system

Abstract

The system according to the embodiment aims to take care of children in place of busy parents. [Solution] A system according to an embodiment includes a speech recognition unit, a natural language processing unit, a speech synthesis unit, a voice changer unit, and a control unit. The speech recognition unit recognizes the child's words. The natural language processing unit generates a response based on the words recognized by the speech recognition unit. The speech synthesis unit converts the response generated by the natural language processing unit into speech. The voice changer unit changes the voice converted by the speech synthesis unit for each character. The control unit controls each unit via the Internet.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for busy parents to find time to spend with their children, making it difficult to meet their children's needs.

[0005] The system according to the embodiment aims to take care of children in place of busy parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a natural language processing unit, a speech synthesis unit, a voice changer unit, and a control unit. The speech recognition unit recognizes the child's words. The natural language processing unit generates a response based on the words recognized by the speech recognition unit. The speech synthesis unit converts the response generated by the natural language processing unit into speech. The voice changer unit changes the voice converted by the speech synthesis unit for each character. The control unit controls each unit via the Internet. [Effects of the Invention]

[0007] The system according to the embodiment can take care of children in place of busy parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A child-rearing support system according to an embodiment of the present invention recognizes a child's words, generates appropriate responses, and provides them in different voices for each character. When a child speaks to the system, the system uses speech recognition technology to understand the words and natural language processing technology to generate appropriate responses. The generated responses are reproduced in the voices of dolls or stuffed animals using speech synthesis technology. Furthermore, different voices can be provided for each character using voice changer technology. For example, when a child speaks to the system, the system uses speech recognition technology to understand the words. Then, natural language processing technology is used to generate appropriate responses. The generated responses are reproduced in the voices of dolls or stuffed animals using speech synthesis technology. Furthermore, different voices can be provided for each character using voice changer technology. This allows children to enjoy communicating with their dolls or stuffed animals while their mothers get on with their housework. Furthermore, these functions can be controlled via the Internet using IoT technology. For example, settings for dolls or stuffed animals can be changed or new conversation patterns can be added via a smartphone app. This system is expected to reduce the burden on parents and contribute to addressing the declining birthrate. This allows the child-rearing support system to support communication with children by recognizing what the child is saying, generating appropriate responses, and providing them in different voices for each character. For example, when a child speaks, the system uses voice recognition technology to understand what the child is saying and natural language processing technology to generate an appropriate response. The generated response is then played back in the voice of a doll or stuffed animal using speech synthesis technology. Furthermore, voice changer technology can be used to provide different voices for each character. This allows children to enjoy communicating with their dolls and stuffed animals, while their mothers can get on with their housework. Furthermore, using IoT technology, these functions can be controlled via the Internet. For example, a smartphone app can be used to change the settings of the doll or stuffed animal or add new conversation patterns.This is expected to reduce the burden on the child-rearing generation and contribute to measures to combat the declining birthrate.

[0029] A child-rearing support system according to an embodiment includes a voice recognition unit, a natural language processing unit, a voice synthesis unit, a voice changer unit, and a control unit. The voice recognition unit recognizes a child's words. Examples of the child's words include, but are not limited to, age groups, languages, and speech styles. The voice recognition unit understands the words spoken by the child using voice recognition technology. The voice recognition unit can also dynamically change voice recognition parameters depending on the child's speaking speed and volume. For example, if the child speaks quickly, the voice recognition processing speed can be increased to enable real-time recognition. The natural language processing unit generates an appropriate response based on the words recognized by the voice recognition unit. Examples of responses include, but are not limited to, voice messages and text messages. For example, if a child asks, "Why is the sky blue?", the natural language processing unit generates a response including a scientific explanation. The natural language processing unit can also estimate the child's emotions and adjust the tone and content of the response based on the estimated emotions. For example, if the child is excited, the natural language processing unit generates a response with a cheerful and upbeat tone. The speech synthesis unit converts the response generated by the natural language processing unit into speech. The speech synthesis unit converts the response into speech, for example, using a generative AI. The speech synthesis unit can also estimate the child's emotions and adjust the tone and pitch of the speech based on the estimated child's emotions. For example, if the child is excited, it generates a cheerful and bright tone of speech. The voice changer unit provides the speech converted by the speech synthesis unit in a different voice for each character. For example, the voice changer unit sets a high-pitched voice to match the voice of an anime character. The voice changer unit can also estimate the child's emotions and adjust the voice changer settings based on the estimated child's emotions. For example, if the child is excited, it sets the voice changer settings to a cheerful and bright voice. The control unit controls each unit via the Internet. For example, the control unit changes the settings of a doll or stuffed animal through a smartphone app. The control unit can also add new conversation patterns and functions via the Internet. For example, it downloads and adds new conversation patterns via the Internet.As a result, the child-rearing support system according to the embodiment can support communication with children by recognizing what the child is saying, generating appropriate responses, and providing them in different voices for each character.

