System

Abstract

An object of a system according to an embodiment is to accurately estimate an emotion of a child and provide feedback to an adult.SOLUTION: A system according to an embodiment includes a behavior analysis unit, an emotion estimation unit, and a feedback unit. The behavior analysis unit analyzes a behavior or speech of the child. The emotion estimator estimates an emotion of the child on the basis of a result of the analysis performed by the behavior analyzer. The feedback unit feeds back the emotion estimated by the emotion estimation unit to the adult.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately understand a child's emotions and true feelings, making it difficult to respond appropriately.

[0005] The system according to the embodiment aims to accurately estimate the emotions of children and provide feedback to adults. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior analysis unit, a feeling estimation unit, and a feedback unit. The behavior analysis unit analyzes the behavior or speech of the child. The feeling estimation unit estimates the feeling of the child based on the analysis result by the behavior analysis unit. The feedback unit feeds back the feeling estimated by the feeling estimation unit to the adult. [Effects of the Invention]

[0007] The system according to the embodiment can accurately estimate the child's emotions and provide feedback to the adult. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The support system according to an embodiment of the present invention uses AI to verbalize the true feelings of elementary and junior high school students who are not yet old enough to express their feelings and to support the adults around them in understanding the situation. This allows adults to gain a deeper understanding of the children's feelings and thoughts, and to solve the problem of school refusal in an early and appropriate way.

[0029] The support system according to the embodiment includes a behavior analysis unit, an emotion estimation unit, and a feedback unit. The behavior analysis unit analyzes the behavior or statements of a child. For example, the behavior analysis unit observes the child's daily behavior and analyzes the child's behavioral patterns. The behavior analysis unit can also analyze the content of statements made by the child in specific situations. For example, the behavior analysis unit analyzes the content of what the child says about events at school and estimates the emotion behind it. The emotion estimation unit estimates the child's emotion based on the results of the analysis by the behavior analysis unit. For example, the emotion estimation unit estimates the child's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the child's emotion using voice tone analysis technology. For example, the emotion estimation unit analyzes the tone and speed of the child's voice and estimates the emotion. The feedback unit feeds back the emotion estimated by the emotion estimation unit to an adult. For example, the feedback unit communicates the emotion to the adult using a text message. The feedback unit can also communicate the emotion using a voice message. For example, the feedback unit explains the child's emotion using voice and conveys it to the adult. As a result, the support system according to the embodiment accurately estimates a child's emotions and provides feedback to adults, making it easier for them to understand the child's true feelings. For example, the support system monitors a child's emotions in real time and notifies adults of any changes. The support system also visually displays a child's emotions in graphs and charts, making it easier for adults to understand. Furthermore, the support system translates a child's emotions into multiple languages, making it easier for adults from different language backgrounds to understand.

[0030] The behavior analysis unit can refer to past behavioral history or speech history and perform analysis taking into account long-term trends. For example, when the generation AI analyzes a child's behavior or speech, the behavior analysis unit refers to past behavioral history and speech history and performs analysis taking into account long-term trends. For example, it analyzes behavioral patterns over the past few months and finds correlations with current behavior. The behavior analysis unit refers to past behavioral history and speech history and performs analysis taking into account long-term trends. For example, when the generation AI analyzes a child's behavior or speech, it refers to past behavioral history and speech history and performs analysis taking into account long-term trends. In this way, by referring to past behavioral history and speech history, analysis taking into account long-term trends becomes possible.

[0031] The behavior analysis unit takes into account the individual characteristics (personality or interests) of the child to perform a more personalized analysis. For example, when the generation AI analyzes a child's behavior or comments, the behavior analysis unit takes into account the individual characteristics (e.g., personality or interests) of the child to perform a more personalized analysis. For example, different analysis methods are used for children with introverted personalities and children with extroverted personalities. The behavior analysis unit takes into account the individual characteristics (e.g., personality or interests) of the child to perform a more personalized analysis. For example, when the generation AI analyzes a child's behavior or comments, the behavior analysis unit takes into account the individual characteristics (e.g., personality or interests) of the child to perform a more personalized analysis. In this way, by taking into account the individual characteristics of the child, a more personalized analysis is possible.

