system

Abstract

The system according to the embodiment aims to provide a mental support person that children can easily talk to and receive professional advice and support. [Solution] A system according to an embodiment includes a selection unit, a reception unit, a response generation unit, an analysis unit, and an advice provision unit. The selection unit allows a child to select a virtual friend or animal. The reception unit receives the child's speech, which the child speaks to the virtual friend or animal selected by the selection unit. The response generation unit understands the speech received by the reception unit and generates a response. The analysis unit provides information to a professional counselor as needed based on the response generated by the response generation unit. The advice provision unit allows the counselor to provide advice or support based on the information provided by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a lack of emotional support that children could easily turn to, and there were limited opportunities for them to receive professional advice and support.

[0005] The system according to the embodiment aims to provide a mental support person that children can easily talk to and receive professional advice and support. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a reception unit, a response generation unit, an analysis unit, and an advice provision unit. The selection unit allows a child to select a virtual friend or animal. The reception unit receives the child's speech, which the child speaks to the virtual friend or animal selected by the selection unit. The response generation unit understands the speech received by the reception unit and generates a response. The analysis unit provides information to a professional counselor as needed based on the response generated by the response generation unit. The advice provision unit allows the counselor to provide advice or support based on the information provided by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a mental advisor that children can easily talk to and receive professional advice and support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online mental health platform according to an embodiment of the present invention allows children to talk to virtual friends or their favorite animals for advice. This system is designed with the understanding that children often have various concerns that they cannot discuss with adults, such as problems with parents or friends or stress at school. Children can talk to them as if they were friends and receive advice and support from professional counselors. First, a child selects a virtual friend or animal. For example, they can choose their favorite animal, a dog or cat, or a virtual friend. Next, the child can talk to their selected virtual friend or animal. For example, they can talk about their concerns, such as "I had a bad day at school." A generation AI then understands what the child is saying and generates an appropriate response. The generation AI then analyzes the content of the child's conversation and provides information to a professional counselor as needed. The counselor then provides appropriate advice and support for the child's concerns. For example, if a child talks about stress at school, the counselor can suggest stress management methods and relaxation techniques. This platform aims to provide an environment where children can talk about their concerns in a safe manner and support their mental health. For example, children can sort out their feelings and reduce stress by talking to virtual friends or animals. They can also find specific solutions by receiving advice and support from professional counselors. In this way, online platforms that allow children to easily talk to virtual friends or animals for advice support children's mental health and provide an environment where they can safely talk about their worries. In this way, online platforms that provide emotional support provide an environment where children can safely talk about their worries, thereby supporting their mental health.

[0029] An online platform serving as a confidant in accordance with an embodiment includes a selection unit, a reception unit, a response generation unit, an analysis unit, and an advice provision unit. The selection unit allows a child to select a virtual friend or animal. When selecting a virtual friend or animal, the child can choose, for example, an animal such as a dog or a cat, or a virtual friend. The selection unit provides an interface for the child to select a favorite animal or virtual friend. For example, the selection unit allows the child to select an image of an animal or virtual friend on a screen. The reception unit receives the child's speech, which the child speaks to the virtual friend or animal selected by the child. The content of the child's speech includes worries, such as, "I had a bad day at school." The reception unit can receive the child's speech via voice input or text input. For example, the reception unit can receive the child's speech via a microphone as voice input. The reception unit can also receive the child's speech via a keyboard as text input. The response generation unit uses a generation AI to understand the speech received by the reception unit and generate an appropriate response. For example, the generation AI analyzes the child's speech and generates an appropriate response. The generation AI generates responses to what the child says using text generation AI (e.g., LLM) or multimodal generation AI. For example, in response to a child saying, "Something bad happened at school today," the generation AI generates a response such as, "That must have been tough. What happened?" The analysis unit provides information to a professional counselor as needed based on the response generated by the response generation unit. For example, the analysis unit analyzes the content of what the generation AI says and generates information to provide to the counselor. The generation AI analyzes the content of what the child says and generates information to provide to the counselor. For example, if a child talks about stress at school, the generation AI analyzes the content and generates information to provide to the counselor. The advice provision unit allows the counselor to provide advice and support based on the information provided by the analysis unit. For example, the advice provision unit allows the counselor to provide appropriate advice and support for the child's concerns. For example, if a child talks about stress at school, the counselor may suggest stress management methods and relaxation techniques.As a result, the online platform that serves as a mental health advisor according to the embodiment can provide an environment where children can feel safe talking about their worries, thereby supporting their mental health.

[0030] When making a selection, the selection unit can analyze the child's past selection history and prioritize displaying the virtual friends and animals selected most frequently. For example, the selection unit can prioritize displaying virtual friends that the child has frequently selected in the past. For example, the selection unit can analyze the types of animals the child has selected in the past and prioritize displaying animals of the same type. For example, the selection unit can analyze the characteristics of characters the child has selected in the past and prioritize displaying virtual friends and animals with similar characteristics. This improves selection efficiency by preferentially displaying virtual friends and animals that the child likes based on the past selection history. Some or all of the above-described processing in the selection unit can be performed using, or without, a generation AI. For example, the selection unit can input the child's past selection history data into the generation AI and identify the virtual friends and animals that the generation AI has selected most frequently.

[0031] The selection unit can dynamically change the options depending on the child's current mood and situation. For example, if the child has just returned home from school, the selection unit can suggest a virtual friend or animal that will help them relax. For example, if the child is in the mood to play, the selection unit can also suggest a playful virtual friend or animal. For example, if the child wants to concentrate on their studies, the selection unit can also suggest a virtual friend or animal that will help them concentrate. This allows the child to select a more appropriate person to talk to by providing optimal options according to the child's mood and situation. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input data on the child's current mood and situation into the generation AI, which can then suggest optimal options.

