System

Abstract

To provide a system that estimates emotions of children and provides appropriate responses according thereto.SOLUTION: A system comprises an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating emotions of children. The estimation unit estimates the emotions of the children on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines a response according to the emotions of the children estimated by the estimation unit. The response unit executes the response determined by the determination unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to properly read a child's emotions and respond accordingly.

[0005] The system according to the embodiment aims to estimate a child's emotions and respond appropriately accordingly. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating an emotion of a child. The estimation unit estimates an emotion of the child based on the emotion estimation information acquired by the acquisition unit. The decision unit decides a response according to the emotion of the child estimated by the estimation unit. The response unit executes the response decided by the decision unit. Effect of the Invention

[0007] The system according to the embodiment can estimate the child's emotions and respond appropriately accordingly. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system in which an agent having an emotion engine supports communication with children. This system acquires information for estimating the child's emotion, estimates the child's emotion based on the acquired information, determines an appropriate response according to the estimated emotion, and executes the response. For example, the agent acquires information such as the child's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. Next, based on the acquired information, the agent estimates the child's emotion. Depending on the estimated emotion, the agent determines an appropriate response. For example, if the child is sad, the agent will say comforting words, if the child is happy, the agent will rejoice with the child, and if the child is angry, the agent will provide a topic to calm the child. Furthermore, the agent can provide a story according to the child's emotion. In this way, the agent having the emotion engine can support communication with children and promote the mental growth of children.

[0029] The communication support system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of the child. The emotion estimation information includes, for example, facial expression data, voice data, and biological data, but is not limited to such examples. For example, the acquisition unit photographs the facial expression of the child with a camera and acquires the facial expression data. The acquisition unit can also record the child's voice with a microphone and acquire the voice data. Furthermore, the acquisition unit can measure the child's heart rate and blood pressure with a sensor and acquire the biological data. The estimation unit estimates the emotion of the child based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion of the child from the facial expression data, for example, using a machine learning algorithm. Furthermore, the estimation unit can estimate the emotion of the child from the voice data. Furthermore, the estimation unit can estimate the emotion of the child from the biological data. The decision unit determines a response according to the emotion of the child estimated by the estimation unit. For example, if the child is sad, the decision unit determines a response to say comforting words. Moreover, the determination unit can determine a response to rejoice together with the child when the child is happy. Furthermore, the determination unit can determine a response to provide a topic to calm the child when the child is angry. The response unit executes the response determined by the determination unit. The response unit, for example, speaks words to comfort the child. Furthermore, the response unit can rejoice together with the child. Furthermore, the response unit can provide a topic to calm the child. In this way, the communication support system according to the embodiment can support the mental growth of the child by estimating the child's emotions and taking an appropriate response.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the child. The emotion estimation information includes, but is not limited to, facial expression data, voice data, and biological data, for example. The acquisition unit, for example, photographs the facial expression of the child with a camera and acquires the facial expression data. Specifically, a high-resolution camera can be used to capture subtle changes in the facial expression of the child. This makes it possible to accurately capture subtle emotional expressions such as smiles, tears, and surprise. The acquisition unit can also record the child's voice with a microphone and acquire voice data. By using a high-sensitivity microphone, it is possible to collect voice features such as the tone, pitch, and rhythm of the child's voice in detail. Furthermore, the acquisition unit can measure the child's heart rate and blood pressure with a sensor and acquire biological data. For example, a wearable device can be used to monitor biological signals such as the child's heart rate, blood pressure, and skin electrodermal activity in real time. This makes it possible to grasp the child's emotional state from multiple angles. The acquisition unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the estimation unit and the determination unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the acquisition unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The estimation unit estimates the child's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the child's emotion from facial expression data using, for example, a machine learning algorithm. Specifically, the estimation unit uses image recognition technology using deep learning to classify emotions such as joy, sadness, anger, and surprise from the child's facial expressions with high accuracy. The estimation unit can also estimate the child's emotion from voice data. Voice recognition technology is used to analyze voice features such as tone, pitch, and rhythm of the voice to estimate the emotional state. Furthermore, the estimation unit can estimate the child's emotion from biometric data. For example, the estimation unit analyzes heart rate fluctuations and changes in skin electrodermal activity to estimate stress and relaxation states. The estimation unit can integrate these different data sources and perform comprehensive emotion estimation. For example, more accurate emotion estimation is possible by combining facial expression data, voice data, and biometric data. Furthermore, the estimation unit can also analyze long-term emotion fluctuations and trends using past data and statistical information. This allows the estimation unit to provide information to support the mental growth of children by grasping long-term emotion fluctuations as well as real-time emotion estimation.

[0032] The determination unit determines a response according to the child's emotions estimated by the estimation unit. For example, if the child is sad, the determination unit determines a response of comforting the child. Specifically, if it is estimated that the child is sad, the determination unit selects the most effective comforting words and actions based on past data and cases. In addition, the determination unit can determine a response to make the child happy together if the child is happy. For example, if it is estimated that the child is happy, the determination unit selects words and actions to share the joy with the child. Furthermore, the determination unit can determine a response to provide a topic to calm the child if the child is angry. For example, if it is estimated that the child is angry, the determination unit selects a topic and actions to calm the child. The determination unit utilizes a decision-making algorithm using AI to quickly and appropriately determine these responses. This allows the determination unit to quickly determine the optimal response according to the child's emotions and support the child's mental growth. Furthermore, the determination unit can incorporate past response results as feedback and continuously improve the accuracy and effectiveness of the response. This allows the determination unit to always provide a highly accurate response based on the latest information and support the child's mental growth.

[0033] The response unit executes the response determined by the determination unit. The response unit, for example, speaks words of comfort to the child. Specifically, if the child is sad, the response unit speaks words of comfort such as "It's okay" in a gentle voice. The response unit can also rejoice with the child. For example, if the child is happy, the response unit shares the joy with the child by saying "Good job!". The response unit can also provide a topic to calm the child. For example, if the child is angry, the response unit provides a topic such as "Let's play your favorite game" to calm the child. The response unit can utilize voice synthesis technology and robot technology to execute these responses. For example, the response unit can speak to the child in a natural voice using voice synthesis technology. The response unit can also play with and talk to the child using robot technology. This allows the response unit to quickly execute an appropriate response to the child and support the child's mental growth. The response unit can also record the results of the response it has executed and use them in the next response. This enables the response department to always provide highly accurate responses based on the latest information and support the psychological development of children.