[0030] The voice recognition unit can change voice recognition parameters according to the child's speaking rate and volume. The voice recognition unit changes voice recognition parameters according to the child's speaking rate and volume. The speaking rate includes, for example, but is not limited to, the number of words per second and the number of syllables per second. The volume includes, for example, but is not limited to, decibels (dB). For example, when a child speaks quickly, the voice recognition unit can increase the voice recognition processing speed to enable real-time recognition. Furthermore, when a child speaks softly, the voice recognition unit can increase the voice recognition sensitivity to enable accurate recognition even at low volumes. Furthermore, when a child speaks loudly, the voice recognition unit can decrease the voice recognition sensitivity to enable recognition while reducing noise. In this way, various speaking situations can be accommodated by dynamically changing voice recognition parameters according to the child's speaking rate and volume. Some or all of the above-described processing in the voice recognition unit may be performed, for example, using AI or without AI. For example, the voice recognition unit can input the child's speaking rate and volume data into the generation AI and cause the generation AI to change the voice recognition parameters.

[0031] The voice recognition unit can prioritize recognition of specific keywords based on the child's speech. The voice recognition unit prioritizes recognition of specific keywords based on the child's speech. Specific keywords include, but are not limited to, frequently occurring words and important words. For example, if a child says "Mom," the voice recognition unit can prioritize recognition of the word and generate a related response. Furthermore, if a child says "Let's play," the voice recognition unit can prioritize recognition of the word and generate a response related to play. Furthermore, if a child says "I'm hungry," the voice recognition unit can prioritize recognition of the word and generate a response related to eating. Thus, by prioritizing recognition of specific keywords, it is possible to quickly respond to important speech content. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the voice recognition unit can input specific keyword data to a generation AI and cause the generation AI to prioritize keyword recognition.

[0032] The voice recognition unit can filter voice recognition according to the child's speaking environment. The voice recognition unit filters voice recognition according to the child's speaking environment. The speaking environment includes, but is not limited to, for example, background sound and noise level. For example, when the background sound is loud, the voice recognition unit strengthens noise cancellation to maintain voice recognition accuracy. Furthermore, in a quiet environment, the voice recognition unit can minimize filtering to recognize natural voices. Furthermore, when sudden noise occurs, the voice recognition unit can remove the noise and perform voice recognition. In this way, by filtering according to the speaking environment, voice recognition with the influence of noise minimized can be performed. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input speaking environment data to a generation AI and cause the generation AI to perform voice recognition filtering.

[0033] The natural language processing unit can generate a response including an educational element based on the child's utterance. The natural language processing unit generates a response including an educational element based on the child's utterance. The educational element includes, but is not limited to, learning goals and educational content. For example, when a child asks, "Why is the sky blue?", the natural language processing unit can generate a response including a scientific explanation. Furthermore, when a child asks, "Why do I have to study?", the natural language processing unit can generate a response explaining the importance of learning. Furthermore, when a child asks, "How do I make friends?", the natural language processing unit can generate a response that teaches social skills. Thus, by generating a response including an educational element, it is possible to support the child's learning and development. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input educational element data into a generation AI and cause the generation AI to generate a response including an educational element.