[0032] At the time of selection, the selection unit can suggest a virtual friend or animal based on the child's age and gender. For example, the selection unit can suggest a virtual friend or animal with a friendly character to a young child. For example, the selection unit can also suggest a virtual friend or animal that supports learning to an elementary school student. For example, the selection unit can also suggest a virtual friend or animal with a cute character to a girl. This makes it possible to provide a more appropriate person to talk to by suggesting the optimal virtual friend or animal according to the child's age and gender. Some or all of the above-described processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the child's age and gender data into the generation AI, which then suggests the optimal virtual friend or animal.

[0033] The selection unit can customize options taking into account the child's favorite colors and characters when making a selection. For example, the selection unit can suggest virtual friends or animals of the same color based on the child's favorite color. For example, the selection unit can also suggest virtual friends or animals with similar characteristics based on the characteristics of a character the child likes. For example, the selection unit can also suggest virtual friends or animals of the same type based on the type of animal the child likes. This improves the efficiency of selection by suggesting optimal virtual friends or animals according to the child's preferences. Some or all of the above-described processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the child's favorite color or character data into the generation AI, which can then customize the optimal options.

[0034] The reception unit can analyze the content of the child's speech in real time and extract important keywords. For example, the reception unit extracts keywords such as "school" and "friends" from the child's speech. The reception unit can also extract keywords such as "stress" and "worry" from the child's speech. The reception unit can also extract keywords such as "fun" and "happy" from the child's speech. This enables a more appropriate response by extracting important keywords in real time. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input audio data of the child's speech into a generation AI, which then extracts important keywords.

[0035] The reception unit can set a response time based on the length and frequency of the child's speech. For example, if the child speaks briefly, the reception unit can provide a short response. For example, if the child speaks long, the reception unit can also provide a detailed response. For example, if the child speaks frequently, the reception unit can adjust the frequency of the response. This enables more effective dialogue by setting an appropriate response time according to the length and frequency of the speech. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input data on the length and frequency of the child's speech into the generation AI, which can then set the response time.

[0036] The reception unit can convert the content of the child's speech into text using speech recognition technology, thereby improving the accuracy of analysis. The reception unit, for example, performs speech recognition on the child's speech in real time and converts it into text. The reception unit can, for example, extract important keywords based on the converted text. The reception unit can, for example, generate an appropriate response based on the converted text. In this way, by using speech recognition technology, the accuracy of analysis is improved and a more appropriate response is possible. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input audio data of the child's speech into a generation AI, which then converts the data into text.

[0037] The reception unit can record the content of the child's speech so that the counselor can check it later. The reception unit, for example, can record the child's speech so that the counselor can check it later. The reception unit can also enable the counselor to provide appropriate advice based on the recorded content, for example. The reception unit can also enable the counselor to understand the child's situation based on the recorded content, for example. This allows the counselor to provide appropriate advice based on the recorded content. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input audio data of the child's speech to the generation AI, and the generation AI can manage the recorded data.

[0038] When generating a response, the response generation unit can generate a more natural response by taking into account the context of the child's speech. For example, if the child is talking about school, the response generation unit generates a response that matches the context. For example, if the child is talking about his or her friends, the response generation unit can also generate a response that matches the context. For example, if the child is talking about his or her family, the response generation unit can also generate a response that matches the context. This enables more effective dialogue by generating natural responses according to the context. Some or all of the above-described processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input context data of the child's speech into the generation AI, which can then generate a natural response.

[0039] When generating a response, the response generation unit can provide a consistent response by referring to the content of the child's past conversations. The response generation unit generates a consistent response based on, for example, the content of the child's past conversations. The response generation unit can also generate an appropriate response based on, for example, worries the child has talked about in the past. The response generation unit can also generate a response based on, for example, happy events the child has talked about in the past. This makes it possible to provide a consistent response by referring to the content of past conversations. Some or all of the above-described processing in the response generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response generation unit can input content data of the child's past conversations into the generation AI, which can then generate a consistent response.

[0040] When generating a response, the response generation unit can provide relevant advice or information based on the content of the child's speech. For example, if a child talks about stress at school, the response generation unit can provide stress management advice. For example, if a child talks about trouble with a friend, the response generation unit can also provide a solution. For example, if a child talks about family problems, the response generation unit can also provide ways to improve family relationships. This enables more effective dialogue by providing appropriate advice or information based on the content of the child's speech. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input content data of the child's speech into a generation AI, which can then provide relevant advice or information.

[0041] When generating a response, the response generation unit can generate appropriate questions based on the content of the child's conversation to promote dialogue. For example, if the child is talking about school, the response generation unit can generate questions about school life. For example, if the child is talking about friends, the response generation unit can also generate questions about friendships. For example, if the child is talking about their family, the response generation unit can also generate questions about their family. This generates appropriate questions based on the content of the child's conversation, promoting dialogue and enabling more effective communication. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input content data of the child's conversation into the generation AI, which can then generate appropriate questions.

[0042] During analysis, the analysis unit classifies the content of the child's conversation into categories, enabling efficient analysis. For example, the analysis unit classifies and analyzes conversations about school as a category. For example, the analysis unit can also classify and analyze conversations about friends as a category. For example, the analysis unit can also classify and analyze conversations about family as a category. This enables efficient analysis by classifying conversations into categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input content data of the child's conversation into a generation AI, which can classify and analyze the conversations by category.

[0043] During analysis, the analysis unit can analyze the content of the child's speech along a timeline and track changes. The analysis unit, for example, organizes and analyzes the content of the child's speech by date. The analysis unit can also organize and analyze the content of the child's speech by week, for example. The analysis unit can also organize and analyze the content of the child's speech by month, for example. In this way, by performing analysis along a timeline, changes in the content of the child's speech can be tracked. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input content data of the child's speech into a generation AI, which then analyzes the content along a timeline and tracks changes.

