based on the emotion score. This makes it possible to identify scenes that caused a particularly strong emotional response and analyze the causes, thereby identifying areas for improvement.

[0066] The history recording unit can compare the communication history between parents and children with the communication history with other family members and friends to analyze differences in interactions. The history recording unit, for example, builds a system that compares the communication history between parents and children with the communication history with other family members and friends. For example, it analyzes communication patterns in different relationships. The history recording unit also compares the communication history between parents and children with the history with other family members and friends to understand differences in interactions. For example, it analyzes differences between communication between parents and children and communication between friends. The history recording unit also finds areas for improvement in the parent-child relationship based on differences in interactions. For example, it suggests applying communication patterns that have been successful in other relationships to the parent-child relationship. In this way, by comparing the communication history between parents and children with the history with other family members and friends, it is possible to understand differences in interactions and find areas for improvement.

[0067] The history recording unit can suggest workshops and seminars that are useful for improving parent-child relationships based on the communication history. The history recording unit, for example, analyzes the communication history and builds a system that suggests workshops and seminars that are useful for improving parent-child relationships. For example, it suggests workshops based on a specific theme. The history recording unit also suggests seminars aimed at improving parent-child relationships. For example, it suggests seminars to improve communication skills. The history recording unit also suggests specific activities aimed at improving parent-child relationships. For example, it suggests events and activities that parents and children can participate in. In this way, it is possible to improve parent-child relationships by suggesting workshops and seminars.

[0068] The history recording unit uses the emotion estimation function to share the analysis results of the communication history not only with parents but also with school counselors and medical professionals, thereby enabling comprehensive support to be provided. For example, the history recording unit builds a system for sharing the analysis results of the communication history with school counselors and medical professionals. For example, emotion data is stored in the cloud so that experts can access it. The history recording unit also provides support in cooperation with school counselors and medical professionals based on the analysis results. For example, it suggests regular counseling and medical support. The history recording unit also creates a support plan for the child in cooperation with experts based on the analysis results. For example, emotion data is shared and the optimal support method is considered. In this way, comprehensive support can be provided by sharing the analysis results of the communication history.

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

[0070] The communication suggestion unit can analyze the parent's past communication history, learn successful communication patterns, and reflect them in its suggestions. For example, it can analyze the parent's past communication history and extract successful patterns. For example, if a particular word or attitude has had a positive impact on a child, it can learn that pattern. The communication suggestion unit also makes optimal suggestions based on successful communication patterns. For example, it can suggest reusing words or attitudes that have been successful in the past. The communication suggestion unit also makes suggestions to avoid unsuccessful patterns based on the parent's communication history. For example, it can suggest avoiding words or attitudes that have rebelled against a child in the past. In this way, by learning successful communication patterns from the past and reflecting them in its suggestions, effective communication becomes possible.

[0071] The emotion analysis unit also collects emotional data of other family members in the home, enabling the emotional state of the entire family to be grasped. For example, the emotion analysis unit collects emotional data of other family members in the home and analyzes the emotional state of the entire family. For example, it monitors the facial expressions and tone of voice of all family members. The emotion analysis unit also integrates the emotional data of all family members and evaluates the emotional balance of the entire family. For example, it calculates an emotional score for all family members to grasp the emotional balance. The emotion analysis unit also analyzes the emotional dynamics within the home based on the emotional data of all family members. For example, it analyzes the impact that the emotions of a particular family member have on other family members. In this way, the emotional state of the entire family can be grasped, thereby maintaining emotional balance within the home.

[0072] The communication suggestion unit can consider a child's hobbies and interests and suggest a communication method based on them. For example, it can analyze a child's hobbies and interests and suggest a communication method based on them. For example, it can suggest conversations that incorporate topics that the child likes. The communication suggestion unit also makes suggestions to find common topics based on the child's interests. For example, if a parent and child have a common hobby, it can suggest topics related to that hobby. The communication suggestion unit can also suggest new activities for parents based on the child's hobbies and interests. For example, it can suggest events or activities related to areas that the child is interested in. This enables more effective communication by taking a child's hobbies and interests into consideration.

[0073] The emotion analysis unit can also collect data on school and friendships, and perform emotion analysis that takes social factors into account. For example, data on events at school and friendships can be collected and used for emotion analysis. For example, data on school grades and interactions with friends can be analyzed. The emotion analysis unit also performs emotion analysis that takes social factors into account based on the data on school and friendships. For example, it can analyze the impact of stress at school or troubles with friends on emotions. The emotion analysis unit can also predict changes in emotions based on the data on school and friendships. For example, it can predict the impact of specific events or occurrences on emotions. This improves the accuracy of emotion analysis by taking social factors into account.

[0074] The communication suggestion unit can analyze the parent's personality and communication style and make suggestions that are optimized accordingly. For example, it can analyze the parent's personality and communication style and suggest a communication method that is optimized accordingly. For example, if the parent is introverted, it can suggest a gentle way of speaking. The communication suggestion unit also suggests a communication method that suits the parent's personality. For example, if the parent is extroverted, it can suggest active dialogue. The communication suggestion unit also suggests the optimal communication method based on the parent's communication style. For example, if the parent has an empathetic style, it can suggest words that show empathy. In this way, suggestions that are optimized for the parent's personality and communication style can be made, enabling effective communication.