[0032] The behavior analysis unit can simultaneously analyze voice or facial expression data and perform analysis based on multimodal information. For example, when the generation AI analyzes a child's behavior or statements, the behavior analysis unit simultaneously analyzes voice and facial expression data and performs analysis based on multimodal information. For example, it simultaneously analyzes changes in the child's tone of voice and facial expression, and records changes in emotion in detail. The behavior analysis unit simultaneously analyzes voice and facial expression data and performs analysis based on multimodal information. For example, when the generation AI analyzes a child's behavior or statements, it simultaneously analyzes voice and facial expression data and performs analysis based on multimodal information. This makes it possible to estimate emotions in more detail by simultaneously analyzing voice and facial expression data.

[0033] The behavior analysis unit is able to take into account the backgrounds of different cultures and regions and perform an analysis that reflects cultural differences. For example, when the generation AI analyzes a child's behavior or utterances, the behavior analysis unit takes into account the backgrounds of different cultures and regions and performs an analysis that reflects cultural differences. For example, it analyzes the behavior and utterances of children from different cultural backgrounds and adjusts the analysis results by taking into account their cultural backgrounds. The behavior analysis unit takes into account the backgrounds of different cultures and regions and performs an analysis that reflects cultural differences. For example, when the generation AI analyzes a child's behavior or utterances, it takes into account the backgrounds of different cultures and regions and performs an analysis that reflects cultural differences. In this way, by taking into account the backgrounds of different cultures and regions, an analysis that reflects cultural differences becomes possible.

[0034] The feedback unit can visually display the feedback content, allowing it to be intuitively understood using graphs and charts. For example, when feeding back the results of an analysis by the generation AI, the feedback unit visually displays the feedback content, allowing it to be intuitively understood using graphs and charts. For example, changes in a child's emotions can be shown in a graph to make it visually easier to understand. The feedback unit visually displays the feedback content, allowing it to be intuitively understood using graphs and charts. For example, when feeding back the results of an analysis by the generation AI, the feedback content can be visually displayed, allowing it to be intuitively understood using graphs and charts. In this way, visually displaying the feedback content makes it easier to understand intuitively.

[0035] The feedback unit can translate the feedback content into multiple languages ​​and provide it in a form that is easy to understand for adults from different language speaking areas. For example, when feeding back the results of an analysis by the generation AI, the feedback unit translates the feedback content into multiple languages ​​and provides it in a form that is easy to understand for adults from different language speaking areas. For example, the feedback unit translates the feedback content into multiple languages, such as English and Chinese, and provides it. The feedback unit translates the feedback content into multiple languages ​​and provides it in a form that is easy to understand for adults from different language speaking areas. For example, when feeding back the results of an analysis by the generation AI, the feedback content is translated into multiple languages ​​and provides it in a form that is easy to understand for adults from different language speaking areas. In this way, translating the feedback content into multiple languages ​​makes it easier for adults from different language speaking areas to understand.

[0036] The feedback unit can display the feedback content as animation or video, and communicate it in a visually easy-to-understand format. For example, when feeding back the results of analysis by the generation AI, the feedback unit can display the feedback content as animation or video, and communicate it in a visually easy-to-understand format. For example, changes in a child's emotions can be shown using animation, making it visually easy to understand. The feedback unit can display the feedback content as animation or video, and communicate it in a visually easy-to-understand format. For example, when feeding back the results of analysis by the generation AI, the feedback content can be displayed as animation or video, and communicate it in a visually easy-to-understand format. In this way, displaying the feedback content as animation or video makes it visually easy to understand.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The support system can further include a health management unit that monitors the child's health. The health management unit collects biological data such as the child's heart rate and body temperature, and evaluates the child's health based on this data. For example, it can analyze heart rate fluctuations to estimate stress levels. It can also monitor changes in body temperature to detect early signs of poor health. Furthermore, the health management unit can analyze the child's sleep patterns and detect signs of insufficient or excessive sleep. This makes it possible to comprehensively understand the child's health and provide appropriate support.