[0044] During analysis, the analysis unit can compare the content of a child's speech with the speech of other children to identify common problems. For example, if multiple children are talking about stress at school, the analysis unit can identify common points. For example, if multiple children are talking about problems with their friendships, the analysis unit can also identify common points. For example, if multiple children are talking about family problems, the analysis unit can also identify common points. By comparing the content of a child's speech with the speech of other children, common problems can be identified and more effective countermeasures can be taken. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input content data of a child's speech into a generation AI, which can compare the content of a child's speech with the speech of other children to identify common problems.

[0045] During analysis, the analysis unit can visualize the content of the child's speech and provide it in a format that is easy for the counselor to understand. The analysis unit, for example, visualizes the content of the child's speech in a graph or chart. The analysis unit can also visualize the content of the child's speech in a mind map, for example. The analysis unit can also visualize the content of the child's speech in a timeline, for example. This visualization makes it easier for the counselor to understand the content of the child's speech. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input content data of the child's speech into a generation AI, and the generation AI can perform the visualization.

[0046] When providing advice, the advice providing unit can provide consistent advice by referring to the content of past consultations made by the child. The advice providing unit can provide consistent advice, for example, based on the content of consultations made by the child in the past. The advice providing unit can also provide continuous support, for example, based on advice the child has received in the past. The advice providing unit can also provide appropriate advice, for example, based on worries that the child has talked about in the past. This makes it possible to provide consistent advice by referring to the content of past consultations. Some or all of the above-mentioned processing in the advice providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the advice providing unit can input data on the content of past consultations made by the child into the generation AI, which can then provide consistent advice.

[0047] When providing advice, the advice providing unit can propose a specific action plan based on the child's current situation and environment. For example, if the child is feeling stressed at school, the advice providing unit can propose a specific stress management method. For example, if the child is having trouble with friendships, the advice providing unit can also propose a specific solution. For example, if the child is having problems at home, the advice providing unit can also propose a specific improvement measure. This enables more effective support by proposing a specific action plan based on the current situation and environment. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit can input data about the child's current situation and environment into the generation AI, which can then propose a specific action plan.

[0048] When providing advice, the advice providing unit can provide appropriate advice based on the child's age and gender. For example, the advice providing unit can provide advice in friendly language to young children. For example, the advice providing unit can also provide advice to support learning to elementary school students. For example, the advice providing unit can also provide advice in cute language to girls. This enables more effective support by providing optimal advice according to the child's age and gender. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input the child's age and gender data into the generation AI, which can then provide appropriate advice.

[0049] When providing advice, the advice providing unit can introduce relevant resources and support based on the content of the child's speech. For example, if the child talks about stress at school, the advice providing unit can introduce stress management resources. For example, if the child talks about troubles with friends, the advice providing unit can also introduce resources that offer solutions. For example, if the child talks about family problems, the advice providing unit can also introduce resources that offer ways to improve family relationships. This enables more effective support by introducing relevant resources and support based on the content of the child's speech. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit can input content data of the child's speech into a generation AI, which can then introduce relevant resources and support.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The selection unit can also suggest virtual friends or animals based on the child's interests and hobbies. For example, if a child is interested in sports, the selection unit can suggest virtual friends or animals that are good at sports. For example, if a child is interested in music, the selection unit can suggest virtual friends or animals that like music. For example, if a child likes drawing, the selection unit can suggest virtual friends or animals that are interested in art. This makes it possible to provide a more approachable person to talk to by suggesting the most suitable virtual friends or animals according to the child's interests and hobbies. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input data on the child's interests and hobbies into the generation AI, which can then suggest the most suitable virtual friends or animals.

[0052] The reception unit can translate the content of the child's speech in real time, enabling multilingual support. For example, if a child speaks in English, the reception unit can translate it into Japanese and provide it to the counselor. For example, if a child speaks in Spanish, the reception unit can translate it into English and provide it to the counselor. For example, if a child speaks in French, the reception unit can translate it into German and provide it to the counselor. This enables multilingual support, making it possible to accommodate children who speak different languages. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can input audio data of a child's speech into a generation AI, which can then translate it in real time.

[0053] The response generation unit can provide relevant videos and animations based on the content of the child's story. For example, if a child talks about stress at school, a video on stress management can be provided. For example, if a child talks about troubles with friends, the response generation unit can also provide an animation showing a solution. For example, if a child talks about family problems, the response generation unit can also provide a video showing how to improve family relationships. This makes it easier for the child to understand by providing visual information. Some or all of the above-mentioned processing in the response generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the response generation unit can input content data of the child's story into a generation AI, which can then provide relevant videos and animations.

[0054] The analysis unit can analyze the content of a child's conversation and track the child's growth and changes. For example, it can compare what the child has talked about in the past with what the child has talked about now to evaluate the degree of growth. The analysis unit can also compare, for example, worries the child has talked about in the past with worries they have talked about now to track changes. The analysis unit can also compare, for example, happy events the child has talked about in the past with happy events they talk about now to track growth. This allows for understanding the child's growth and changes and enables more appropriate support. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input content data of the child's conversation into the generation AI, which can then track the child's growth and changes.

[0055] When providing advice, the advice providing unit can provide appropriate advice based on the child's age and gender. For example, the advice providing unit can provide advice in friendly language to young children. For example, the advice providing unit can also provide advice to support learning to elementary school children. For example, the advice providing unit can also provide advice in cute language to girls. This enables more effective support by providing optimal advice according to the child's age and gender. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input the child's age and gender data into the generation AI, which can then provide appropriate advice.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The selection unit allows the child to select a virtual friend or animal. When the child selects a virtual friend or animal, the child can select an animal such as a dog or a cat, or a virtual friend. The selection unit provides an interface for the child to select a favorite animal or virtual friend. For example, the selection unit allows the child to select an image of an animal or virtual friend on the screen. Step 2: The reception unit receives the child's speech as he or she talks to the virtual friend or animal selected by the child. The content of the child's speech may include worries such as, "Something bad happened at school today." The reception unit can receive the child's speech as voice input or text input. For example, the reception unit can receive the child's speech as voice input through a microphone. The reception unit can also receive the child's speech as text input through a keyboard. Step 3: The response generation unit uses a generation AI to understand the story received by the reception unit and generate an appropriate response. For example, the response generation unit uses a generation AI to analyze the child's story and generate an appropriate response. The generation AI generates a response to the child's story using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, in response to a story such as "Something bad happened at school today," the generation AI generates a response such as "That must have been tough. What happened?" Step 4: The analysis unit provides information to a professional counselor as needed based on the response generated by the response generation unit. For example, the analysis unit uses the generation AI to analyze the content of the child's speech and generate information to be provided to the counselor. The generation AI analyzes the content of the child's speech and generates information to be provided to the counselor. For example, if a child talks about stress at school, the generation AI analyzes the content and generates information to be provided to the counselor. Step 5: In the advice providing unit, a counselor provides advice and support based on the information provided by the analysis unit. For example, in the advice providing unit, a counselor provides appropriate advice and support for a child's worries. For example, if a child talks about stress at school, the counselor will suggest stress management methods and relaxation techniques.