[0034] The determination unit determines a dialogue in a manner corresponding to the child's emotion as a response corresponding to the child's emotion, and the response unit can conduct a dialogue with the child in a manner corresponding to the child's emotion. For example, the determination unit determines a manner of speaking comforting words when the child is sad. The determination unit can also determine a manner of rejoicing together with the child when the child is happy. Furthermore, the determination unit can also determine a manner of providing a topic to calm the child when the child is angry. The response unit, for example, speaks comforting words to the child. The response unit can also rejoice together with the child. Furthermore, the response unit can provide a topic to calm the child. In this way, by conducting a dialogue corresponding to the child's emotion, it is possible to support the child's mental growth.

[0035] The determination unit determines to provide a story narrative in accordance with the child's emotions as a response in accordance with the child's emotions, and the response unit can provide the child with the story narrative in accordance with the child's emotions. For example, the determination unit determines to provide a comforting story when the child is sad. The determination unit can also determine to provide a fun story when the child is happy. Furthermore, the determination unit can also determine to provide a story to calm the child when the child is angry. The response unit provides, for example, a comforting story to the child. The response unit can also provide a fun story to the child. Furthermore, the response unit can provide a story to calm the child. In this way, by providing a story in accordance with the child's emotions, it is possible to support the child's mental growth.

[0036] The acquisition unit can acquire one or more pieces of information among the child's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain waves, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit captures the child's facial expression with a camera and acquires facial expression data. The acquisition unit can also detect the child's movement with a sensor and acquire movement data. The acquisition unit can also record the child's voice with a microphone and acquire voice data. For example, the acquisition unit measures the child's heart rate with a sensor and acquires heart rate data. The acquisition unit can also measure the child's blood pressure with a sensor and acquire blood pressure data. The acquisition unit can also capture the child's pupil response with a camera and acquire pupil data. For example, the acquisition unit measures the child's brain waves with a sensor and acquires brain wave data. The acquisition unit can also measure the child's body temperature with a sensor and acquire body temperature data. The acquisition unit can also measure the child's breathing with a sensor and acquire breathing data. For example, the acquisition unit analyzes the child's posted content and acquires text data. The acquisition unit can also analyze the content posted by the child and acquire image data, thereby acquiring a variety of information and making it possible to more accurately estimate the child's emotions.

[0037] The acquisition unit can analyze the child's past emotion data and select an appropriate acquisition method. For example, the acquisition unit analyzes what emotions the child has shown in a particular situation in the past and optimizes the acquisition method of emotion estimation information in a similar situation. The acquisition unit can also specify from the child's past emotion data that emotions are likely to change during a particular time period and acquire information focusing on that time period. Furthermore, the acquisition unit can detect a precursor to the appearance of a particular emotion based on the child's past emotion data and acquire information when the precursor appears. In this way, the optimal information acquisition method can be selected by analyzing the past emotion data.

[0038] When acquiring emotion estimation information, the acquisition unit can perform filtering based on the child's current activity and environment. For example, when the child is playing, the acquisition unit preferentially acquires information related to the content of the play. In addition, when the child is studying, the acquisition unit can also acquire information related to the progress and concentration of the study. Furthermore, when the child is outside, the acquisition unit can also acquire surrounding environmental sound and weather information and use it for emotion estimation. In this way, by filtering information based on the current activity and environment, more relevant information can be acquired.

[0039] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information by taking into consideration the geographical location information of the child. For example, when the child is in a park, the acquisition unit acquires the environmental sounds of the park and the movements of people around the child. In addition, when the child is at school, the acquisition unit can also acquire the atmosphere of the classroom and the progress of the class. Furthermore, when the child is at home, the acquisition unit can also acquire sounds in the home and the movements of the family. In this way, by taking into consideration the geographical location information, highly relevant information can be preferentially acquired.

[0040] When acquiring the emotion estimation information, the acquisition unit can analyze the child's social media activity and acquire related information. For example, the acquisition unit analyzes the content posted by the child on social media and uses it for emotion estimation. The acquisition unit can also acquire comments and reactions received by the child on social media and estimate changes in emotion. Furthermore, the acquisition unit can analyze the content of accounts followed by the child on social media and acquire information related to emotion estimation. In this way, it is possible to acquire information related to emotion estimation by analyzing social media activity.

[0041] The estimation unit can adjust the level of detail of the estimation based on the importance of the acquired emotion estimation information when estimating emotions. For example, the estimation unit preferentially analyzes information with high importance (e.g., heart rate and pupil response) to perform detailed emotion estimation. The estimation unit can also simply analyze information with low importance (e.g., posted content) to grasp the overall emotional tendency. Furthermore, the estimation unit can appropriately analyze information with medium importance (e.g., facial expressions and actions) to improve the accuracy of emotion estimation. This enables efficient emotion estimation by adjusting the level of detail of the estimation based on the importance of the information.

[0042] The estimation unit can apply different estimation algorithms depending on the child's attribute information when estimating emotions. For example, the estimation unit applies an emotion estimation algorithm according to age and takes into account age-specific emotional expressions. The estimation unit can also apply an emotion estimation algorithm according to gender and take into account gender-specific emotional expressions. Furthermore, the estimation unit can apply an emotion estimation algorithm according to cultural background and take into account culture-specific emotional expressions. In this way, the accuracy of emotion estimation can be improved by applying an estimation algorithm according to attribute information.

[0043] The estimation unit can determine the priority of estimation based on the time of submission of acquired information when estimating emotions. For example, the estimation unit preferentially analyzes the latest information and performs real-time emotion estimation. The estimation unit can also perform current emotion estimation while referring to past information. Furthermore, the estimation unit can also preferentially analyze information acquired during a specific time period and estimate emotion changes during that time period. This enables real-time emotion estimation by determining the priority based on the time of submission of information.

[0044] The estimation unit can improve the accuracy of estimation by referring to related past data when estimating emotions. For example, the estimation unit improves the accuracy of the current emotion estimation by referring to past emotion data. The estimation unit can also analyze past emotion change patterns and reflect them in the current emotion estimation. Furthermore, the estimation unit can compare the past emotion data with the current emotion data to improve the reliability of the estimation result. In this way, the accuracy of emotion estimation can be improved by referring to past data.

