system

Abstract

An object of the system according to the embodiment is to enable a parent to grasp the interest and psychological state of a child while protecting the privacy of the child.SOLUTION: A system includes an analysis unit and a provision unit. In the analysis unit, the generation AI installed in the child's smartphone analyzes the SNS and the message application to estimate the child's interest and mental state. The providing unit provides the result estimated by the analyzing unit to the parent.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 is difficult to grasp children's smartphone usage and psychological state, which could lead to a decrease in communication between parents and children.

[0005] The system according to the embodiment aims to enable parents to understand their children's interests and psychological state while protecting the children's privacy. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit and a provision unit. The analysis unit uses a generation AI installed on a child's smartphone to analyze data from social media and messaging apps to estimate the child's interests and psychological state. The provision unit provides the results of the analysis unit's estimation to the parent. [Effects of the Invention]

[0007] The system according to the embodiment allows parents to understand their children's interests and psychological state while protecting the children's privacy. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generating AI to help parents understand their children's interests and psychological state while protecting their privacy. This system installs a generating AI on a child's smartphone, analyzes data from social media and messaging apps, estimates the child's interests and psychological state, and provides the results to the parent. For example, if a child frequently posts about a particular game or anime, the generating AI can estimate the child's interests based on that information. The generating AI can also estimate the child's psychological state by analyzing the content of the child's posts and the tone of their messages. This allows parents to understand their child's interests and psychological state without violating their child's privacy. The system allows parents to understand their child's situation based on the information provided by the generating AI, without the need to forcibly take away their child's smartphone or directly check the content of social media posts. For example, parents can deepen their understanding of the games their child is interested in, or, if their child is feeling stressed, provide advice on the causes and solutions. This facilitates communication between parents and children, supporting their healthy development.

[0029] A system for understanding a child's interests and psychological state according to an embodiment includes an analysis unit and a provision unit. The analysis unit uses a generation AI installed on a child's smartphone to analyze data from social media and messaging apps to estimate the child's interests and psychological state. For example, if a child frequently posts about a particular game or anime, the analysis unit estimates the child's interests based on that information. The analysis unit can also estimate the child's psychological state by analyzing the content of the child's posts and the tone of their messages. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze text and perform sentiment analysis using natural language processing technology. The provision unit provides the results estimated by the analysis unit to parents. For example, the provision unit visually displays the results in a dashboard format to allow parents to intuitively understand. If a child is interested in a particular game, the provision unit can provide the name of the game and related information. Furthermore, if a child is feeling stressed, the provision unit can provide advice on the cause and countermeasures. As a result, the system for understanding a child's interests and psychological state according to an embodiment allows parents to understand their child's interests and psychological state while protecting their child's privacy.

[0030] The analysis unit can analyze text and perform sentiment analysis using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can divide text into words using morphological analysis and identify the part of speech of each word. The analysis unit can also analyze sentence structure using grammatical analysis to clarify relationships between subjects, predicates, objects, and the like. The analysis unit can also understand the meaning of text and perform sentiment analysis using semantic analysis. For example, the analysis unit can identify words and phrases in the text that indicate positive emotions and infer positive emotions. The analysis unit can also identify words and phrases in the text that indicate negative emotions and infer negative emotions. This makes it possible to perform sentiment analysis of text using natural language processing technology.

[0031] The providing unit may visually display the information in a dashboard format, allowing parents to intuitively understand the information. Dashboard formats include, but are not limited to, graphs, charts, tables, and the like. For example, the providing unit may display data indicating a child's interests and psychological state as a graph. The providing unit may also display fluctuations in the child's emotions as a chart. The providing unit may also display detailed information regarding the child's interests and psychological state as a table. For example, the providing unit may display a list of topics and activities in which the child is interested, allowing parents to intuitively understand the information. This allows parents to intuitively understand their child's interests and psychological state.

[0032] If a child is interested in a particular game, the providing unit can provide the name of the game and related information. For example, the providing unit can provide the name of the game in which the child is interested to the parent. For example, the providing unit can list the names of games that the child frequently posts and provide the list to the parent. The providing unit can also provide news and articles related to the game in which the child is interested. For example, the providing unit can provide the parent with the latest information and update information on the game. The providing unit can also provide gameplay videos and reviews of the game in which the child is interested. For example, the providing unit can provide the parent with gameplay videos and reviews of the game, allowing the parent to deepen their understanding of the game. This allows the parent to deepen their understanding of the game in which the child is interested.

[0033] If a child is feeling stressed, the provision unit can provide advice on the causes and countermeasures. For example, the provision unit can analyze the causes of a child's stress and advise parents on countermeasures. For example, if a child is feeling stressed due to problems at school or interpersonal issues, the provision unit can identify the causes and advise parents on countermeasures. The provision unit can also have the generating AI suggest specific methods for reducing stress. For example, the provision unit can suggest stress reduction methods to parents, such as relaxation techniques, counseling, and exercise. The provision unit can also provide information related to children's stress and support parents in taking appropriate countermeasures. For example, the provision unit can provide parents with articles on stress and expert advice. This allows parents to understand the causes of their children's stress and take appropriate countermeasures.