[0058] (Example 2) An online mental health platform according to an embodiment of the present invention allows children to talk to virtual friends or their favorite animals for advice. This system is designed with the understanding that children often have various concerns, such as problems with parents or friends, or stress at school, that they cannot discuss with adults. Children can talk to them as if they were friends and receive advice and support from professional counselors. First, a child selects a virtual friend or animal. For example, they can choose their favorite animal, a dog or cat, or a virtual friend. Next, the child can talk to the selected virtual friend or animal. For example, they can talk about their concerns, such as, "I had a bad day at school." A generation AI then understands what the child is saying and generates an appropriate response. The generation AI then analyzes the content of the child's conversation and provides information to a professional counselor as needed. The counselor then provides appropriate advice and support for the child's concerns. For example, if a child talks about stress at school, the counselor can suggest stress management methods and relaxation techniques. This platform aims to provide an environment where children can talk about their concerns in a safe manner and support their mental health. For example, children can sort out their feelings and reduce stress by talking to virtual friends or animals. They can also find specific solutions by receiving advice and support from professional counselors. In this way, online platforms that allow children to easily talk to virtual friends or animals for advice support children's mental health and provide an environment where they can safely talk about their worries. In this way, online platforms that provide emotional support provide an environment where children can safely talk about their worries, thereby supporting their mental health.

[0059] An online platform serving as a confidant in accordance with an embodiment includes a selection unit, a reception unit, a response generation unit, an analysis unit, and an advice provision unit. The selection unit allows a child to select a virtual friend or animal. When selecting a virtual friend or animal, the child can choose, for example, an animal such as a dog or a cat, or a virtual friend. The selection unit provides an interface for the child to select a favorite animal or virtual friend. For example, the selection unit allows the child to select an image of an animal or virtual friend on a screen. The reception unit receives the child's speech, which the child speaks to the virtual friend or animal selected by the child. The content of the child's speech includes worries, such as, "I had a bad day at school." The reception unit can receive the child's speech via voice input or text input. For example, the reception unit can receive the child's speech via a microphone as voice input. The reception unit can also receive the child's speech via a keyboard as text input. The response generation unit uses a generation AI to understand the speech received by the reception unit and generate an appropriate response. For example, the generation AI analyzes the child's speech and generates an appropriate response. The generation AI generates responses to what the child says using text generation AI (e.g., LLM) or multimodal generation AI. For example, in response to a child saying, "Something bad happened at school today," the generation AI generates a response such as, "That must have been tough. What happened?" The analysis unit provides information to a professional counselor as needed based on the response generated by the response generation unit. For example, the analysis unit analyzes the content of what the generation AI says and generates information to provide to the counselor. The generation AI analyzes the content of what the child says and generates information to provide to the counselor. For example, if a child talks about stress at school, the generation AI analyzes the content and generates information to provide to the counselor. The advice provision unit allows the counselor to provide advice and support based on the information provided by the analysis unit. For example, the advice provision unit allows the counselor to provide appropriate advice and support for the child's concerns. For example, if a child talks about stress at school, the counselor may suggest stress management methods and relaxation techniques.As a result, the online platform that serves as a mental health advisor according to the embodiment can provide an environment where children can feel safe talking about their worries, thereby supporting their mental health.

[0060] The selection unit can estimate the child's emotions and suggest a virtual friend or animal based on the estimated child's emotions. For example, if the child is sad, the selection unit can suggest a virtual friend or animal with a kind personality. For example, if the child is excited, the selection unit can also suggest a virtual friend or animal with a lively personality. For example, if the child is tired, the selection unit can also suggest a virtual friend or animal with a relaxing personality. This makes it possible to provide a more appropriate person to talk to by suggesting the optimal virtual friend or animal according to the child's emotions. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the child's facial expression data into the generation AI to estimate the child's emotions, and the generation AI can estimate the emotions.

[0061] When making a selection, the selection unit can analyze the child's past selection history and prioritize displaying the virtual friends and animals selected most frequently. For example, the selection unit can prioritize displaying virtual friends that the child has frequently selected in the past. For example, the selection unit can analyze the types of animals the child has selected in the past and prioritize displaying animals of the same type. For example, the selection unit can analyze the characteristics of characters the child has selected in the past and prioritize displaying virtual friends and animals with similar characteristics. This improves selection efficiency by preferentially displaying virtual friends and animals that the child likes based on the past selection history. Some or all of the above-described processing in the selection unit can be performed using, or without, a generation AI. For example, the selection unit can input the child's past selection history data into the generation AI and identify the virtual friends and animals that the generation AI has selected most frequently.

[0062] The selection unit can dynamically change the options depending on the child's current mood and situation. For example, if the child has just returned home from school, the selection unit can suggest a virtual friend or animal that will help them relax. For example, if the child is in the mood to play, the selection unit can also suggest a playful virtual friend or animal. For example, if the child wants to concentrate on their studies, the selection unit can also suggest a virtual friend or animal that will help them concentrate. This allows the child to select a more appropriate person to talk to by providing optimal options according to the child's mood and situation. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input data on the child's current mood and situation into the generation AI, which can then suggest optimal options.