[0039] The support system can further include a learning management unit that monitors the learning progress of children. The learning management unit records children's learning activities and evaluates their progress. For example, it can analyze which subjects a child is interested in and provide advice to increase their motivation to study. The learning management unit can also evaluate a child's level of understanding and provide supplementary lessons or additional learning resources as needed. Furthermore, the learning management unit can create a customized learning plan that matches the child's learning style and support effective learning. This makes it possible to grasp a child's learning progress comprehensively and provide appropriate support.

[0040] The support system can further include a social skills assessment unit that evaluates a child's social skills. The social skills assessment unit evaluates a child's interpersonal relationships and communication abilities and provides advice to improve those skills. For example, it can analyze a child's behavioral patterns when building relationships with friends and suggest appropriate communication methods. The social skills assessment unit can also observe a child's behavior when participating in group activities and evaluate their cooperation and leadership skills. Furthermore, the social skills assessment unit can analyze how a child cope when stressed and provide advice to improve stress management skills. This makes it possible to comprehensively evaluate a child's social skills and provide appropriate support.

[0041] The support system may further include a creativity evaluation unit that evaluates a child's creativity. The creativity evaluation unit records a child's creative activities and evaluates their creativity. For example, it may analyze a child's drawings or works of art and evaluate their creativity. The creativity evaluation unit may also analyze a child's thought process when coming up with a new idea and provide advice to promote creative thinking. Furthermore, the creativity evaluation unit may observe a child's approach to problem solving and provide advice to improve creative problem-solving skills. This makes it possible to comprehensively evaluate a child's creativity and provide appropriate support.

[0042] The support system can further include a career assessment unit that evaluates a child's future career aspirations. The career assessment unit evaluates the child's future career aspirations based on the child's interests and abilities and suggests an appropriate career path. For example, if a child shows a strong interest in a particular field, it can suggest a job related to that field. The career assessment unit can also evaluate a child's abilities and skills and suggest an appropriate career path based on that. Furthermore, the career assessment unit can support the child in setting future goals and suggest specific steps toward achieving those goals. This makes it possible to comprehensively evaluate a child's future career aspirations and provide appropriate support.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The behavior analysis unit analyzes the child's behavior or statements. For example, the behavior analysis unit observes the child's daily behavior and analyzes their behavioral patterns. The behavior analysis unit can also analyze what the child says in specific situations. For example, the behavior analysis unit analyzes what the child says about events at school and infers the emotions behind them. Step 2: The emotion estimation unit estimates the child's emotion based on the results of the analysis by the behavior analysis unit. For example, the emotion estimation unit estimates the child's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the child's emotion using voice tone analysis technology. For example, the emotion estimation unit analyzes the tone and speed of the child's voice to estimate the emotion. Step 3: The feedback unit feeds back the emotion estimated by the emotion estimation unit to the adult. For example, the feedback unit may convey the emotion to the adult using a text message. The feedback unit may also convey the emotion using a voice message. For example, the feedback unit may explain the child's emotion by voice and convey it to the adult.

[0045] (Example 2) The support system according to an embodiment of the present invention uses AI to verbalize the true feelings of elementary and junior high school students who are not yet old enough to express their feelings and to support the adults around them in understanding the situation. This allows adults to gain a deeper understanding of the children's feelings and thoughts, and to solve the problem of school refusal in an early and appropriate way.