[0034] The analysis unit can analyze a child's past posting history and select the optimal analysis method. For example, the analysis unit uses a generation AI to select the optimal analysis method based on words and phrases frequently used by the child in the past. For example, the analysis unit can extract words and phrases frequently used by the child in the past and select the optimal analysis method based on the extracted words and phrases. The analysis unit can also select an analysis method related to a specific theme or topic from a child's past posting history. For example, the analysis unit can analyze the themes and topics that the child has posted on in the past and select the optimal analysis method based on the analyzed words and phrases. The analysis unit can also analyze a child's past posting history and select an analysis method based on emotional fluctuation patterns. For example, the analysis unit can analyze a child's past posting history, identify emotional fluctuation patterns, and select the optimal analysis method based on the identified emotional fluctuation patterns. This improves the accuracy of the analysis by selecting the optimal analysis method based on past posting history.

[0035] During analysis, the analysis unit can perform filtering based on the child's current living situation and areas of interest. For example, the analysis unit analyzes only posts related to topics in which the child is currently interested. For example, the analysis unit can filter posts related to topics in which the child is currently interested and analyze them. The analysis unit can also prioritize analyzing posts related to the child's current living situation (school, home, etc.). For example, the analysis unit can filter posts related to the child's current living situation and analyze them preferentially. The analysis unit can also filter and analyze posts containing specific keywords based on the child's areas of interest. For example, the analysis unit can filter posts containing keywords related to the child's areas of interest and analyze them. In this way, by filtering based on the child's current living situation and areas of interest, more relevant data can be analyzed.

[0036] During analysis, the analysis unit can select the optimal analysis method depending on the child's input method. For example, when a child uses voice input, the analysis unit causes the generation AI to analyze the data using voice analysis technology. For example, when a child uses voice input, the analysis unit can convert the voice data into text data using voice recognition technology and analyze the text data. Furthermore, when a child uses text input, the analysis unit can cause the generation AI to analyze the text using natural language processing technology. For example, when a child uses text input, the analysis unit can perform sentiment analysis of the text using natural language processing technology. Furthermore, when a child posts images, the analysis unit can cause the generation AI to analyze the data using image analysis technology. For example, when a child posts an image, the analysis unit can analyze the image data using image recognition technology to understand the content. This improves the accuracy of the analysis by selecting the optimal analysis method depending on the input method.

[0037] During analysis, the analysis unit can prioritize analyzing highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific location, the analysis unit can prioritize analyzing data related to that location. For example, if the child is in a specific location, the analysis unit can filter and prioritize analyzing data related to that location. The analysis unit can also analyze data related to nearby events and activities based on the child's geographical location information. For example, the analysis unit can filter and analyze data related to nearby events and activities based on the child's geographical location information. The analysis unit can also prioritize analyzing data related to region-specific topics by taking into account the child's geographical location information. For example, the analysis unit can filter and prioritize analyzing data related to region-specific topics based on the child's geographical location information. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0038] During the analysis, the analysis unit can analyze the child's social media activities and analyze the related data. For example, the analysis unit can prioritize the analysis of content that the child frequently posts on social media. For example, the analysis unit can filter and prioritize the analysis of content that the child frequently posts on social media. The analysis unit can also analyze the child's friendships on social media and analyze the related data. For example, the analysis unit can analyze the child's friendships on social media and analyze the related data based on the friendships. The analysis unit can also analyze the child's activity patterns on social media and select the optimal analysis method. For example, the analysis unit can analyze the child's activity patterns on social media and select the optimal analysis method based on the friendships. This allows the related data to be analyzed efficiently by analyzing social media activities.

[0039] During analysis, the analysis unit can customize the analysis method by reflecting the child's past feedback. The analysis unit customizes the analysis method, for example, based on feedback provided by the child in the past. For example, the analysis unit can analyze feedback provided by the child in the past and customize the analysis method based on the feedback. The analysis unit can also preferentially use a specific analysis method based on the child's past feedback. For example, the analysis unit can preferentially use a specific analysis method based on the child's past feedback. The analysis unit can also improve the accuracy of the analysis by reflecting the child's past feedback. For example, the analysis unit can adjust the algorithm for improving the accuracy of the analysis by reflecting the child's past feedback. In this way, the accuracy of the analysis is improved by reflecting the past feedback.

[0040] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing it. For example, the providing unit can provide highly important information in detail and less important information in brief. For example, the providing unit can provide highly important information in detail and less important information in brief. The providing unit can also have the generation AI automatically evaluate the importance of the information and adjust the level of detail of the information provided. For example, the providing unit can have the generation AI automatically evaluate the importance of the information and adjust the level of detail of the information provided based on the evaluation. The providing unit can also have the generation AI adjust the level of detail of the information based on the importance set by the parent. For example, the providing unit can adjust the level of detail of the information based on the importance set by the parent. In this way, by adjusting the level of detail based on the importance of the information, the parent can efficiently obtain the information he or she needs.

[0041] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can provide detailed information related to the child's interests and concise other information. For example, the providing unit can provide detailed information related to the child's interests and concise other information. The providing unit can also have the generation AI automatically classify the category of information and apply an appropriate providing algorithm. For example, the providing unit can have the generation AI automatically classify the category of information and apply an appropriate providing algorithm based on that. The providing unit can also adjust the way the generation AI provides information based on the category set by the parent. For example, the providing unit can adjust the way the information is provided based on the category set by the parent. This allows parents to efficiently understand the information by applying a providing algorithm according to the category of information.

[0042] When providing information, the providing unit can improve the accuracy of the information provided by referring to the parent's past usage history. For example, the providing unit allows the generation AI to improve the accuracy of the information provided based on information viewed by the parent in the past. For example, the providing unit can analyze information viewed by the parent in the past and improve the accuracy of the information provided based on that. The providing unit can also provide specific information preferentially based on the parent's past usage history. For example, the providing unit can provide specific information preferentially based on the parent's past usage history. The providing unit can also improve the accuracy of the generation AI by reflecting the parent's past feedback. For example, the providing unit can reflect the parent's past feedback and improve the accuracy of the information provided based on that. In this way, the accuracy of the information provided is improved by referring to the past usage history.