[0063] The selection unit can estimate the child's emotions and adjust the display order of options based on the estimated child's emotions. For example, if the child is feeling anxious, the selection unit can first display virtual friends or animals that give a sense of security. For example, if the child is having fun, the selection unit can also first display virtual friends or animals with fun personalities. For example, if the child is tired, the selection unit can also first display virtual friends or animals that help the child relax. This improves selection efficiency by displaying optimal options according to the child's emotions. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the child's emotion data into the generation AI, which can then adjust the display order of options.

[0064] At the time of selection, the selection unit can suggest a virtual friend or animal based on the child's age and gender. For example, the selection unit can suggest a virtual friend or animal with a friendly character to a young child. For example, the selection unit can also suggest a virtual friend or animal that supports learning to an elementary school student. For example, the selection unit can also suggest a virtual friend or animal with a cute character to a girl. This makes it possible to provide a more appropriate person to talk to by suggesting the optimal virtual friend or animal according to the child's age and gender. Some or all of the above-described processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the child's age and gender data into the generation AI, which then suggests the optimal virtual friend or animal.

[0065] The selection unit can customize options taking into account the child's favorite colors and characters when making a selection. For example, the selection unit can suggest virtual friends or animals of the same color based on the child's favorite color. For example, the selection unit can also suggest virtual friends or animals with similar characteristics based on the characteristics of a character the child likes. For example, the selection unit can also suggest virtual friends or animals of the same type based on the type of animal the child likes. This improves the efficiency of selection by suggesting optimal virtual friends or animals according to the child's preferences. Some or all of the above-described processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the child's favorite color or character data into the generation AI, which can then customize the optimal options.

[0066] The reception unit can estimate the child's emotions and adjust the timing of speaking to the child based on the estimated emotions. For example, if the child is relaxed, the reception unit can immediately speak to the child. For example, if the child is excited, the reception unit can wait a short time before speaking to the child. For example, if the child is sad, the reception unit can speak to the child in a gentler tone. This enables more effective dialogue by speaking to the child at the optimal timing according to the child's emotions. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the child's emotion data into the generation AI and adjust the timing at which the generation AI speaks to the child.

[0067] The reception unit can analyze the content of the child's speech in real time and extract important keywords. For example, the reception unit extracts keywords such as "school" and "friends" from the child's speech. The reception unit can also extract keywords such as "stress" and "worry" from the child's speech. The reception unit can also extract keywords such as "fun" and "happy" from the child's speech. This enables a more appropriate response by extracting important keywords in real time. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input audio data of the child's speech into a generation AI, which then extracts important keywords.

[0068] The reception unit can set a response time based on the length and frequency of the child's speech. For example, if the child speaks briefly, the reception unit can provide a short response. For example, if the child speaks long, the reception unit can also provide a detailed response. For example, if the child speaks frequently, the reception unit can adjust the frequency of the response. This enables more effective dialogue by setting an appropriate response time according to the length and frequency of the speech. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, the generation AI. For example, the reception unit can input data on the length and frequency of the child's speech into the generation AI, which can then set the response time.

[0069] The reception unit can estimate the child's emotions and determine the priority of the content to be spoken to based on the estimated child's emotions. For example, if the child is feeling anxious, the reception unit can prioritize content that gives the child a sense of security. For example, if the child is having fun, the reception unit can also prioritize fun content. For example, if the child is tired, the reception unit can also prioritize relaxing content. This enables more effective dialogue by prioritizing optimal content according to the child's emotions. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the child's emotion data into the generation AI and determine the priority of the content to be spoken to by the generation AI.

[0070] The reception unit can convert the content of the child's speech into text using speech recognition technology, thereby improving the accuracy of analysis. The reception unit, for example, performs speech recognition on the child's speech in real time and converts it into text. The reception unit can, for example, extract important keywords based on the converted text. The reception unit can, for example, generate an appropriate response based on the converted text. In this way, by using speech recognition technology, the accuracy of analysis is improved and a more appropriate response is possible. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input audio data of the child's speech into a generation AI, which then converts the data into text.

[0071] The reception unit can record the content of the child's speech so that the counselor can check it later. The reception unit, for example, can record the child's speech so that the counselor can check it later. The reception unit can also enable the counselor to provide appropriate advice based on the recorded content, for example. The reception unit can also enable the counselor to understand the child's situation based on the recorded content, for example. This allows the counselor to provide appropriate advice based on the recorded content. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, the generation AI, for example. For example, the reception unit can input audio data of the child's speech to the generation AI, and the generation AI can manage the recorded data.

[0072] The response generation unit can estimate the child's emotions and generate a response in a tone or expression method based on the estimated child's emotions. For example, if the child is sad, the response generation unit can generate a response in a gentle tone. For example, if the child is excited, the response generation unit can also generate a response in a lively tone. For example, if the child is relaxed, the response generation unit can also generate a response in a calm tone. This enables more effective dialogue by generating a response in an optimal tone or expression method according to the child's emotions. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input the child's emotion data into the generation AI, which can then adjust the tone and expression method.

[0073] When generating a response, the response generation unit can generate a more natural response by taking into account the context of the child's speech. For example, if the child is talking about school, the response generation unit generates a response that matches the context. For example, if the child is talking about his or her friends, the response generation unit can also generate a response that matches the context. For example, if the child is talking about his or her family, the response generation unit can also generate a response that matches the context. This enables more effective dialogue by generating natural responses according to the context. Some or all of the above-described processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input context data of the child's speech into the generation AI, which can then generate a natural response.

[0074] When generating a response, the response generation unit can provide a consistent response by referring to the content of the child's past conversations. The response generation unit generates a consistent response based on, for example, the content of the child's past conversations. The response generation unit can also generate an appropriate response based on, for example, worries the child has talked about in the past. The response generation unit can also generate a response based on, for example, happy events the child has talked about in the past. This makes it possible to provide a consistent response by referring to the content of past conversations. Some or all of the above-described processing in the response generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the response generation unit can input content data of the child's past conversations into the generation AI, which can then generate a consistent response.