[0045] When determining how to respond, the determination unit can analyze the child's past reaction data and select an appropriate method of response. For example, if the child has been calmed down in the past by comforting words, the determination unit selects a similar response. The determination unit can also select a similar response if the child has been happy in the past by sharing joy. Furthermore, the determination unit can also select a similar response if the child has been calmed down in the past by providing a topic of conversation. In this way, the optimal method of response can be selected by analyzing past reaction data.

[0046] When determining the response, the decision unit can customize the response means based on the child's current situation. For example, when the child is at school, the decision unit selects a response suitable for the school environment. Furthermore, when the child is at home, the decision unit can also select a response suitable for the home environment. Furthermore, when the child is out, the decision unit can also select a response suitable for the environment at the outing. In this way, an appropriate response can be taken by customizing the response means based on the current situation. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0047] When determining the response, the decision unit can select the optimal response method by taking into consideration the geographical location information of the child. For example, when the child is in a park, the decision unit selects a response suitable for the park environment. Furthermore, when the child is at school, the decision unit can also select a response suitable for the school environment. Furthermore, when the child is at home, the decision unit can also select a response suitable for the home environment. In this way, the optimal response method can be selected by taking into consideration the geographical location information. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0048] When determining how to respond, the decision unit can analyze the child's social media activity and suggest a means of response. For example, if the child posts on social media that he / she is sad, the decision unit can suggest a comforting response. In addition, if the child posts on social media that he / she is happy, the decision unit can also suggest sharing the joy. Furthermore, if the child posts on social media that he / she is angry, the decision unit can also suggest a calming response. In this way, by analyzing social media activity, appropriate means of response can be suggested. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0049] The response unit can select the optimal execution method by referring to the child's past reaction data when executing the response. For example, if the child has been calmed down by comforting words in the past, the response unit selects a similar execution method. The response unit can also select a similar execution method if the child has been happy in the past by sharing joy. Furthermore, the response unit can also select a similar execution method if the child has been calmed down by the provision of a topic in the past. In this way, the optimal execution method can be selected by referring to the past reaction data. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0050] The response unit can customize the means of response based on the child's current situation when performing the response. For example, when the child is at school, the response unit selects an execution method suitable for the school environment. In addition, when the child is at home, the response unit can also select an execution method suitable for the home environment. Furthermore, when the child is out, the response unit can also select an execution method suitable for the environment at the outing location. In this way, by customizing the means of response based on the current situation, an appropriate response can be performed. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0051] The response unit can select the optimal execution method in consideration of the child's geographical location information when executing the response. For example, when the child is in a park, the response unit selects an execution method suitable for the park environment. Furthermore, when the child is at school, the response unit can also select an execution method suitable for the school environment. Furthermore, when the child is at home, the response unit can also select an execution method suitable for the home environment. In this way, the optimal execution method can be selected by considering the geographical location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0052] The response unit can analyze the child's social media activity and suggest a response measure when the response is executed. For example, when the child posts on social media that he / she is sad, the response unit can suggest a comforting response. In addition, when the child posts on social media that he / she is happy, the response unit can also suggest sharing the joy. Furthermore, when the child posts on social media that he / she is angry, the response unit can also suggest a calming response. In this way, it is possible to suggest an appropriate response measure by analyzing the social media activity. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When acquiring the information for estimating the child's emotion, the acquisition unit can analyze environmental sounds and background sounds around the child. For example, when the child is in a park, natural sounds such as birds chirping and wind sounds are acquired, and the influence of these sounds on the child's emotion is considered. In addition, when the child is at school, classroom noise and the progress of lessons can be acquired, and the influence of these sounds on the child's concentration and stress level can be analyzed. Furthermore, when the child is at home, sounds in the home and conversations with family members can be acquired, and the influence of these sounds on the child's emotion can be considered. In this way, the accuracy of estimating the child's emotion can be improved by taking into account the surrounding environmental sounds.

[0055] The estimation unit can refer to the child's past behavioral patterns when estimating the child's emotions. For example, it can analyze what emotions the child showed in a specific situation in the past, and use this to help estimate emotions in similar situations. It can also analyze what emotions the child showed in a specific time period in the past, and focus emotion estimation on that time period. It can also analyze what emotions the child showed toward a specific event or occurrence in the past, and use this to help estimate emotions toward similar events or occurrences. In this way, it is possible to improve the accuracy of emotion estimation by referring to past behavioral patterns.

[0056] The determination unit can take into consideration the child's preferences and interests when determining a response according to the child's emotions. For example, if the child likes a particular character or story, the determination unit can determine a response that uses the character or story. Also, if the child likes a particular activity or play, the determination unit can determine a response that incorporates the activity or play. Furthermore, if the child likes particular music or video, the determination unit can determine a response that uses the music or video. In this way, by taking the child's preferences and interests into consideration, a more effective response can be provided.

[0057] The response unit can take the child's physical condition into consideration when taking a response according to the child's emotions. For example, if the child is tired, a response that allows the child to relax can be taken. If the child is energetic, an active response can be taken. Furthermore, if the child is not feeling well, a response that takes the child's physical condition into consideration can be taken. In this way, a more appropriate response can be taken by taking the child's physical condition into consideration.

[0058] The acquisition unit can analyze the child's social media activities when acquiring the information for emotion estimation of the child. For example, the content posted by the child on social media can be analyzed to help with emotion estimation. In addition, the acquisition unit can acquire comments and reactions received by the child on social media to estimate changes in emotion. Furthermore, the acquisition unit can analyze the content of accounts followed by the child on social media to acquire information related to emotion estimation. In this way, information related to emotion estimation can be acquired by analyzing social media activities.

[0059] When determining how to respond according to the child's emotions, the decision unit can select an appropriate response method by analyzing the child's past reaction data. For example, if the child was calmed down in the past by comforting words, a similar response can be selected. Also, if the child was happy in the past by sharing joy, a similar response can be selected. Furthermore, if the child was calmed down in the past by being offered a topic of conversation, a similar response can be selected. In this way, the optimal response method can be selected by analyzing past reaction data.

[0060] When executing a response according to the child's emotions, the response unit can customize the means of response based on the child's current situation. For example, when the child is at school, an execution method suitable for the school environment is selected. When the child is at home, an execution method suitable for the home environment can also be selected. Furthermore, when the child is out, an execution method suitable for the environment at the child's home can also be selected. In this way, an appropriate response can be taken by customizing the means of response based on the current situation.