[0043] The providing unit can determine the priority of provision based on the time of submission of information when the information is provided. For example, the providing unit can provide the latest information preferentially and provide old information briefly. For example, the providing unit can provide the latest information preferentially and provide old information briefly. The providing unit can also have the generation AI automatically evaluate the time of submission of information and determine the priority of provision. For example, the providing unit can have the generation AI automatically evaluate the time of submission of information and determine the priority of provision based on that. The providing unit can also adjust the method of providing information by the generation AI based on the submission time set by the parent. For example, the providing unit can adjust the method of providing information based on the submission time set by the parent. In this way, by determining the priority based on the submission time, the latest information can be provided preferentially.

[0044] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can prioritize providing information related to the child's interests and postpone other information. For example, the providing unit can prioritize providing information related to the child's interests and postpone other information. The providing unit can also have the generation AI automatically evaluate the relevance of the information and adjust the order of provision. For example, the providing unit can have the generation AI automatically evaluate the relevance of the information and adjust the order of provision based on that. The providing unit can also have the generation AI adjust the order of provision of the information based on the relevance set by the parent. For example, the providing unit can adjust the order of provision of the information based on the relevance set by the parent. In this way, by adjusting the order of provision based on relevance, the parent can obtain important information preferentially.

[0045] When providing the information, the providing unit can adjust the use of technical terminology in the provision depending on the parent's level of expertise. For example, if the parent has technical expertise, the providing unit can provide information using detailed technical terminology. For example, if the parent has technical expertise, the providing unit can provide information using detailed technical terminology. In addition, if the parent does not have technical expertise, the providing unit can provide information using concise and easy-to-understand language. For example, if the parent does not have technical expertise, the providing unit can provide information using concise and easy-to-understand language. In addition, the providing unit can cause the generation AI to automatically evaluate the parent's level of expertise and select appropriate terminology. For example, the providing unit can cause the generation AI to automatically evaluate the parent's level of expertise and select appropriate terminology based on that evaluation. This makes it easier for the parent to understand the information by adjusting the use of technical terminology depending on the level of expertise.

[0046] The providing unit can provide information related to a specific event or occurrence to a child, enabling a parent to deepen their understanding of the event. The providing unit, for example, provides information about an event that a child is scheduled to attend. For example, the providing unit can provide information about an event that a child is scheduled to attend to a parent. The providing unit can also provide news and articles related to events in which a child is interested. For example, the providing unit can provide news and articles related to events in which a child is interested to a parent. The providing unit can also extract information related to a specific event or occurrence from content posted by a child and provide the information to a parent. For example, the providing unit can analyze content posted by a child, extract information related to a specific event or occurrence, and provide the information to a parent. In this way, providing information related to a specific event or occurrence makes it easier for a parent to understand their child's activities.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The providing unit may provide interactive functions that allow parents to understand their child's interests and psychological state. For example, the providing unit may provide an interface that allows parents to request detailed information on a specific topic. The providing unit may also provide a chatbot function that allows parents to directly ask questions about their child's interests and psychological state to the generation AI. Furthermore, the providing unit may provide a dashboard that allows parents to customize and display information about their child's interests and psychological state. This allows parents to more proactively understand their child's situation.

[0049] The providing unit may provide a notification function that allows parents to understand their child's interests and psychological state. For example, the providing unit may send a notification to the parent in real time if an important change in the child's interests or psychological state is detected. The providing unit may also customize notifications based on specific conditions set by the parent. Furthermore, the providing unit may provide a function that allows parents to check the history of past notifications. This allows parents to understand their child's situation in a timely manner without missing important information.

[0050] The providing unit can provide a report function that allows parents to understand their child's interests and psychological state. For example, the providing unit can automatically generate weekly or monthly reports on the child's interests and psychological state and provide them to the parent. The providing unit can also provide a function that allows parents to visually check fluctuations in the child's interests and psychological state over a specific period using graphs and charts. Furthermore, the providing unit can also provide a function that allows parents to customize and display the contents of the report. This makes it easier for parents to regularly understand their child's situation.

[0051] The providing unit may provide an alert function that allows parents to understand their child's interests and psychological state. For example, the providing unit may send an alert to the parent if an abnormal change in the child's interests or psychological state is detected. The providing unit may also customize the alert based on specific conditions set by the parent. Furthermore, the providing unit may provide a function that allows parents to check the history of past alerts. This allows parents to understand their child's situation in a timely manner without missing important information.

[0052] The providing unit can provide a calendar function that enables parents to understand their child's interests and psychological state. For example, the providing unit can display important events and occurrences related to the child's interests and psychological state on a calendar so that parents can check them. The providing unit can also provide a function that enables parents to visually check fluctuations in their child's interests and psychological state over a specific period on the calendar. Furthermore, the providing unit can also provide a function that enables parents to customize and display the contents of the calendar. This makes it easier for parents to understand their child's situation over time.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The analysis unit uses the generation AI installed on the child's smartphone to analyze data from social media and messaging apps to estimate the child's interests and psychological state. For example, if the child frequently posts about a particular game or anime, the analysis unit can estimate the child's interests based on that information. The analysis unit can also estimate the child's psychological state by analyzing the content of the child's posts and the tone of their messages. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze text and perform sentiment analysis using natural language processing technology. Step 2: The providing unit provides the results estimated by the analysis unit to the parent. For example, the providing unit may visually display the results in a dashboard format to allow the parent to intuitively understand. If the child is interested in a particular game, the providing unit may provide the name of the game and related information. Furthermore, if the child is feeling stressed, the providing unit may provide advice on the cause and measures to take.