[0075] The response generation unit can estimate the child's emotions and adjust the length of the response based on the estimated child's emotions. For example, if the child is in a hurry, the response generation unit can generate a short response. For example, if the child is relaxed, the response generation unit can also generate a detailed response. For example, if the child is excited, the response generation unit can also generate a response of appropriate length. This enables more effective dialogue by providing an optimal response length according to the child's emotions. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input the child's emotion data into the generation AI, which can then adjust the length of the response.

[0076] When generating a response, the response generation unit can provide relevant advice or information based on the content of the child's speech. For example, if a child talks about stress at school, the response generation unit can provide stress management advice. For example, if a child talks about trouble with a friend, the response generation unit can also provide a solution. For example, if a child talks about family problems, the response generation unit can also provide ways to improve family relationships. This enables more effective dialogue by providing appropriate advice or information based on the content of the child's speech. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input content data of the child's speech into a generation AI, which can then provide relevant advice or information.

[0077] When generating a response, the response generation unit can generate appropriate questions based on the content of the child's conversation to promote dialogue. For example, if the child is talking about school, the response generation unit can generate questions about school life. For example, if the child is talking about friends, the response generation unit can also generate questions about friendships. For example, if the child is talking about their family, the response generation unit can also generate questions about their family. This generates appropriate questions based on the content of the child's conversation, promoting dialogue and enabling more effective communication. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input content data of the child's conversation into the generation AI, which can then generate appropriate questions.

[0078] The analysis unit can estimate the child's emotions and determine analysis priorities based on the estimated child's emotions. For example, if the child is feeling strong stress, the analysis unit can prioritize analyzing that content. For example, if the child is having fun, the analysis unit can postpone analyzing that content. For example, if the child is sad, the analysis unit can prioritize analyzing that content. This enables more effective analysis by determining the optimal analysis priority according to the child's emotions. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the child's emotion data into the generation AI, and the generation AI can determine the analysis priorities.

[0079] During analysis, the analysis unit classifies the content of the child's conversation into categories, enabling efficient analysis. For example, the analysis unit classifies and analyzes conversations about school as a category. For example, the analysis unit can also classify and analyze conversations about friends as a category. For example, the analysis unit can also classify and analyze conversations about family as a category. This enables efficient analysis by classifying conversations into categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input content data of the child's conversation into a generation AI, which can classify and analyze the conversations by category.

[0080] During analysis, the analysis unit can analyze the content of the child's speech along a timeline and track changes. The analysis unit, for example, organizes and analyzes the content of the child's speech by date. The analysis unit can also organize and analyze the content of the child's speech by week, for example. The analysis unit can also organize and analyze the content of the child's speech by month, for example. In this way, by performing analysis along a timeline, changes in the content of the child's speech can be tracked. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input content data of the child's speech into a generation AI, which then analyzes the content along a timeline and tracks changes.

[0081] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated child's emotions. For example, if the child is feeling anxious, the analysis unit can provide a display method that gives a sense of security. For example, if the child is having fun, the analysis unit can also provide a fun display method. For example, if the child is tired, the analysis unit can also provide a display method that helps the child to relax. This improves understanding of the analysis results by providing an optimal display method according to the child's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the child's emotion data into the generation AI, which can adjust the display method of the analysis results.

[0082] During analysis, the analysis unit can compare the content of a child's speech with the speech of other children to identify common problems. For example, if multiple children are talking about stress at school, the analysis unit can identify common points. For example, if multiple children are talking about problems with their friendships, the analysis unit can also identify common points. For example, if multiple children are talking about family problems, the analysis unit can also identify common points. By comparing the content of a child's speech with the speech of other children, common problems can be identified and more effective countermeasures can be taken. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input content data of a child's speech into a generation AI, which can compare the content of a child's speech with the speech of other children to identify common problems.

[0083] During analysis, the analysis unit can visualize the content of the child's speech and provide it in a format that is easy for the counselor to understand. The analysis unit, for example, visualizes the content of the child's speech in a graph or chart. The analysis unit can also visualize the content of the child's speech in a mind map, for example. The analysis unit can also visualize the content of the child's speech in a timeline, for example. This visualization makes it easier for the counselor to understand the content of the child's speech. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input content data of the child's speech into a generation AI, and the generation AI can perform the visualization.

[0084] The advice providing unit can estimate the child's emotions and provide advice based on the estimated child's emotions. For example, if the child is sad, the advice providing unit can provide advice including comforting words. For example, if the child is excited, the advice providing unit can also provide advice to stay calm. For example, if the child is relaxed, the advice providing unit can also provide advice to maintain relaxation. This enables more effective support by providing optimal advice according to the child's emotions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input the child's emotion data into the generation AI, which can then adjust the content of the advice.

[0085] When providing advice, the advice providing unit can provide consistent advice by referring to the content of past consultations made by the child. The advice providing unit can provide consistent advice, for example, based on the content of consultations made by the child in the past. The advice providing unit can also provide continuous support, for example, based on advice the child has received in the past. The advice providing unit can also provide appropriate advice, for example, based on worries that the child has talked about in the past. This makes it possible to provide consistent advice by referring to the content of past consultations. Some or all of the above-mentioned processing in the advice providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the advice providing unit can input data on the content of past consultations made by the child into the generation AI, which can then provide consistent advice.

[0086] When providing advice, the advice providing unit can propose a specific action plan based on the child's current situation and environment. For example, if the child is feeling stressed at school, the advice providing unit can propose a specific stress management method. For example, if the child is having trouble with friendships, the advice providing unit can also propose a specific solution. For example, if the child is having problems at home, the advice providing unit can also propose a specific improvement measure. This enables more effective support by proposing a specific action plan based on the current situation and environment. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit can input data about the child's current situation and environment into the generation AI, which can then propose a specific action plan.