[0061] When executing a response according to the child's emotions, the response unit can analyze the child's social media activity and suggest a response. For example, if the child posts on social media that he or she is sad, a comforting response can be suggested. Also, if the child posts on social media that he or she is happy, it can also suggest sharing the joy. Furthermore, if the child posts on social media that he or she is angry, it can also suggest a calming response. In this way, appropriate response measures can be suggested by analyzing social media activity.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a child. The emotion estimation information includes, for example, facial expression data, voice data, and biometric data. The acquisition unit photographs the child's facial expression with a camera to acquire the facial expression data. It can also record the child's voice with a microphone to acquire the voice data. It can also measure the child's heart rate and blood pressure with a sensor to acquire biometric data. Step 2: The estimation unit estimates the child's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the child's emotion from facial expression data, voice data, and biometric data using a machine learning algorithm. Step 3: The determination unit determines a response according to the child's emotion estimated by the estimation unit. For example, if the child is sad, the determination unit determines a response of saying comforting words, if the child is happy, the determination unit determines a response of sharing the child's joy, and if the child is angry, the determination unit determines a response of providing a topic to calm the child. Step 4: The response unit executes the response decided by the decision unit, such as speaking comforting words to the child, sharing joy with the child, or providing topics to calm the child.

[0064] (Example 2) The system according to the embodiment of the present invention is a system in which an agent having an emotion engine supports communication with children. This system acquires information for estimating the child's emotion, estimates the child's emotion based on the acquired information, determines an appropriate response according to the estimated emotion, and executes the response. For example, the agent acquires information such as the child's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content. Next, based on the acquired information, the agent estimates the child's emotion. Depending on the estimated emotion, the agent determines an appropriate response. For example, if the child is sad, the agent will say comforting words, if the child is happy, the agent will rejoice with the child, and if the child is angry, the agent will provide a topic to calm the child. Furthermore, the agent can provide a story according to the child's emotion. In this way, the agent having the emotion engine can support communication with children and promote the mental growth of children.

[0065] The communication support system according to the embodiment includes an acquisition unit, an estimation unit, a decision unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of the child. The emotion estimation information includes, for example, facial expression data, voice data, and biological data, but is not limited to such examples. For example, the acquisition unit photographs the facial expression of the child with a camera and acquires the facial expression data. The acquisition unit can also record the child's voice with a microphone and acquire the voice data. Furthermore, the acquisition unit can measure the child's heart rate and blood pressure with a sensor and acquire the biological data. The estimation unit estimates the emotion of the child based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the emotion of the child from the facial expression data, for example, using a machine learning algorithm. Furthermore, the estimation unit can estimate the emotion of the child from the voice data. Furthermore, the estimation unit can estimate the emotion of the child from the biological data. The decision unit determines a response according to the emotion of the child estimated by the estimation unit. For example, if the child is sad, the decision unit determines a response to say comforting words. Moreover, the determination unit can determine a response to rejoice together with the child when the child is happy. Furthermore, the determination unit can determine a response to provide a topic to calm the child when the child is angry. The response unit executes the response determined by the determination unit. The response unit, for example, speaks words to comfort the child. Furthermore, the response unit can rejoice together with the child. Furthermore, the response unit can provide a topic to calm the child. In this way, the communication support system according to the embodiment can support the mental growth of the child by estimating the child's emotions and taking an appropriate response.

[0066] The acquisition unit acquires emotion estimation information for estimating the emotion of the child. The emotion estimation information includes, but is not limited to, facial expression data, voice data, and biological data, for example. The acquisition unit, for example, photographs the facial expression of the child with a camera and acquires the facial expression data. Specifically, a high-resolution camera can be used to capture subtle changes in the facial expression of the child. This makes it possible to accurately capture subtle emotional expressions such as smiles, tears, and surprise. The acquisition unit can also record the child's voice with a microphone and acquire voice data. By using a high-sensitivity microphone, it is possible to collect voice features such as the tone, pitch, and rhythm of the child's voice in detail. Furthermore, the acquisition unit can measure the child's heart rate and blood pressure with a sensor and acquire biological data. For example, a wearable device can be used to monitor biological signals such as the child's heart rate, blood pressure, and skin electrodermal activity in real time. This makes it possible to grasp the child's emotional state from multiple angles. The acquisition unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the estimation unit and the determination unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the acquisition unit to collect data efficiently and effectively, improving the performance of the entire system.

[0067] The estimation unit estimates the child's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the child's emotion from facial expression data using, for example, a machine learning algorithm. Specifically, the estimation unit uses image recognition technology using deep learning to classify emotions such as joy, sadness, anger, and surprise from the child's facial expressions with high accuracy. The estimation unit can also estimate the child's emotion from voice data. Voice recognition technology is used to analyze voice features such as tone, pitch, and rhythm of the voice to estimate the emotional state. Furthermore, the estimation unit can estimate the child's emotion from biometric data. For example, the estimation unit analyzes heart rate fluctuations and changes in skin electrodermal activity to estimate stress and relaxation states. The estimation unit can integrate these different data sources and perform comprehensive emotion estimation. For example, more accurate emotion estimation is possible by combining facial expression data, voice data, and biometric data. Furthermore, the estimation unit can also analyze long-term emotion fluctuations and trends using past data and statistical information. This allows the estimation unit to provide information to support the mental growth of children by grasping long-term emotion fluctuations as well as real-time emotion estimation.

[0068] The determination unit determines a response according to the child's emotions estimated by the estimation unit. For example, if the child is sad, the determination unit determines a response of comforting the child. Specifically, if it is estimated that the child is sad, the determination unit selects the most effective comforting words and actions based on past data and cases. In addition, the determination unit can determine a response to make the child happy together if the child is happy. For example, if it is estimated that the child is happy, the determination unit selects words and actions to share the joy with the child. Furthermore, the determination unit can determine a response to provide a topic to calm the child if the child is angry. For example, if it is estimated that the child is angry, the determination unit selects a topic and actions to calm the child. The determination unit utilizes a decision-making algorithm using AI to quickly and appropriately determine these responses. This allows the determination unit to quickly determine the optimal response according to the child's emotions and support the child's mental growth. Furthermore, the determination unit can incorporate past response results as feedback and continuously improve the accuracy and effectiveness of the response. This allows the determination unit to always provide a highly accurate response based on the latest information and support the child's mental growth.