[0055] (Example 2) A system according to an embodiment of the present invention uses a generating AI to help parents understand their children's interests and psychological state while protecting their privacy. This system installs a generating AI on a child's smartphone, analyzes data from social media and messaging apps, estimates the child's interests and psychological state, and provides the results to the parent. For example, if a child frequently posts about a particular game or anime, the generating AI can estimate the child's interests based on that information. The generating AI can also estimate the child's psychological state by analyzing the content of the child's posts and the tone of their messages. This allows parents to understand their child's interests and psychological state without violating their child's privacy. The system allows parents to understand their child's situation based on the information provided by the generating AI, without the need to forcibly take away their child's smartphone or directly check the content of social media posts. For example, parents can deepen their understanding of the games their child is interested in, or, if their child is feeling stressed, provide advice on the causes and solutions. This facilitates communication between parents and children, supporting their healthy development.

[0056] A system for understanding a child's interests and psychological state according to an embodiment includes an analysis unit and a provision unit. The analysis unit uses a generation AI installed on a child's smartphone to analyze data from social media and messaging apps to estimate the child's interests and psychological state. For example, if a child frequently posts about a particular game or anime, the analysis unit estimates the child's interests based on that information. The analysis unit can also estimate the child's psychological state by analyzing the content of the child's posts and the tone of their messages. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze text and perform sentiment analysis using natural language processing technology. The provision unit provides the results estimated by the analysis unit to parents. For example, the provision unit visually displays the results in a dashboard format to allow parents to intuitively understand. If a child is interested in a particular game, the provision unit can provide the name of the game and related information. Furthermore, if a child is feeling stressed, the provision unit can provide advice on the cause and countermeasures. As a result, the system for understanding a child's interests and psychological state according to an embodiment allows parents to understand their child's interests and psychological state while protecting their child's privacy.

[0057] The analysis unit can analyze text and perform sentiment analysis using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can divide text into words using morphological analysis and identify the part of speech of each word. The analysis unit can also analyze sentence structure using grammatical analysis to clarify relationships between subjects, predicates, objects, and the like. The analysis unit can also understand the meaning of text and perform sentiment analysis using semantic analysis. For example, the analysis unit can identify words and phrases in the text that indicate positive emotions and infer positive emotions. The analysis unit can also identify words and phrases in the text that indicate negative emotions and infer negative emotions. This makes it possible to perform sentiment analysis of text using natural language processing technology.

[0058] The providing unit may visually display the information in a dashboard format, allowing parents to intuitively understand the information. Dashboard formats include, but are not limited to, graphs, charts, tables, and the like. For example, the providing unit may display data indicating a child's interests and psychological state as a graph. The providing unit may also display fluctuations in the child's emotions as a chart. The providing unit may also display detailed information regarding the child's interests and psychological state as a table. For example, the providing unit may display a list of topics and activities in which the child is interested, allowing parents to intuitively understand the information. This allows parents to intuitively understand their child's interests and psychological state.

[0059] The analysis unit can analyze the content of a child's posts and the tone of the messages to identify positive and negative emotions. The analysis unit can, for example, identify words and phrases that indicate positive emotions from the content of a child's posts. For example, the analysis unit can identify words that indicate positive emotions, such as "fun," "happy," and "interesting." The analysis unit can also analyze the tone of a child's messages to identify patterns that indicate negative emotions. For example, the analysis unit can identify words that indicate negative emotions, such as "sad," "painful," and "unpleasant." The analysis unit can also comprehensively analyze the content of a child's posts and the tone of the messages to identify emotional trends. For example, the analysis unit can calculate the ratio of positive and negative emotions to understand the child's emotional trends. In this way, by identifying a child's emotions, parents can understand their child's psychological state.

[0060] If a child is interested in a particular game, the providing unit can provide the name of the game and related information. For example, the providing unit can provide the name of the game in which the child is interested to the parent. For example, the providing unit can list the names of games that the child frequently posts and provide the list to the parent. The providing unit can also provide news and articles related to the game in which the child is interested. For example, the providing unit can provide the parent with the latest information and update information on the game. The providing unit can also provide gameplay videos and reviews of the game in which the child is interested. For example, the providing unit can provide the parent with gameplay videos and reviews of the game, allowing the parent to deepen their understanding of the game. This allows the parent to deepen their understanding of the game in which the child is interested.

[0061] If a child is feeling stressed, the provision unit can provide advice on the causes and countermeasures. For example, the provision unit can analyze the causes of a child's stress and advise parents on countermeasures. For example, if a child is feeling stressed due to problems at school or interpersonal issues, the provision unit can identify the causes and advise parents on countermeasures. The provision unit can also have the generating AI suggest specific methods for reducing stress. For example, the provision unit can suggest stress reduction methods to parents, such as relaxation techniques, counseling, and exercise. The provision unit can also provide information related to children's stress and support parents in taking appropriate countermeasures. For example, the provision unit can provide parents with articles on stress and expert advice. This allows parents to understand the causes of their children's stress and take appropriate countermeasures.