[0046] The support system according to the embodiment includes a behavior analysis unit, an emotion estimation unit, and a feedback unit. The behavior analysis unit analyzes the behavior or statements of a child. For example, the behavior analysis unit observes the child's daily behavior and analyzes the child's behavioral patterns. The behavior analysis unit can also analyze the content of statements made by the child in specific situations. For example, the behavior analysis unit analyzes the content of what the child says about events at school and estimates the emotion behind it. The emotion estimation unit estimates the child's emotion based on the results of the analysis by the behavior analysis unit. For example, the emotion estimation unit estimates the child's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the child's emotion using voice tone analysis technology. For example, the emotion estimation unit analyzes the tone and speed of the child's voice and estimates the emotion. The feedback unit feeds back the emotion estimated by the emotion estimation unit to an adult. For example, the feedback unit communicates the emotion to the adult using a text message. The feedback unit can also communicate the emotion using a voice message. For example, the feedback unit explains the child's emotion using voice and conveys it to the adult. As a result, the support system according to the embodiment accurately estimates a child's emotions and provides feedback to adults, making it easier for them to understand the child's true feelings. For example, the support system monitors a child's emotions in real time and notifies adults of any changes. The support system also visually displays a child's emotions in graphs and charts, making it easier for adults to understand. Furthermore, the support system translates a child's emotions into multiple languages, making it easier for adults from different language backgrounds to understand.

[0047] The behavior analysis unit can use the emotion estimation function to track changes in a child's emotions in real time and update the analysis results based on those changes. For example, when the generation AI analyzes a child's behavior or utterances, the behavior analysis unit uses the emotion estimation function to track changes in a child's emotions in real time. For example, it detects the moment a child smiles or sheds tears and reflects those changes in emotion in the analysis results. The behavior analysis unit uses the emotion estimation function to track changes in a child's emotions in real time and updates the analysis results based on those changes. For example, when the generation AI analyzes a child's behavior or utterances, the emotion estimation function to track changes in a child's emotions in real time and updates the analysis results based on those changes. In this way, by tracking changes in a child's emotions in real time and updating the analysis results, more accurate emotion estimation is possible.

[0048] The behavior analysis unit can refer to past behavioral history or speech history and perform analysis taking into account long-term trends. For example, when the generation AI analyzes a child's behavior or speech, the behavior analysis unit refers to past behavioral history and speech history and performs analysis taking into account long-term trends. For example, it analyzes behavioral patterns over the past few months and finds correlations with current behavior. The behavior analysis unit refers to past behavioral history and speech history and performs analysis taking into account long-term trends. For example, when the generation AI analyzes a child's behavior or speech, it refers to past behavioral history and speech history and performs analysis taking into account long-term trends. In this way, by referring to past behavioral history and speech history, analysis taking into account long-term trends becomes possible.

[0049] The behavior analysis unit takes into account the individual characteristics (personality or interests) of the child to perform a more personalized analysis. For example, when the generation AI analyzes a child's behavior or comments, the behavior analysis unit takes into account the individual characteristics (e.g., personality or interests) of the child to perform a more personalized analysis. For example, different analysis methods are used for children with introverted personalities and children with extroverted personalities. The behavior analysis unit takes into account the individual characteristics (e.g., personality or interests) of the child to perform a more personalized analysis. For example, when the generation AI analyzes a child's behavior or comments, the behavior analysis unit takes into account the individual characteristics (e.g., personality or interests) of the child to perform a more personalized analysis. In this way, by taking into account the individual characteristics of the child, a more personalized analysis is possible.

[0050] The behavior analysis unit can simultaneously analyze voice or facial expression data and perform analysis based on multimodal information. For example, when the generation AI analyzes a child's behavior or statements, the behavior analysis unit simultaneously analyzes voice and facial expression data and performs analysis based on multimodal information. For example, it simultaneously analyzes changes in the child's tone of voice and facial expression, and records changes in emotion in detail. The behavior analysis unit simultaneously analyzes voice and facial expression data and performs analysis based on multimodal information. For example, when the generation AI analyzes a child's behavior or statements, it simultaneously analyzes voice and facial expression data and performs analysis based on multimodal information. This makes it possible to estimate emotions in more detail by simultaneously analyzing voice and facial expression data.