[0069] The response unit executes the response determined by the determination unit. The response unit, for example, speaks words of comfort to the child. Specifically, if the child is sad, the response unit speaks words of comfort such as "It's okay" in a gentle voice. The response unit can also rejoice with the child. For example, if the child is happy, the response unit shares the joy with the child by saying "Good job!". The response unit can also provide a topic to calm the child. For example, if the child is angry, the response unit provides a topic such as "Let's play your favorite game" to calm the child. The response unit can utilize voice synthesis technology and robot technology to execute these responses. For example, the response unit can speak to the child in a natural voice using voice synthesis technology. The response unit can also play with and talk to the child using robot technology. This allows the response unit to quickly execute an appropriate response to the child and support the child's mental growth. The response unit can also record the results of the response it has executed and use them in the next response. This enables the response department to always provide highly accurate responses based on the latest information and support the psychological development of children.

[0070] The determination unit determines a dialogue in a manner corresponding to the child's emotion as a response corresponding to the child's emotion, and the response unit can conduct a dialogue with the child in a manner corresponding to the child's emotion. For example, the determination unit determines a manner of speaking comforting words when the child is sad. The determination unit can also determine a manner of rejoicing together with the child when the child is happy. Furthermore, the determination unit can also determine a manner of providing a topic to calm the child when the child is angry. The response unit, for example, speaks comforting words to the child. The response unit can also rejoice together with the child. Furthermore, the response unit can provide a topic to calm the child. In this way, by conducting a dialogue corresponding to the child's emotion, it is possible to support the child's mental growth.

[0071] The determination unit determines to provide a story narrative in accordance with the child's emotions as a response in accordance with the child's emotions, and the response unit can provide the child with the story narrative in accordance with the child's emotions. For example, the determination unit determines to provide a comforting story when the child is sad. The determination unit can also determine to provide a fun story when the child is happy. Furthermore, the determination unit can also determine to provide a story to calm the child when the child is angry. The response unit provides, for example, a comforting story to the child. The response unit can also provide a fun story to the child. Furthermore, the response unit can provide a story to calm the child. In this way, by providing a story in accordance with the child's emotions, it is possible to support the child's mental growth.

[0072] The acquisition unit can acquire one or more pieces of information among the child's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain waves, body temperature, breathing, and posted content as emotion estimation information. For example, the acquisition unit captures the child's facial expression with a camera and acquires facial expression data. The acquisition unit can also detect the child's movement with a sensor and acquire movement data. The acquisition unit can also record the child's voice with a microphone and acquire voice data. For example, the acquisition unit measures the child's heart rate with a sensor and acquires heart rate data. The acquisition unit can also measure the child's blood pressure with a sensor and acquire blood pressure data. The acquisition unit can also capture the child's pupil response with a camera and acquire pupil data. For example, the acquisition unit measures the child's brain waves with a sensor and acquires brain wave data. The acquisition unit can also measure the child's body temperature with a sensor and acquire body temperature data. The acquisition unit can also measure the child's breathing with a sensor and acquire breathing data. For example, the acquisition unit analyzes the child's posted content and acquires text data. The acquisition unit can also analyze the content posted by the child and acquire image data, thereby acquiring a variety of information and making it possible to more accurately estimate the child's emotions.

[0073] The acquisition unit can estimate the child's emotion and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the child. For example, when the child is excited, the acquisition unit increases the frequency of acquiring the emotion estimation information and tracks changes in emotion in real time. Also, when the child is relaxed, the acquisition unit can reduce the frequency of acquiring the emotion estimation information and acquire the minimum amount of information required. Furthermore, when the child is stressed, the acquisition unit can adjust the timing of acquiring the emotion estimation information and preferentially acquire information for identifying the cause of stress. In this way, by adjusting the timing of acquiring information according to the child's emotion, more appropriate information can be acquired. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] The acquisition unit can analyze the child's past emotion data and select an appropriate acquisition method. For example, the acquisition unit analyzes what emotions the child has shown in a particular situation in the past and optimizes the acquisition method of emotion estimation information in a similar situation. The acquisition unit can also specify from the child's past emotion data that emotions are likely to change during a particular time period and acquire information focusing on that time period. Furthermore, the acquisition unit can detect a precursor to the appearance of a particular emotion based on the child's past emotion data and acquire information when the precursor appears. In this way, the optimal information acquisition method can be selected by analyzing the past emotion data.

[0075] When acquiring emotion estimation information, the acquisition unit can perform filtering based on the child's current activity and environment. For example, when the child is playing, the acquisition unit preferentially acquires information related to the content of the play. In addition, when the child is studying, the acquisition unit can also acquire information related to the progress and concentration of the study. Furthermore, when the child is outside, the acquisition unit can also acquire surrounding environmental sound and weather information and use it for emotion estimation. In this way, by filtering information based on the current activity and environment, more relevant information can be acquired.

[0076] The acquisition unit can estimate the child's emotion and determine the priority of emotion estimation information to be acquired based on the estimated emotion of the child. For example, when the child is sad, the acquisition unit preferentially acquires facial expression and voice information. Also, when the child is happy, the acquisition unit can preferentially acquire movement and heart rate information. Furthermore, when the child is angry, the acquisition unit can preferentially acquire pupil response and breathing information. In this way, important information can be preferentially acquired by determining the priority of information based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information by taking into consideration the geographical location information of the child. For example, when the child is in a park, the acquisition unit acquires the environmental sounds of the park and the movements of people around the child. In addition, when the child is at school, the acquisition unit can also acquire the atmosphere of the classroom and the progress of the class. Furthermore, when the child is at home, the acquisition unit can also acquire sounds in the home and the movements of the family. In this way, by taking into consideration the geographical location information, highly relevant information can be preferentially acquired.

[0078] When acquiring the emotion estimation information, the acquisition unit can analyze the child's social media activity and acquire related information. For example, the acquisition unit analyzes the content posted by the child on social media and uses it for emotion estimation. The acquisition unit can also acquire comments and reactions received by the child on social media and estimate changes in emotion. Furthermore, the acquisition unit can analyze the content of accounts followed by the child on social media and acquire information related to emotion estimation. In this way, it is possible to acquire information related to emotion estimation by analyzing social media activity.