[0062] The analysis unit can estimate the child's emotions and adjust the timing of analysis based on the estimated child's emotions. For example, if the analysis unit estimates that the child is stressed, the generation AI can reduce the frequency of analysis, thereby reducing the burden on the child. For example, if the analysis unit estimates that the child is stressed, the analysis unit can reduce the frequency of analysis from once a week to once a month. Also, if the analysis unit estimates that the child is relaxed, the generation AI can increase the frequency of analysis and collect more detailed data. For example, if the analysis unit estimates that the child is relaxed, the analysis unit can increase the frequency of analysis from once a week to daily. Also, if the child is excited, the analysis unit can adjust the timing of analysis and collect data at an appropriate time. For example, if the analysis unit estimates that the child is excited, the analysis unit can change the timing of analysis to after the child has calmed down. This allows the burden on the child to be reduced by adjusting the timing of analysis according to the child's emotions.

[0063] The analysis unit can analyze a child's past posting history and select the optimal analysis method. For example, the analysis unit uses a generation AI to select the optimal analysis method based on words and phrases frequently used by the child in the past. For example, the analysis unit can extract words and phrases frequently used by the child in the past and select the optimal analysis method based on the extracted words and phrases. The analysis unit can also select an analysis method related to a specific theme or topic from a child's past posting history. For example, the analysis unit can analyze the themes and topics that the child has posted on in the past and select the optimal analysis method based on the analyzed words and phrases. The analysis unit can also analyze a child's past posting history and select an analysis method based on emotional fluctuation patterns. For example, the analysis unit can analyze a child's past posting history, identify emotional fluctuation patterns, and select the optimal analysis method based on the identified emotional fluctuation patterns. This improves the accuracy of the analysis by selecting the optimal analysis method based on past posting history.

[0064] During analysis, the analysis unit can perform filtering based on the child's current living situation and areas of interest. For example, the analysis unit analyzes only posts related to topics in which the child is currently interested. For example, the analysis unit can filter posts related to topics in which the child is currently interested and analyze them. The analysis unit can also prioritize analyzing posts related to the child's current living situation (school, home, etc.). For example, the analysis unit can filter posts related to the child's current living situation and analyze them preferentially. The analysis unit can also filter and analyze posts containing specific keywords based on the child's areas of interest. For example, the analysis unit can filter posts containing keywords related to the child's areas of interest and analyze them. In this way, by filtering based on the child's current living situation and areas of interest, more relevant data can be analyzed.

[0065] During analysis, the analysis unit can select the optimal analysis method depending on the child's input method. For example, when a child uses voice input, the analysis unit causes the generation AI to analyze the data using voice analysis technology. For example, when a child uses voice input, the analysis unit can convert the voice data into text data using voice recognition technology and analyze the text data. Furthermore, when a child uses text input, the analysis unit can cause the generation AI to analyze the text using natural language processing technology. For example, when a child uses text input, the analysis unit can perform sentiment analysis of the text using natural language processing technology. Furthermore, when a child posts images, the analysis unit can cause the generation AI to analyze the data using image analysis technology. For example, when a child posts an image, the analysis unit can analyze the image data using image recognition technology to understand the content. This improves the accuracy of the analysis by selecting the optimal analysis method depending on the input method.

[0066] The analysis unit can analyze the content of a child's posts and the tone of the messages to detect specific emotional fluctuations. The analysis unit, for example, detects patterns of emotional fluctuations from the content of a child's posts. For example, the analysis unit can analyze the content of a child's posts in chronological order to identify patterns of emotional fluctuations. The analysis unit can also analyze the tone of a child's messages to detect emotional fluctuations in real time. For example, the analysis unit can analyze the tone of a child's messages in real time to instantly detect emotional fluctuations. The analysis unit can also comprehensively analyze the content of a child's posts and the tone of the messages to monitor emotional fluctuations over the long term. For example, the analysis unit can monitor the content of a child's posts and the tone of the messages over the long term to understand patterns of emotional fluctuations. In this way, by detecting specific emotional fluctuations, changes in the child's psychological state can be understood.

[0067] The analysis unit can estimate the child's emotions and determine the priority of data to be analyzed based on the child's estimated emotions. For example, if the child is feeling stressed, the analysis unit causes the generation AI to prioritize analyzing data related to stress. For example, if it is estimated that the child is feeling stressed, the analysis unit can prioritize analyzing data related to stress. Furthermore, if the child is relaxed, the analysis unit can also cause the generation AI to prioritize analyzing data related to interests and concerns. For example, if it is estimated that the child is relaxed, the analysis unit can prioritize analyzing data related to interests and concerns. Furthermore, if the child is excited, the analysis unit can also cause the generation AI to prioritize analyzing data related to excitement. For example, if it is estimated that the child is excited, the analysis unit can prioritize analyzing data related to excitement. In this way, by determining the priority of data based on emotions, important data can be analyzed preferentially.

[0068] During analysis, the analysis unit can prioritize analyzing highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific location, the analysis unit can prioritize analyzing data related to that location. For example, if the child is in a specific location, the analysis unit can filter and prioritize analyzing data related to that location. The analysis unit can also analyze data related to nearby events and activities based on the child's geographical location information. For example, the analysis unit can filter and analyze data related to nearby events and activities based on the child's geographical location information. The analysis unit can also prioritize analyzing data related to region-specific topics by taking into account the child's geographical location information. For example, the analysis unit can filter and prioritize analyzing data related to region-specific topics based on the child's geographical location information. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0069] During the analysis, the analysis unit can analyze the child's social media activities and analyze the related data. For example, the analysis unit can prioritize the analysis of content that the child frequently posts on social media. For example, the analysis unit can filter and prioritize the analysis of content that the child frequently posts on social media. The analysis unit can also analyze the child's friendships on social media and analyze the related data. For example, the analysis unit can analyze the child's friendships on social media and analyze the related data based on the friendships. The analysis unit can also analyze the child's activity patterns on social media and select the optimal analysis method. For example, the analysis unit can analyze the child's activity patterns on social media and select the optimal analysis method based on the friendships. This allows the related data to be analyzed efficiently by analyzing social media activities.