[0087] The advice providing unit can estimate the child's emotions and determine the priority of advice based on the estimated child's emotions. For example, if the child is feeling strong stress, the advice providing unit can prioritize advice on that content. For example, if the child is having fun, the advice providing unit can also postpone that content. For example, if the child is sad, the advice providing unit can also prioritize advice on that content. This enables more effective support by determining the optimal priority of advice according to the child's emotions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, the generation AI. For example, the advice providing unit can input the child's emotion data into the generation AI, and the generation AI can determine the priority of advice.

[0088] When providing advice, the advice providing unit can provide appropriate advice based on the child's age and gender. For example, the advice providing unit can provide advice in friendly language to young children. For example, the advice providing unit can also provide advice to support learning to elementary school students. For example, the advice providing unit can also provide advice in cute language to girls. This enables more effective support by providing optimal advice according to the child's age and gender. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input the child's age and gender data into the generation AI, which can then provide appropriate advice.

[0089] When providing advice, the advice providing unit can introduce relevant resources and support based on the content of the child's speech. For example, if the child talks about stress at school, the advice providing unit can introduce stress management resources. For example, if the child talks about troubles with friends, the advice providing unit can also introduce resources that offer solutions. For example, if the child talks about family problems, the advice providing unit can also introduce resources that offer ways to improve family relationships. This enables more effective support by introducing relevant resources and support based on the content of the child's speech. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI, for example. For example, the advice providing unit can input content data of the child's speech into a generation AI, which can then introduce relevant resources and support. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, reception unit, response generation unit, analysis unit, and advice provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14, allowing a child to select an image of an animal or a virtual friend on the screen. The reception unit receives the child's speech as voice input or text input via the microphone 38B or keyboard of the smart device 14. The response generation unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to understand the child's speech and generate an appropriate response. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and the generation AI analyzes the content of the child's speech and generates information to be provided to a counselor. The advice provision unit is realized by the specific processing unit 290 of the data processing device 12, allowing a counselor to provide appropriate advice and support to the child's concerns. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, reception unit, response generation unit, analysis unit, and advice provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214, allowing a child to select an image of an animal or a virtual friend on the screen. The reception unit receives the child's speech as voice input or text input via the microphone 238 or keyboard of the smart glasses 214. The response generation unit is realized by the specific processing unit 290 of the data processing device 12, using a generation AI to understand the child's speech and generate an appropriate response. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, using a generation AI to analyze the content of the child's speech and generate information to be provided to a counselor. The advice provision unit is realized by the specific processing unit 290 of the data processing device 12, allowing a counselor to provide appropriate advice and support for the child's concerns. === Hard Collateral 1-3 === Each of the multiple elements, including the selection unit, reception unit, response generation unit, analysis unit, and advice provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset-type terminal 314, allowing a child to select an image of an animal or a virtual friend on the screen. The reception unit receives the child's speech as voice input or text input via the microphone 238 or keyboard of the headset-type terminal 314. The response generation unit is realized by the specific processing unit 290 of the data processing device 12, using a generation AI to understand the child's speech and generate an appropriate response. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, using a generation AI to analyze the content of the child's speech and generate information to be provided to a counselor. The advice provision unit is realized by the specific processing unit 290 of the data processing device 12, allowing a counselor to provide appropriate advice and support for the child's concerns. === Hard Collateral 1-4 === Each of the multiple elements, including the selection unit, reception unit, response generation unit, analysis unit, and advice provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414, allowing the child to select an image of an animal or a virtual friend on the screen. The reception unit receives the child's speech as voice input or text input via the microphone 238 or keyboard of the robot 414. The response generation unit is realized by the specific processing unit 290 of the data processing device 12, using a generation AI to understand the child's speech and generate an appropriate response. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, using a generation AI to analyze the content of the child's speech and generate information to be provided to a counselor. The advice provision unit is realized by the specific processing unit 290 of the data processing device 12, allowing the counselor to provide appropriate advice and support for the child's concerns.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The selection unit can also suggest virtual friends or animals based on the child's interests and hobbies. For example, if a child is interested in sports, the selection unit can suggest virtual friends or animals that are good at sports. For example, if a child is interested in music, the selection unit can suggest virtual friends or animals that like music. For example, if a child likes drawing, the selection unit can suggest virtual friends or animals that are interested in art. This makes it possible to provide a more approachable person to talk to by suggesting the most suitable virtual friends or animals according to the child's interests and hobbies. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input data on the child's interests and hobbies into the generation AI, which can then suggest the most suitable virtual friends or animals.

[0092] The reception unit can translate the content of the child's speech in real time, enabling multilingual support. For example, if a child speaks in English, the reception unit can translate it into Japanese and provide it to the counselor. For example, if a child speaks in Spanish, the reception unit can translate it into English and provide it to the counselor. For example, if a child speaks in French, the reception unit can translate it into German and provide it to the counselor. This enables multilingual support, making it possible to accommodate children who speak different languages. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI, for example. For example, the reception unit can input audio data of a child's speech into a generation AI, which can then translate it in real time.

[0093] The response generation unit can provide relevant videos and animations based on the content of the child's story. For example, if a child talks about stress at school, a video on stress management can be provided. For example, if a child talks about troubles with friends, the response generation unit can also provide an animation showing a solution. For example, if a child talks about family problems, the response generation unit can also provide a video showing how to improve family relationships. This makes it easier for the child to understand by providing visual information. Some or all of the above-mentioned processing in the response generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the response generation unit can input content data of the child's story into a generation AI, which can then provide relevant videos and animations.

[0094] The analysis unit can analyze the content of a child's conversation and track the child's growth and changes. For example, it can compare what the child has talked about in the past with what the child has talked about now to evaluate the degree of growth. The analysis unit can also compare, for example, worries the child has talked about in the past with worries they have talked about now to track changes. The analysis unit can also compare, for example, happy events the child has talked about in the past with happy events they talk about now to track growth. This allows for understanding the child's growth and changes and enables more appropriate support. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input content data of the child's conversation into the generation AI, which can then track the child's growth and changes.