[0079] The estimation unit can estimate the child's emotion and adjust the emotion estimation algorithm based on the estimated emotion of the child. For example, when the child is relaxed, the estimation unit sets the sensitivity of the emotion estimation algorithm low and ignores minute emotion changes. Also, when the child is excited, the estimation unit can set the sensitivity of the emotion estimation algorithm high and quickly detect emotion changes. Furthermore, when the child is stressed, the estimation unit can adjust the emotion estimation algorithm and prioritize analysis of information for identifying the cause of stress. In this way, the emotion estimation algorithm can be adjusted to improve the accuracy of emotion estimation. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The estimation unit can adjust the level of detail of the estimation based on the importance of the acquired emotion estimation information when estimating emotions. For example, the estimation unit preferentially analyzes information with high importance (e.g., heart rate and pupil response) to perform detailed emotion estimation. The estimation unit can also simply analyze information with low importance (e.g., posted content) to grasp the overall emotional tendency. Furthermore, the estimation unit can appropriately analyze information with medium importance (e.g., facial expressions and actions) to improve the accuracy of emotion estimation. This enables efficient emotion estimation by adjusting the level of detail of the estimation based on the importance of the information.

[0081] The estimation unit can apply different estimation algorithms depending on the child's attribute information when estimating emotions. For example, the estimation unit applies an emotion estimation algorithm according to age and takes into account age-specific emotional expressions. The estimation unit can also apply an emotion estimation algorithm according to gender and take into account gender-specific emotional expressions. Furthermore, the estimation unit can apply an emotion estimation algorithm according to cultural background and take into account culture-specific emotional expressions. In this way, the accuracy of emotion estimation can be improved by applying an estimation algorithm according to attribute information.

[0082] The estimation unit can estimate the child's emotion and adjust the display method of the estimation result based on the estimated emotion of the child. For example, when the child is relaxed, the estimation unit displays a detailed emotion estimation result. Also, when the child is excited, the estimation unit can display a concise emotion estimation result. Furthermore, when the child is stressed, the estimation unit can display an emotion estimation result including the cause of stress and measures to deal with it. In this way, by adjusting the display method based on the emotion, it is possible to provide an appropriate estimation result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The estimation unit can determine the priority of estimation based on the time of submission of acquired information when estimating emotions. For example, the estimation unit preferentially analyzes the latest information and performs real-time emotion estimation. The estimation unit can also perform current emotion estimation while referring to past information. Furthermore, the estimation unit can also preferentially analyze information acquired during a specific time period and estimate emotion changes during that time period. This enables real-time emotion estimation by determining the priority based on the time of submission of information.

[0084] The estimation unit can improve the accuracy of estimation by referring to related past data when estimating emotions. For example, the estimation unit improves the accuracy of the current emotion estimation by referring to past emotion data. The estimation unit can also analyze past emotion change patterns and reflect them in the current emotion estimation. Furthermore, the estimation unit can compare the past emotion data with the current emotion data to improve the reliability of the estimation result. In this way, the accuracy of emotion estimation can be improved by referring to past data.

[0085] The determination unit can estimate the child's emotion and adjust the response determination method based on the estimated child's emotion. For example, when the child is sad, the determination unit determines the response to offer comforting words. Also, when the child is happy, the determination unit can determine the response to share the child's joy. Furthermore, when the child is angry, the determination unit can determine the response to provide a topic to calm the child. In this way, an appropriate response can be made by adjusting the response determination method based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0086] When determining how to respond, the determination unit can analyze the child's past reaction data and select an appropriate method of response. For example, if the child has been calmed down in the past by comforting words, the determination unit selects a similar response. The determination unit can also select a similar response if the child has been happy in the past by sharing joy. Furthermore, the determination unit can also select a similar response if the child has been calmed down in the past by providing a topic of conversation. In this way, the optimal method of response can be selected by analyzing past reaction data.

[0087] When determining the response, the decision unit can customize the response means based on the child's current situation. For example, when the child is at school, the decision unit selects a response suitable for the school environment. Furthermore, when the child is at home, the decision unit can also select a response suitable for the home environment. Furthermore, when the child is out, the decision unit can also select a response suitable for the environment at the outing. In this way, an appropriate response can be taken by customizing the response means based on the current situation. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0088] The determination unit can estimate the child's emotion and determine the priority of the response based on the estimated emotion of the child. For example, when the child is sad, the determination unit can prioritize comforting the child. Also, when the child is happy, the determination unit can prioritize sharing the joy. Furthermore, when the child is angry, the determination unit can prioritize calming the child. In this way, by determining the priority of the response based on the emotion, important responses can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0089] When determining the response, the decision unit can select the optimal response method by taking into consideration the geographical location information of the child. For example, when the child is in a park, the decision unit selects a response suitable for the park environment. Furthermore, when the child is at school, the decision unit can also select a response suitable for the school environment. Furthermore, when the child is at home, the decision unit can also select a response suitable for the home environment. In this way, the optimal response method can be selected by taking into consideration the geographical location information. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] When determining how to respond, the decision unit can analyze the child's social media activity and suggest a means of response. For example, if the child posts on social media that he / she is sad, the decision unit can suggest a comforting response. In addition, if the child posts on social media that he / she is happy, the decision unit can also suggest sharing the joy. Furthermore, if the child posts on social media that he / she is angry, the decision unit can also suggest a calming response. In this way, by analyzing social media activity, appropriate means of response can be suggested. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The response unit can estimate the child's emotion and adjust the response execution method based on the estimated emotion of the child. For example, if the child is sad, the response unit can speak comforting words in a gentle voice. If the child is happy, the response unit can also speak happy words together with the child in a cheerful voice. If the child is angry, the response unit can also provide a topic of conversation in a calm voice. This allows an appropriate response to be made by adjusting the execution method based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The response unit can select the optimal execution method by referring to the child's past reaction data when executing the response. For example, if the child has been calmed down by comforting words in the past, the response unit selects a similar execution method. The response unit can also select a similar execution method if the child has been happy in the past by sharing joy. Furthermore, the response unit can also select a similar execution method if the child has been calmed down by the provision of a topic in the past. In this way, the optimal execution method can be selected by referring to the past reaction data. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The response unit can customize the means of response based on the child's current situation when performing the response. For example, when the child is at school, the response unit selects an execution method suitable for the school environment. In addition, when the child is at home, the response unit can also select an execution method suitable for the home environment. Furthermore, when the child is out, the response unit can also select an execution method suitable for the environment at the outing location. In this way, by customizing the means of response based on the current situation, an appropriate response can be performed. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The response unit can estimate the child's emotion and adjust the execution order of responses based on the estimated child's emotion. For example, if the child is sad, the response unit can prioritize comforting the child. Also, if the child is happy, the response unit can prioritize sharing the child's joy. Furthermore, if the child is angry, the response unit can prioritize calming the child. In this way, important responses can be prioritized by adjusting the execution order based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0095] The response unit can select the optimal execution method in consideration of the child's geographical location information when executing the response. For example, when the child is in a park, the response unit selects an execution method suitable for the park environment. Furthermore, when the child is at school, the response unit can also select an execution method suitable for the school environment. Furthermore, when the child is at home, the response unit can also select an execution method suitable for the home environment. In this way, the optimal execution method can be selected by considering the geographical location information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] The response unit can analyze the child's social media activity and suggest a response measure when the response is executed. For example, when the child posts on social media that he / she is sad, the response unit can suggest a comforting response. In addition, when the child posts on social media that he / she is happy, the response unit can also suggest sharing the joy. Furthermore, when the child posts on social media that he / she is angry, the response unit can also suggest a calming response. In this way, it is possible to suggest an appropriate response measure by analyzing the social media activity. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] When acquiring the information for estimating the child's emotion, the acquisition unit can analyze environmental sounds and background sounds around the child. For example, when the child is in a park, natural sounds such as birds chirping and wind sounds are acquired, and the influence of these sounds on the child's emotion is considered. In addition, when the child is at school, classroom noise and the progress of lessons can be acquired, and the influence of these sounds on the child's concentration and stress level can be analyzed. Furthermore, when the child is at home, sounds in the home and conversations with family members can be acquired, and the influence of these sounds on the child's emotion can be considered. In this way, the accuracy of estimating the child's emotion can be improved by taking into account the surrounding environmental sounds.