[0070] During analysis, the analysis unit can customize the analysis method by reflecting the child's past feedback. The analysis unit customizes the analysis method, for example, based on feedback provided by the child in the past. For example, the analysis unit can analyze feedback provided by the child in the past and customize the analysis method based on the feedback. The analysis unit can also preferentially use a specific analysis method based on the child's past feedback. For example, the analysis unit can preferentially use a specific analysis method based on the child's past feedback. The analysis unit can also improve the accuracy of the analysis by reflecting the child's past feedback. For example, the analysis unit can adjust the algorithm for improving the accuracy of the analysis by reflecting the child's past feedback. In this way, the accuracy of the analysis is improved by reflecting the past feedback.

[0071] The analysis unit can analyze the content of the child's posts and the tone of the messages to identify emotions associated with a specific event or occurrence. The analysis unit can, for example, identify emotions associated with a specific event from the content of the child's posts. For example, the analysis unit can analyze the content of the child's posts and identify emotions associated with a specific event. The analysis unit can also analyze the tone of the child's messages to identify emotions associated with a specific event. For example, the analysis unit can analyze the tone of the child's messages to identify emotions associated with a specific event. The analysis unit can also comprehensively analyze the content of the child's posts and the tone of the messages to identify emotions associated with a specific event or occurrence. For example, the analysis unit can comprehensively analyze the content of the child's posts and the tone of the messages to identify emotions associated with a specific event or occurrence. In this way, by identifying emotions associated with a specific event or occurrence, the child's psychological state can be understood.

[0072] The analysis unit can analyze the content of the child's posts and the tone of the messages to detect emotional fluctuations during a specific time period or day of the week. The analysis unit can, for example, detect emotional fluctuations during a specific time period from the content of the child's posts. For example, the analysis unit can analyze the content of the child's posts in chronological order to detect emotional fluctuations during a specific time period. The analysis unit can also analyze the tone of the child's messages to detect emotional fluctuations during a specific day of the week. For example, the analysis unit can analyze the tone of the child's messages in chronological order to detect emotional fluctuations during a specific day of the week. The analysis unit can also comprehensively analyze the content of the child's posts and the tone of the messages to detect emotional fluctuations during a specific time period or day of the week. For example, the analysis unit can comprehensively analyze the content of the child's posts and the tone of the messages to detect emotional fluctuations during a specific time period or day of the week. In this way, by detecting emotional fluctuations during a specific time period or day of the week, it is possible to understand changes in the child's psychological state.

[0073] The providing unit can estimate the child's emotions and adjust the way in which the information is presented based on the estimated child's emotions. For example, if the child is feeling stressed, the providing unit can cause the generation AI to provide simple, highly visible information. For example, if it is estimated that the child is feeling stressed, the providing unit can provide simple, highly visible information. Furthermore, if the child is relaxed, the generating AI can provide detailed information. For example, if it is estimated that the child is relaxed, the providing unit can provide detailed information. Furthermore, if the child is excited, the generating AI can provide visually stimulating information. For example, if it is estimated that the child is excited, the providing unit can provide visually stimulating information. In this way, adjusting the way in which the information is presented based on emotions makes it easier for parents to understand the information.

[0074] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing it. For example, the providing unit can provide highly important information in detail and less important information in brief. For example, the providing unit can provide highly important information in detail and less important information in brief. The providing unit can also have the generation AI automatically evaluate the importance of the information and adjust the level of detail of the information provided. For example, the providing unit can have the generation AI automatically evaluate the importance of the information and adjust the level of detail of the information provided based on the evaluation. The providing unit can also have the generation AI adjust the level of detail of the information based on the importance set by the parent. For example, the providing unit can adjust the level of detail of the information based on the importance set by the parent. In this way, by adjusting the level of detail based on the importance of the information, the parent can efficiently obtain the information he or she needs.

[0075] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can provide detailed information related to the child's interests and concise other information. For example, the providing unit can provide detailed information related to the child's interests and concise other information. The providing unit can also have the generation AI automatically classify the category of information and apply an appropriate providing algorithm. For example, the providing unit can have the generation AI automatically classify the category of information and apply an appropriate providing algorithm based on that. The providing unit can also adjust the way the generation AI provides information based on the category set by the parent. For example, the providing unit can adjust the way the information is provided based on the category set by the parent. This allows parents to efficiently understand the information by applying a providing algorithm according to the category of information.

[0076] When providing information, the providing unit can improve the accuracy of the information provided by referring to the parent's past usage history. For example, the providing unit allows the generation AI to improve the accuracy of the information provided based on information viewed by the parent in the past. For example, the providing unit can analyze information viewed by the parent in the past and improve the accuracy of the information provided based on that. The providing unit can also provide specific information preferentially based on the parent's past usage history. For example, the providing unit can provide specific information preferentially based on the parent's past usage history. The providing unit can also improve the accuracy of the generation AI by reflecting the parent's past feedback. For example, the providing unit can reflect the parent's past feedback and improve the accuracy of the information provided based on that. In this way, the accuracy of the information provided is improved by referring to the past usage history.