[0075] The emotion analysis unit uses its emotion estimation function to share the results of a child's emotion analysis not only with parents but also with school counselors and medical professionals, allowing for comprehensive support. For example, a system can be built to share the results of emotion analysis with school counselors and medical professionals. For example, emotion data can be stored in the cloud so that experts can access it. The emotion analysis unit can also provide support in cooperation with school counselors and medical professionals based on the emotion analysis results. For example, it can suggest regular counseling or medical support. The emotion analysis unit can also create a support plan for the child in collaboration with experts based on the emotion analysis results. For example, emotional data can be shared to consider the optimal support method. In this way, comprehensive support can be provided by sharing the emotion analysis results.

[0076] The history recording unit can compare the communication history between parents and children with the communication history with other family members and friends to analyze differences in interactions. For example, a system can be constructed that compares the communication history between parents and children with the communication history with other family members and friends. For example, the communication patterns in different relationships can be analyzed. The history recording unit also compares the communication history between parents and children with the history with other family members and friends to understand differences in interactions. For example, it analyzes the differences between communication between parents and children and communication between friends. The history recording unit also finds areas for improvement in the parent-child relationship based on differences in interactions. For example, it suggests applying communication patterns that have been successful in other relationships to the parent-child relationship. In this way, by comparing the communication history between parents and children with the history with other family members and friends, differences in interactions can be understood and areas for improvement can be found.

[0077] The history recording unit can use the emotion estimation function to identify scenes in the communication history that caused a particularly strong emotional response and analyze the causes. For example, a system can be built to identify scenes in the communication history that caused a particularly strong emotional response. For example, scenes with a high emotion score can be extracted. The history recording unit then analyzes the causes of the identified scenes. For example, it can analyze why specific words or behaviors caused the emotional response. The history recording unit also uses the emotion estimation function to evaluate the strength of the emotional response. For example, it can quantify the strength of the emotional response based on the emotion score. This makes it possible to identify scenes that caused a particularly strong emotional response and analyze the causes, thereby identifying areas for improvement.

[0078] The history recording unit can suggest workshops and seminars that are useful for improving parent-child relationships based on the communication history. For example, a system can be constructed that analyzes the communication history and suggests workshops and seminars that are useful for improving parent-child relationships. For example, a workshop based on a specific theme can be suggested. The history recording unit can also suggest seminars aimed at improving parent-child relationships. For example, a seminar for improving communication skills can be suggested. The history recording unit can also suggest specific activities aimed at improving parent-child relationships. For example, an event or activity that parents and children can participate in can be suggested. In this way, it is possible to improve parent-child relationships by suggesting workshops and seminars.

[0079] The emotion analysis unit can track changes in emotions over the long term by referring to past behavioral patterns and diary data. For example, it can analyze a child's diary data to track past changes in emotions. For example, it can analyze the text of the diary entries to understand emotional trends. The emotion analysis unit can also analyze a child's behavioral patterns to track changes in emotions over the long term. For example, it can analyze the impact of specific actions on emotions based on daily behavior records. The emotion analysis unit can also analyze a child's social media posts to track changes in emotions. For example, it can analyze the content of posts and the tone of comments to understand changes in emotions. In this way, it is possible to track changes in emotions over the long term by referring to past behavioral patterns and diary data.

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

[0081] Step 1: The emotion analysis unit analyzes the child's emotions. For example, it analyzes the child's facial expressions, tone of voice, and choice of words to understand their current emotional state. The emotion analysis unit uses generative AI (such as text generation AI or multimodal generation AI) to analyze emotions based on the child's facial expressions, voice data, and the content of the conversation. For example, if a child says, "Something bad happened at school today," the generative AI will determine whether the child is sad or angry based on the tone of their words and facial expression. Step 2: The communication suggestion unit proposes the optimal communication method based on the emotional data analyzed by the emotion analysis unit. For example, if a child is sad, it suggests saying kind words such as, "How are you today? Did something happen?" The communication suggestion unit uses a generative AI to make optimal suggestions based on the child's emotional data and the parent's communication history. Step 3: The history recording unit records the communication history between parent and child. For example, it records the content of conversations between parent and child and emotional data to understand past communication patterns. The history recording unit uses the generation AI to analyze the content of conversations between parent and child and emotional data. For example, it records how the child reacted in certain situations in the past, and can use this information as a reference if a similar situation occurs again.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an emotion analysis unit that analyzes the child's emotions; a communication suggestion unit that suggests an optimal communication method based on the emotion data analyzed by the emotion analysis unit; A history recording unit that records the communication history between the parent and child. A system characterized by:

2. The emotion analysis unit Analyzes children's emotions in real time and provides immediate feedback to parents 2. The system of claim 1.

3. The emotion analysis unit Data on school and friendships is also collected, and sentiment analysis is performed taking social factors into account.

2. The system of claim 1.

4. The communication suggestion unit Analyzes parents' past communication history, learns successful communication patterns, and reflects them in suggestions 2. The system of claim 1.

5. The history recording unit Identify moments in communication history that elicited particularly strong emotional responses and analyze the causes 2. The system of claim 1.

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