[0099] The estimation unit can refer to the child's past behavioral patterns when estimating the child's emotions. For example, it can analyze what emotions the child showed in a specific situation in the past, and use this to help estimate emotions in similar situations. It can also analyze what emotions the child showed in a specific time period in the past, and focus emotion estimation on that time period. It can also analyze what emotions the child showed toward a specific event or occurrence in the past, and use this to help estimate emotions toward similar events or occurrences. In this way, it is possible to improve the accuracy of emotion estimation by referring to past behavioral patterns.

[0100] The determination unit can take into consideration the child's preferences and interests when determining a response according to the child's emotions. For example, if the child likes a particular character or story, the determination unit can determine a response that uses the character or story. Also, if the child likes a particular activity or play, the determination unit can determine a response that incorporates the activity or play. Furthermore, if the child likes particular music or video, the determination unit can determine a response that uses the music or video. In this way, by taking the child's preferences and interests into consideration, a more effective response can be provided.

[0101] The response unit can take the child's physical condition into consideration when taking a response according to the child's emotions. For example, if the child is tired, a response that allows the child to relax can be taken. If the child is energetic, an active response can be taken. Furthermore, if the child is not feeling well, a response that takes the child's physical condition into consideration can be taken. In this way, a more appropriate response can be taken by taking the child's physical condition into consideration.

[0102] The acquisition unit can analyze the child's social media activities when acquiring the information for emotion estimation of the child. For example, the content posted by the child on social media can be analyzed to help with emotion estimation. In addition, the acquisition unit can acquire comments and reactions received by the child on social media to estimate changes in emotion. Furthermore, the acquisition unit can analyze the content of accounts followed by the child on social media to acquire information related to emotion estimation. In this way, information related to emotion estimation can be acquired by analyzing social media activities.

[0103] When estimating the child's emotion, the estimation unit can adjust the emotion estimation algorithm based on the child's emotion. For example, when the child is relaxed, the sensitivity of the emotion estimation algorithm can be set low to ignore subtle emotion changes. Also, when the child is excited, the sensitivity of the emotion estimation algorithm can be set high to quickly detect emotion changes. Furthermore, when the child is stressed, the emotion estimation algorithm can be adjusted to prioritize analysis of information for identifying the cause of stress. In this way, the emotion estimation algorithm can be adjusted to improve the accuracy of emotion estimation.

[0104] When determining how to respond according to the child's emotions, the decision unit can select an appropriate response method by analyzing the child's past reaction data. For example, if the child was calmed down in the past by comforting words, a similar response can be selected. Also, if the child was happy in the past by sharing joy, a similar response can be selected. Furthermore, if the child was calmed down in the past by being offered a topic of conversation, a similar response can be selected. In this way, the optimal response method can be selected by analyzing past reaction data.

[0105] When executing a response according to the child's emotions, the response unit can customize the means of response based on the child's current situation. For example, when the child is at school, an execution method suitable for the school environment is selected. When the child is at home, an execution method suitable for the home environment can also be selected. Furthermore, when the child is out, an execution method suitable for the environment at the child's home can also be selected. In this way, an appropriate response can be taken by customizing the means of response based on the current situation.

[0106] When executing responses according to the child's emotions, the response unit can adjust the execution order of responses based on the child's emotions. For example, if the child is sad, comforting the child is given top priority. Also, if the child is happy, sharing the child's joy can be given top priority. Furthermore, if the child is angry, calming the child can be given top priority. Thus, by adjusting the execution order based on emotions, important responses can be given priority.