[0051] The behavior analysis unit is able to take into account the backgrounds of different cultures and regions and perform an analysis that reflects cultural differences. For example, when the generation AI analyzes a child's behavior or utterances, the behavior analysis unit takes into account the backgrounds of different cultures and regions and performs an analysis that reflects cultural differences. For example, it analyzes the behavior and utterances of children from different cultural backgrounds and adjusts the analysis results by taking into account their cultural backgrounds. The behavior analysis unit takes into account the backgrounds of different cultures and regions and performs an analysis that reflects cultural differences. For example, when the generation AI analyzes a child's behavior or utterances, it takes into account the backgrounds of different cultures and regions and performs an analysis that reflects cultural differences. In this way, by taking into account the backgrounds of different cultures and regions, an analysis that reflects cultural differences becomes possible.

[0052] The behavior analysis unit can use the emotion estimation function to estimate the child's emotions when analyzing the child's behavior and utterances, and adjust the analysis results based on those emotions. For example, the behavior analysis unit uses the emotion estimation function to estimate the child's emotions when analyzing the child's behavior and utterances, and adjust the analysis results based on those emotions. For example, the behavior analysis unit simultaneously analyzes what the child is saying and changes in facial expressions, and records the changes in emotion in detail. For example, the behavior analysis unit uses the emotion estimation function to estimate the child's emotions when analyzing the child's behavior and utterances, and adjusts the analysis results based on those emotions. For example, the emotion estimation function can estimate the child's emotions when analyzing the child's behavior and utterances, and adjust the analysis results based on those emotions. In this way, by using the emotion estimation function, it is possible to adjust the analysis results based on the child's emotions.

[0053] The feedback unit can use the emotion estimation function to select expressions that are easiest for adults to understand and provide feedback. For example, when feeding back the results of the analysis by the generation AI, the feedback unit can use the emotion estimation function to select expressions that are easiest for adults to understand and provide feedback. For example, when expressing a child's emotions, the feedback unit can use specific anecdotes to explain. The feedback unit can use the emotion estimation function to select expressions that are easiest for adults to understand and provide feedback. For example, when feeding back the results of the analysis by the generation AI, the feedback unit can use the emotion estimation function to select expressions that are easiest for adults to understand and provide feedback. In this way, by using the emotion estimation function, it is possible to select expressions that are easiest for adults to understand and provide feedback.

[0054] The feedback unit can visually display the feedback content, allowing it to be intuitively understood using graphs and charts. For example, when feeding back the results of an analysis by the generation AI, the feedback unit visually displays the feedback content, allowing it to be intuitively understood using graphs and charts. For example, changes in a child's emotions can be shown in a graph to make it visually easier to understand. The feedback unit visually displays the feedback content, allowing it to be intuitively understood using graphs and charts. For example, when feeding back the results of an analysis by the generation AI, the feedback content can be visually displayed, allowing it to be intuitively understood using graphs and charts. In this way, visually displaying the feedback content makes it easier to understand intuitively.

[0055] The feedback unit can translate the feedback content into multiple languages ​​and provide it in a form that is easy to understand for adults from different language speaking areas. For example, when feeding back the results of an analysis by the generation AI, the feedback unit translates the feedback content into multiple languages ​​and provides it in a form that is easy to understand for adults from different language speaking areas. For example, the feedback unit translates the feedback content into multiple languages, such as English and Chinese, and provides it. The feedback unit translates the feedback content into multiple languages ​​and provides it in a form that is easy to understand for adults from different language speaking areas. For example, when feeding back the results of an analysis by the generation AI, the feedback content is translated into multiple languages ​​and provides it in a form that is easy to understand for adults from different language speaking areas. In this way, translating the feedback content into multiple languages ​​makes it easier for adults from different language speaking areas to understand.

[0056] The feedback unit can display the feedback content as animation or video, and communicate it in a visually easy-to-understand format. For example, when feeding back the results of analysis by the generation AI, the feedback unit can display the feedback content as animation or video, and communicate it in a visually easy-to-understand format. For example, changes in a child's emotions can be shown using animation, making it visually easy to understand. The feedback unit can display the feedback content as animation or video, and communicate it in a visually easy-to-understand format. For example, when feeding back the results of analysis by the generation AI, the feedback content can be displayed as animation or video, and communicate it in a visually easy-to-understand format. In this way, displaying the feedback content as animation or video makes it visually easy to understand.