[0077] The providing unit can estimate the child's emotions and adjust the length of the information to be provided based on the estimated child's emotions. For example, if the child is feeling stressed, the providing unit can cause the generation AI to provide short, to-the-point information. For example, if the providing unit estimates that the child is feeling stressed, the providing unit can provide short, to-the-point information. Furthermore, if the child is relaxed, the generating AI can provide detailed information. For example, if the providing unit estimates that the child is relaxed, the providing unit can provide detailed information. Furthermore, if the child is excited, the generating AI can provide visually stimulating information. For example, if the providing unit estimates that the child is excited, the providing unit can provide visually stimulating information. In this way, adjusting the length of the information based on emotions makes it easier for parents to understand the information.

[0078] The providing unit can determine the priority of provision based on the time of submission of information when the information is provided. For example, the providing unit can provide the latest information preferentially and provide old information briefly. For example, the providing unit can provide the latest information preferentially and provide old information briefly. The providing unit can also have the generation AI automatically evaluate the time of submission of information and determine the priority of provision. For example, the providing unit can have the generation AI automatically evaluate the time of submission of information and determine the priority of provision based on that. The providing unit can also adjust the method of providing information by the generation AI based on the submission time set by the parent. For example, the providing unit can adjust the method of providing information based on the submission time set by the parent. In this way, by determining the priority based on the submission time, the latest information can be provided preferentially.

[0079] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can prioritize providing information related to the child's interests and postpone other information. For example, the providing unit can prioritize providing information related to the child's interests and postpone other information. The providing unit can also have the generation AI automatically evaluate the relevance of the information and adjust the order of provision. For example, the providing unit can have the generation AI automatically evaluate the relevance of the information and adjust the order of provision based on that. The providing unit can also have the generation AI adjust the order of provision of the information based on the relevance set by the parent. For example, the providing unit can adjust the order of provision of the information based on the relevance set by the parent. In this way, by adjusting the order of provision based on relevance, the parent can obtain important information preferentially.

[0080] When providing the information, the providing unit can adjust the use of technical terminology in the provision depending on the parent's level of expertise. For example, if the parent has technical expertise, the providing unit can provide information using detailed technical terminology. For example, if the parent has technical expertise, the providing unit can provide information using detailed technical terminology. In addition, if the parent does not have technical expertise, the providing unit can provide information using concise and easy-to-understand language. For example, if the parent does not have technical expertise, the providing unit can provide information using concise and easy-to-understand language. In addition, the providing unit can cause the generation AI to automatically evaluate the parent's level of expertise and select appropriate terminology. For example, the providing unit can cause the generation AI to automatically evaluate the parent's level of expertise and select appropriate terminology based on that evaluation. This makes it easier for the parent to understand the information by adjusting the use of technical terminology depending on the level of expertise.

[0081] The providing unit can provide information related to a specific event or occurrence to a child, enabling a parent to deepen their understanding of the event. The providing unit, for example, provides information about an event that a child is scheduled to attend. For example, the providing unit can provide information about an event that a child is scheduled to attend to a parent. The providing unit can also provide news and articles related to events in which a child is interested. For example, the providing unit can provide news and articles related to events in which a child is interested to a parent. The providing unit can also extract information related to a specific event or occurrence from content posted by a child and provide the information to a parent. For example, the providing unit can analyze content posted by a child, extract information related to a specific event or occurrence, and provide the information to a parent. In this way, providing information related to a specific event or occurrence makes it easier for a parent to understand their child's activities. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation AI, provision unit, and dashboard display function is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. For example, the generation AI is executed by the processor 46 of the smart device 14 and analyzes data from social media and messaging apps. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the analysis results to the parent. For example, the function of visually displaying in dashboard format is realized by the display 40A of the smart device 14. The provision unit may also be realized by the specific processing unit 290 of the data processing device 12 and provide the analysis results to the parent via the cloud. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, generation AI, provision unit, and dashboard display function is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. For example, the generation AI is executed by the processor 46 of the smart glasses 214 and analyzes data from social media and messaging apps. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the analysis results to the parent. For example, the function of visually displaying in dashboard format is realized by the display of the smart glasses 214. The provision unit may also be realized by the specific processing unit 290 of the data processing device 12 and provide the analysis results to the parent via the cloud. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation AI, provision unit, and dashboard display function is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. For example, the generation AI is executed by the processor 46 of the headset type terminal 314 and analyzes data from social media and messaging apps. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the analysis results to the parent. For example, the function of visually displaying in dashboard format is realized by the display 343 of the headset type terminal 314. The provision unit may also be realized by the specific processing unit 290 of the data processing device 12 and provide the analysis results to the parent via the cloud. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation AI, provision unit, and dashboard display function is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12. For example, the generation AI is executed by the processor 46 of the robot 414 and analyzes data from social media and messaging apps. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the analysis results to the parent. For example, the function of visually displaying in dashboard format is realized by the display of the robot 414. The provision unit may also be realized by the specific processing unit 290 of the data processing device 12 and provide the analysis results to the parent via the cloud.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The analysis unit can take into account a child's past behavioral patterns when analyzing the content of a child's posts and the tone of their messages. For example, the analysis unit can analyze how a child has felt about specific events or happenings in the past and compare that with the content of their current posts and the tone of their messages. The analysis unit can also estimate a child's current interests based on what topics the child has been interested in in the past. Furthermore, the analysis unit can predict future changes in behavior and emotions based on the child's past behavioral patterns. This allows for more accurate analysis by taking past behavioral patterns into account.

[0084] The providing unit may provide interactive functions that allow parents to understand their child's interests and psychological state. For example, the providing unit may provide an interface that allows parents to request detailed information on a specific topic. The providing unit may also provide a chatbot function that allows parents to directly ask questions about their child's interests and psychological state to the generation AI. Furthermore, the providing unit may provide a dashboard that allows parents to customize and display information about their child's interests and psychological state. This allows parents to more proactively understand their child's situation.