[0107] When executing a response according to the child's emotions, the response unit can analyze the child's social media activity and suggest a response. For example, if the child posts on social media that he or she is sad, a comforting response can be suggested. Also, if the child posts on social media that he or she is happy, it can also suggest sharing the joy. Furthermore, if the child posts on social media that he or she is angry, it can also suggest a calming response. In this way, appropriate response measures can be suggested by analyzing social media activity.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a child. The emotion estimation information includes, for example, facial expression data, voice data, and biometric data. The acquisition unit photographs the child's facial expression with a camera to acquire the facial expression data. It can also record the child's voice with a microphone to acquire the voice data. It can also measure the child's heart rate and blood pressure with a sensor to acquire biometric data. Step 2: The estimation unit estimates the child's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit estimates the child's emotion from facial expression data, voice data, and biometric data using a machine learning algorithm. Step 3: The determination unit determines a response according to the child's emotion estimated by the estimation unit. For example, if the child is sad, the determination unit determines a response of saying comforting words, if the child is happy, the determination unit determines a response of sharing the child's joy, and if the child is angry, the determination unit determines a response of providing a topic to calm the child. Step 4: The response unit executes the response decided by the decision unit, such as speaking comforting words to the child, sharing joy with the child, or providing topics to calm the child.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the acquisition unit can acquire the child's facial expression and voice using the camera 42 and microphone 38B of the smart device 14. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the child's emotion based on the acquired facial expression data and voice data. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. For example, the response unit is realized by the control unit 46A of the smart device 14, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the acquisition unit can acquire the child's facial expression and voice using the camera 42 and microphone 238 of the smart glasses 214. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the child's emotion based on the acquired facial expression data and voice data. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. For example, the response unit is realized by the control unit 46A of the smart glasses 214, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the acquisition unit can acquire the child's facial expression and voice using the camera 42 and microphone 238 of the headset type terminal 314. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the child's emotion based on the acquired facial expression data and voice data. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. For example, the response unit is realized by the control unit 46A of the headset type terminal 314, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0162] For example, the acquisition unit can acquire the facial expression and voice of the child using the camera 42 and microphone 238 of the robot 414. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the child's emotion based on the acquired facial expression data and voice data. For example, the decision unit is realized by the specific processing unit 290 of the data processing device 12, and determines a response according to the estimated emotion. For example, the response unit is realized by the control unit 46A of the robot 414, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a child; an estimation unit that estimates an emotion of the child based on the emotion estimation information acquired by the acquisition unit; A determination unit that determines a response according to the child's emotion estimated by the estimation unit; a response unit that executes the response determined by the determination unit. A system characterized by: (Appendix 2) The determination unit is determining a dialogue in a manner corresponding to the child's emotion as a response corresponding to the child's emotion; The corresponding portion is Dialogue with the child in a manner corresponding to the child's emotions 2. The system of claim 1, (Appendix 3) The determination unit is determining a response to the child's emotions to provide a story corresponding to the child's emotions; The corresponding portion is Providing a story narration to the child that responds to the child's emotions 2. The system of claim 1, (Appendix 4) The acquisition unit is One or more pieces of information among the child's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, respiration, and posted content are acquired as emotion estimation information. 2. The system of claim 1, (Appendix 5) The acquisition unit is To estimate a child's emotion and adjust timing of acquiring emotion estimation information based on the estimated emotion of the child. 2. The system of claim 1, (Appendix 6) The acquisition unit is Analyze the child's past emotional data and select the appropriate acquisition method 2. The system of claim 1, (Appendix 7) The acquisition unit is Filtering emotion estimation information based on the child's current activity and environment 2. The system of claim 1, (Appendix 8) The acquisition unit is The child's emotion is estimated, and a priority order of emotion estimation information to be acquired is determined based on the estimated child's emotion. 2. The system of claim 1, (Appendix 9) The acquisition unit is When acquiring information for emotion estimation, the system prioritizes acquiring information that is more relevant to the child by taking into account the child's geographic location information. 2. The system of claim 1, (Appendix 10) The acquisition unit is When obtaining emotion estimation information, the child's social media activity is analyzed to obtain related information. 2. The system of claim 1, (Appendix 11) The estimation unit is Estimating a child's emotion and adjusting the emotion estimation algorithm based on the estimated child's emotion 2. The system of claim 1, (Appendix 12) The estimation unit is When estimating emotions, the level of detail of the estimation is adjusted based on the importance of the acquired emotion estimation information. 2. The system of claim 1, (Appendix 13) The estimation unit is When estimating emotions, different estimation algorithms are applied depending on the child's attribute information. 2. The system of claim 1, (Appendix 14) The estimation unit is Infer a child's emotions and adjust how the estimation is displayed based on the child's estimated emotions 2. The system of claim 1, (Appendix 15) The estimation unit is When estimating emotions, prioritize estimations based on the time when the acquired information was submitted. 2. The system of claim 1, (Appendix 16) The estimation unit is When estimating emotions, refer to related past data to improve the accuracy of the estimation. 2. The system of claim 1, (Appendix 17) The determination unit is Infer a child's emotions and adjust response decisions based on the inferred emotions of the child 2. The system of claim 1, (Appendix 18) The determination unit is When deciding how to respond, analyze the child's past reaction data to select the appropriate response method. 2. The system of claim 1, (Appendix 19) The determination unit is When making decisions, tailor your response based on the child's current situation 2. The system of claim 1, (Appendix 20) The determination unit is Estimate the child's emotions and prioritize responses based on the estimated child's emotions 2. The system of claim 1, (Appendix 21) The determination unit is When deciding on a course of action, the geographic location of the child is taken into consideration to select the most appropriate course of action. 2. The system of claim 1, (Appendix 22) The determination unit is When making decisions, analyze your child's social media activity to suggest action options 2. The system of claim 1, (Appendix 23) The corresponding portion is Inferring a child's emotions and adjusting response execution based on the child's emotions 2. The system of claim 1, (Appendix 24) The corresponding portion is When responding, the system refers to the child's past response data to select the optimal response method. 2. The system of claim 1, (Appendix 25) The corresponding portion is Customize your response based on the child's current situation when it comes to response time 2. The system of claim 1, (Appendix 26) The corresponding portion is Infer the child's emotions and adjust the order of response actions based on the estimated child's emotions 2. The system of claim 1, (Appendix 27) The corresponding portion is When taking action, the optimal action is chosen taking into account the child's geographic location 2. The system of claim 1, (Appendix 28) The corresponding portion is When taking action, analyze your child's social media activity to suggest action measures 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires emotion estimation information for estimating an emotion of a child; an estimation unit that estimates an emotion of the child based on the emotion estimation information acquired by the acquisition unit; A determination unit that determines a response according to the child's emotion estimated by the estimation unit; a response unit that executes the response determined by the determination unit. A system characterized by:

2. The determination unit is determining a dialogue in a manner corresponding to the child's emotion as a response corresponding to the child's emotion; The corresponding portion is Dialogue with the child in a manner corresponding to the child's emotions 2. The system of claim 1 .

3. The determination unit is determining a response to the child's emotions to provide a story corresponding to the child's emotions; The corresponding portion is Providing a story narration to the child that responds to the child's emotions 2. The system of claim 1 .

4. The acquisition unit is One or more pieces of information among the child's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, respiration, and posted content are acquired as the emotion estimation information.

2. The system of claim 1 .

5. The acquisition unit is The emotion of the child is estimated, and the timing of acquiring the emotion estimation information is adjusted based on the estimated emotion of the child.

2. The system of claim 1 .

6. The acquisition unit is Analyze the child's past emotion data and select an appropriate acquisition method.

2. The system of claim 1 .

7. The acquisition unit is When obtaining the emotion estimation information, filtering is performed based on the child's current activity and environment.

2. The system of claim 1 .

8. The acquisition unit is Estimating the emotion of the child, and determining a priority order of the emotion estimation information to be acquired based on the estimated emotion of the child.

2. The system of claim 1 .

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