[0057] The feedback unit can use the emotion estimation function to monitor the adult's emotional reactions in real time and adjust the feedback content based on that reaction. For example, when using the emotion estimation function to feed back the results of the analysis by the generation AI, the feedback unit can monitor the adult's emotional reactions in real time and adjust the feedback content based on that reaction. For example, if the adult feels surprised or anxious, the feedback content can be softened. The feedback unit can use the emotion estimation function to monitor the adult's emotional reactions in real time and adjust the feedback content based on that reaction. For example, when using the emotion estimation function to feed back the results of the analysis by the generation AI, the feedback unit can monitor the adult's emotional reactions in real time and adjust the feedback content based on that reaction. In this way, by monitoring the adult's emotional reactions in real time and adjusting the feedback content based on that reaction, more appropriate feedback can be provided.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The support system can further include a health management unit that monitors the child's health. The health management unit collects biological data such as the child's heart rate and body temperature, and evaluates the child's health based on this data. For example, it can analyze heart rate fluctuations to estimate stress levels. It can also monitor changes in body temperature to detect early signs of poor health. Furthermore, the health management unit can analyze the child's sleep patterns and detect signs of insufficient or excessive sleep. This makes it possible to comprehensively understand the child's health and provide appropriate support.

[0060] The support system can further include a learning management unit that monitors the learning progress of children. The learning management unit records children's learning activities and evaluates their progress. For example, it can analyze which subjects a child is interested in and provide advice to increase their motivation to study. The learning management unit can also evaluate a child's level of understanding and provide supplementary lessons or additional learning resources as needed. Furthermore, the learning management unit can create a customized learning plan that matches the child's learning style and support effective learning. This makes it possible to grasp a child's learning progress comprehensively and provide appropriate support.

[0061] The support system can further include a social skills assessment unit that evaluates a child's social skills. The social skills assessment unit evaluates a child's interpersonal relationships and communication abilities and provides advice to improve those skills. For example, it can analyze a child's behavioral patterns when building relationships with friends and suggest appropriate communication methods. The social skills assessment unit can also observe a child's behavior when participating in group activities and evaluate their cooperation and leadership skills. Furthermore, the social skills assessment unit can analyze how a child cope when stressed and provide advice to improve stress management skills. This makes it possible to comprehensively evaluate a child's social skills and provide appropriate support.

[0062] The support system may further include a creativity evaluation unit that evaluates a child's creativity. The creativity evaluation unit records a child's creative activities and evaluates their creativity. For example, it may analyze a child's drawings or works of art and evaluate their creativity. The creativity evaluation unit may also analyze a child's thought process when coming up with a new idea and provide advice to promote creative thinking. Furthermore, the creativity evaluation unit may observe a child's approach to problem solving and provide advice to improve creative problem-solving skills. This makes it possible to comprehensively evaluate a child's creativity and provide appropriate support.

[0063] The support system can further include a career assessment unit that evaluates a child's future career aspirations. The career assessment unit evaluates the child's future career aspirations based on the child's interests and abilities and suggests an appropriate career path. For example, if a child shows a strong interest in a particular field, it can suggest a job related to that field. The career assessment unit can also evaluate a child's abilities and skills and suggest an appropriate career path based on that. Furthermore, the career assessment unit can support the child in setting future goals and suggest specific steps toward achieving those goals. This makes it possible to comprehensively evaluate a child's future career aspirations and provide appropriate support.

[0064] The support system may further include a relaxation suggestion unit that estimates the child's emotions and suggests appropriate relaxation methods based on those emotions. For example, if the child is feeling stressed, the relaxation suggestion unit may suggest music or meditation methods for relaxation. Also, if the child is feeling anxious, the relaxation suggestion unit may suggest deep breathing or relaxation exercises. Furthermore, the relaxation suggestion unit may provide advice on creating an environment where the child can relax. This makes it possible to suggest appropriate relaxation methods based on the child's emotions and reduce stress and anxiety.