[0095] The advice providing unit can estimate the child's emotions and provide advice that is sensitive to the child's emotions based on the estimated child's emotions. For example, if the child is sad, the advice providing unit can provide advice that includes comforting words. For example, if the child is excited, the advice providing unit can also provide advice to stay calm. For example, if the child is relaxed, the advice providing unit can also provide advice to maintain relaxation. This enables more effective support by providing optimal advice according to the child's emotions. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input the child's emotion data into the generation AI, which can then adjust the content of the advice.

[0096] The selection unit can estimate the child's emotions and adjust the display order of options based on the estimated child's emotions. For example, if the child is feeling anxious, virtual friends or animals that give a sense of security can be displayed first. For example, if the child is having fun, the selection unit can also display virtual friends or animals with fun personalities first. For example, if the child is tired, the selection unit can also display virtual friends or animals that help the child relax first. This improves selection efficiency by displaying optimal options according to the child's emotions. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the child's emotion data into the generation AI, which can adjust the display order of options.

[0097] The reception unit can estimate the child's emotions and adjust the timing of speaking to the child based on the estimated emotions. For example, if the child is relaxed, the reception unit can speak to the child immediately. For example, if the child is excited, the reception unit can wait a short time before speaking to the child. For example, if the child is sad, the reception unit can speak to the child in a gentler tone. This enables more effective dialogue by speaking to the child at the optimal timing according to the child's emotions. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the child's emotion data into the generation AI and adjust the timing at which the generation AI speaks to the child.

[0098] The response generation unit can estimate the child's emotions and generate a response in a tone or expression style based on the estimated child's emotions. For example, if the child is sad, the response generation unit can generate a response in a gentle tone. For example, if the child is excited, the response generation unit can also generate a response in a lively tone. For example, if the child is relaxed, the response generation unit can also generate a response in a calm tone. This enables more effective dialogue by generating a response in an optimal tone or expression style according to the child's emotions. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, a generation AI. For example, the response generation unit can input the child's emotion data into the generation AI, which can then adjust the tone and expression style.

[0099] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated child's emotions. For example, if the child is feeling anxious, the analysis unit can provide a display method that gives a sense of security. For example, if the child is having fun, the analysis unit can also provide a fun display method. For example, if the child is tired, the analysis unit can also provide a display method that helps the child to relax. This improves understanding of the analysis results by providing an optimal display method according to the child's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the child's emotion data into the generation AI, which can then adjust the display method of the analysis results.

[0100] When providing advice, the advice providing unit can provide appropriate advice based on the child's age and gender. For example, the advice providing unit can provide advice in friendly language to young children. For example, the advice providing unit can also provide advice to support learning to elementary school children. For example, the advice providing unit can also provide advice in cute language to girls. This enables more effective support by providing optimal advice according to the child's age and gender. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, a generation AI. For example, the advice providing unit can input the child's age and gender data into the generation AI, which can then provide appropriate advice.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The selection unit allows the child to select a virtual friend or animal. When the child selects a virtual friend or animal, the child can select an animal such as a dog or a cat, or a virtual friend. The selection unit provides an interface for the child to select a favorite animal or virtual friend. For example, the selection unit allows the child to select an image of an animal or virtual friend on the screen. Step 2: The reception unit receives the child's speech as he or she talks to the virtual friend or animal selected by the child. The content of the child's speech may include worries such as, "Something bad happened at school today." The reception unit can receive the child's speech as voice input or text input. For example, the reception unit can receive the child's speech as voice input through a microphone. The reception unit can also receive the child's speech as text input through a keyboard. Step 3: The response generation unit uses a generation AI to understand the story received by the reception unit and generate an appropriate response. For example, the response generation unit uses a generation AI to analyze the child's story and generate an appropriate response. The generation AI generates a response to the child's story using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, in response to a story such as "Something bad happened at school today," the generation AI generates a response such as "That must have been tough. What happened?" Step 4: The analysis unit provides information to a professional counselor as needed based on the response generated by the response generation unit. For example, the analysis unit uses the generation AI to analyze the content of the child's speech and generate information to be provided to the counselor. The generation AI analyzes the content of the child's speech and generates information to be provided to the counselor. For example, if a child talks about stress at school, the generation AI analyzes the content and generates information to be provided to the counselor. Step 5: In the advice providing unit, a counselor provides advice and support based on the information provided by the analysis unit. For example, in the advice providing unit, a counselor provides appropriate advice and support for a child's worries. For example, if a child talks about stress at school, the counselor will suggest stress management methods and relaxation techniques.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] 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.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection section where the child selects a virtual friend or animal; a reception unit that receives a child's speech to talk to the virtual friend or animal selected by the selection unit; a response generation unit that understands the speech received by the reception unit and generates a response; an analysis unit that provides information to a professional counselor as needed based on the response generated by the response generation unit; and an advice providing unit in which a counselor provides advice and support based on the information provided by the analysis unit. A system characterized by:

2. The selection unit Estimates a child's emotions and suggests a virtual friend or animal based on the child's estimated emotions 2. The system of claim 1.

3. The selection unit When making a selection, the system analyzes the child's past selection history and prioritizes the virtual friends and animals that are most frequently selected.

2. The system of claim 1.

4. The selection unit Dynamically change choices depending on the child's current mood or situation 2. The system of claim 1.

5. The selection unit Inferring the child's emotions and adjusting the order in which options are displayed based on the estimated emotions 2. The system of claim 1.

6. The selection unit When selected, suggests virtual friends or animals based on the child's age and gender 2. The system of claim 1.

7. The selection unit When selecting, customize your choices by taking into account your child's favorite colors and characters.

2. The system of claim 1.

8. The reception unit Estimate the child's emotions and speak to them at the right time based on the estimated emotions 2. The system of claim 1.

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