[0085] The analysis unit can take into account the child's friendships when analyzing the content of the child's posts and the tone of the messages. For example, if the child frequently interacts with a particular friend, the analysis unit can estimate the child's interests and psychological state by taking into account the influence of that friend. The analysis unit can also analyze changes in the child's friendships and evaluate the impact they have on the child's psychological state. Furthermore, the analysis unit can determine the social network the child belongs to based on the child's friendships and evaluate the child's position within that network. This allows for a more comprehensive analysis by taking friendships into account.

[0086] The providing unit may provide a notification function that allows parents to understand their child's interests and psychological state. For example, the providing unit may send a notification to the parent in real time if an important change in the child's interests or psychological state is detected. The providing unit may also customize notifications based on specific conditions set by the parent. Furthermore, the providing unit may provide a function that allows parents to check the history of past notifications. This allows parents to understand their child's situation in a timely manner without missing important information.

[0087] The analysis unit can take the child's health condition into consideration when analyzing the content of the child's posts and the tone of the messages. For example, the analysis unit can detect posts or messages in which the child complains of poor health and estimate the child's psychological state based on that information. The analysis unit can also collect data on the child's health condition and evaluate the impact that data has on the child's interests and psychological state. Furthermore, the analysis unit can predict future changes in the child's psychological state based on the child's health condition. This allows for more accurate analysis by taking the child's health condition into consideration.

[0088] The providing unit can provide a report function that allows parents to understand their child's interests and psychological state. For example, the providing unit can automatically generate weekly or monthly reports on the child's interests and psychological state and provide them to the parent. The providing unit can also provide a function that allows parents to visually check fluctuations in the child's interests and psychological state over a specific period using graphs and charts. Furthermore, the providing unit can also provide a function that allows parents to customize and display the contents of the report. This makes it easier for parents to regularly understand their child's situation.

[0089] The analysis unit can take into account a child's academic performance when analyzing the content of a child's posts and the tone of their messages. For example, if a child's grades are declining, the analysis unit can identify the cause and evaluate the impact on the child's psychological state. The analysis unit can also collect data on a child's academic performance and evaluate the impact it has on the child's interests and psychological state. Furthermore, the analysis unit can predict future changes in the child's psychological state based on the child's academic performance. This allows for more accurate analysis by taking academic performance into account.

[0090] The providing unit may provide an alert function that allows parents to understand their child's interests and psychological state. For example, the providing unit may send an alert to the parent if an abnormal change in the child's interests or psychological state is detected. The providing unit may also customize the alert based on specific conditions set by the parent. Furthermore, the providing unit may provide a function that allows parents to check the history of past alerts. This allows parents to understand their child's situation in a timely manner without missing important information.

[0091] The analysis unit can take into account the child's hobbies and special skills when analyzing the content of the child's posts and the tone of the messages. For example, if the child frequently posts about a particular hobby or special skill, the analysis unit can estimate the child's interests based on that information. The analysis unit can also collect data on the child's hobbies and special skills and evaluate the impact that data has on the child's psychological state. Furthermore, the analysis unit can predict future changes in interests and psychological state based on the child's hobbies and special skills. This allows for more accurate analysis by taking hobbies and special skills into account.

[0092] The providing unit can provide a calendar function that enables parents to understand their child's interests and psychological state. For example, the providing unit can display important events and occurrences related to the child's interests and psychological state on a calendar so that parents can check them. The providing unit can also provide a function that enables parents to visually check fluctuations in their child's interests and psychological state over a specific period on the calendar. Furthermore, the providing unit can also provide a function that enables parents to customize and display the contents of the calendar. This makes it easier for parents to understand their child's situation over time.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The analysis unit uses the generation AI installed on the child's smartphone to analyze data from social media and messaging apps to estimate the child's interests and psychological state. For example, if the child frequently posts about a particular game or anime, the analysis unit can estimate the child's interests based on that information. The analysis unit can also estimate the child's psychological state by analyzing the content of the child's posts and the tone of their messages. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, and can analyze text and perform sentiment analysis using natural language processing technology. Step 2: The providing unit provides the results estimated by the analysis unit to the parent. For example, the providing unit may visually display the results in a dashboard format to allow the parent to intuitively understand. If the child is interested in a particular game, the providing unit may provide the name of the game and related information. Furthermore, if the child is feeling stressed, the providing unit may provide advice on the cause and measures to take.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

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

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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. The AI ​​generator installed on the child's smartphone analyzes data from social media and messaging apps to estimate the child's interests and psychological state. a providing unit that provides the result estimated by the analysis unit to a parent. A system characterized by:

2. The analysis unit Analyze text and perform sentiment analysis using natural language processing technology 2. The system of claim 1.

3. The providing unit Visually display the information in a dashboard format to make it intuitive for parents 2. The system of claim 1.

4. The analysis unit Analyze the content and tone of your child's posts to identify positive and negative emotions 2. The system of claim 1.

5. The providing unit If your child is interested in a particular game, provide the name of that game and related information.

2. The system of claim 1.

6. The providing unit If your child is stressed, offer advice on what causes it and what to do about it.

2. The system of claim 1.

7. The analysis unit Inferring the child's emotions and adjusting the timing of analysis based on the estimated emotions 2. The system of claim 1.

8. The analysis unit Analyze the child's past posting history and select the most appropriate analysis method 2. The system of claim 1.

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