[0065] The support system can further include a learning suggestion unit that estimates the child's emotions and suggests an appropriate learning method based on those emotions. The learning suggestion unit can, for example, suggest a fun way to learn in a field that the child is interested in. The learning suggestion unit can also suggest a learning environment that will help the child improve their concentration if they are lacking in concentration. Furthermore, the learning suggestion unit can suggest a way for the child to study while relaxing if they are feeling stressed. This makes it possible to suggest an appropriate learning method based on the child's emotions and support effective learning.

[0066] The support system can further include a communication suggestion unit that estimates the child's emotions and suggests an appropriate communication method based on the emotions. For example, if the child is feeling anxious, the communication suggestion unit can suggest a communication method that will give the child a sense of security. Also, if the child is feeling angry, the communication suggestion unit can suggest a method for calmly communicating with the child. Furthermore, if the child is feeling happy, the communication suggestion unit can suggest a method for sharing that happiness. This makes it possible to suggest an appropriate communication method based on the child's emotions and support effective dialogue.

[0067] The support system may further include an activity suggestion unit that estimates the child's emotions and suggests appropriate games and activities based on the emotions. For example, if the child is feeling bored, the activity suggestion unit may suggest interesting games and activities. If the child is feeling stressed, the activity suggestion unit may also suggest relaxing activities. If the child has too much energy, the activity suggestion unit may also suggest physical activities. This makes it possible to suggest appropriate games and activities based on the child's emotions and improve the child's mood.

[0068] The support system can further include a nutrition suggestion unit that estimates the child's emotions and suggests appropriate meals and nutrition based on those emotions. For example, if the child is feeling stressed, the nutrition suggestion unit can suggest foods that have a relaxing effect. Also, if the child is feeling tired, the nutrition suggestion unit can suggest foods that will replenish energy. Furthermore, if the child wants to improve their concentration, the nutrition suggestion unit can suggest foods that will support concentration. This makes it possible to suggest appropriate meals and nutrition based on the child's emotions and support the child's health and well-being.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The behavior analysis unit analyzes the child's behavior or statements. For example, the behavior analysis unit observes the child's daily behavior and analyzes their behavioral patterns. The behavior analysis unit can also analyze what the child says in specific situations. For example, the behavior analysis unit analyzes what the child says about events at school and infers the emotions behind them. Step 2: The emotion estimation unit estimates the child's emotion based on the results of the analysis by the behavior analysis unit. For example, the emotion estimation unit estimates the child's emotion using facial expression recognition technology. The emotion estimation unit can also estimate the child's emotion using voice tone analysis technology. For example, the emotion estimation unit analyzes the tone and speed of the child's voice to estimate the emotion. Step 3: The feedback unit feeds back the emotion estimated by the emotion estimation unit to the adult. For example, the feedback unit may convey the emotion to the adult using a text message. The feedback unit may also convey the emotion using a voice message. For example, the feedback unit may explain the child's emotion by voice and convey it to the adult.

[0071] 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0085] 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.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a behavior analysis unit that analyzes the behavior or speech of a child; an emotion estimation unit that estimates the emotion of the child based on the results of the analysis by the behavior analysis unit; a feedback unit that feeds back the emotion estimated by the emotion estimation unit to an adult. A system characterized by:

2. The behavior analysis unit Tracks changes in a child's emotions in real time and updates the analysis results based on those changes 2. The system of claim 1.

3. The behavior analysis unit Simultaneously analyze voice or facial expression data and perform analysis based on multimodal information 2. The system of claim 1.

4. The feedback unit Select expressions that are easiest for adults to understand and provide feedback 2. The system of claim 1.

5. The behavior analysis unit Conduct analyses that reflect cultural differences, taking into account different cultural and regional contexts 2. The system of claim 1.

6. The feedback unit Visually display feedback and make it easy to understand using graphs and charts 2. The system of claim 1.

7. The feedback unit Translate feedback into multiple languages ​​and provide it in a way that is easy for adults from different language backgrounds to understand 2. The system of claim 1.

8. The feedback unit Monitor adults' emotional responses in real time and adjust feedback based on those responses 2. The system of claim 1.

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