[0034] The natural language processing unit can generate an individually customized response by referring to the child's speech history. The natural language processing unit generates an individually customized response by referring to the child's speech history. The speech history includes, for example, past speech content, interests, and concerns, but is not limited to these examples. The natural language processing unit generates a relevant response based on, for example, content that the child has spoken in the past. The natural language processing unit can also generate a customized response based on the child's interests and concerns. The natural language processing unit can also analyze the child's speech history and generate a response based on a specific pattern. In this way, an individually customized response can be generated by referring to the speech history. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input speech history data to a generation AI and cause the generation AI to generate a customized response.

[0035] The natural language processing unit can generate replies that support the progress of the story and game based on the content of the child's utterances. The natural language processing unit generates replies that support the progress of the story and game based on the content of the child's utterances. The progress of the story and game includes, but is not limited to, branching scenarios and game rules. For example, when a child says, "Tell me the story," the natural language processing unit can generate a continuation of the story. Furthermore, when a child says, "Let's play a game," the natural language processing unit can generate a replies that explain the rules and progress of the game. Furthermore, when a child asks, "What do we do next?" the natural language processing unit can generate a replies that explain the next step in the story or game. In this way, by generating replies that support the progress of the story or game, it is possible to keep the child interested. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input story and game progress data into a generation AI and cause the generation AI to generate replies that support the progress.

[0036] The voice synthesis unit can select a character voice according to the child's preferences when synthesizing voice. The voice synthesis unit can select a character voice according to the child's preferences when synthesizing voice. Character voices include, but are not limited to, for example, the voices of anime characters, animals, and singers. For example, the voice synthesis unit can select the voice of an anime character that the child likes to generate voice. The voice synthesis unit can also select the voice of an animal that the child likes to generate voice. The voice synthesis unit can also select the voice of a singer that the child likes to generate voice. In this way, by selecting a character voice according to the child's preferences, it is possible to keep the child interested. Some or all of the above-described processing in the voice synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice synthesis unit can input character voice data to a generation AI and cause the generation AI to select a character voice.

[0037] The speech synthesis unit can adjust the speed and rhythm of the speech during speech synthesis according to the content of the child's speech. The speech synthesis unit can adjust the speed and rhythm of the speech during speech synthesis according to the content of the child's speech. Examples of the speed and rhythm of the speech include, but are not limited to, changing the tempo and selecting a rhythm pattern. For example, if the child speaks quickly, the speech synthesis unit can synthesize the speech at a faster speed. Furthermore, if the child speaks slowly, the speech synthesis unit can synthesize the speech at a slower speed. Furthermore, if the child speaks rhythmically, the speech synthesis unit can synthesize the speech by adjusting the rhythm of the speech. This enables more natural speech expression by generating speech with a speed and rhythm according to the content of the speech. Some or all of the above-described processing in the speech synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech synthesis unit can input speech content data to a generation AI and cause the generation AI to adjust the speed and rhythm of the speech.

[0038] The voice synthesis unit can add music and sound effects based on the child's speech during voice synthesis. The voice synthesis unit can add music and sound effects based on the child's speech during voice synthesis. Examples of music and sound effects include, but are not limited to, a music library and types of sound effects. For example, when a child says "sing me," the voice synthesis unit can add music to the voice synthesis to generate a song. Furthermore, when a child says "tell me a story," the voice synthesis unit can add sound effects to the voice synthesis to generate a story. Furthermore, when a child says "let's play," the voice synthesis unit can add game sound effects to the voice synthesis to create a playful atmosphere. By adding music and sound effects, more enjoyable voice expression is possible. Some or all of the above-described processing in the voice synthesis unit may be performed using, or without, AI. For example, the voice synthesis unit can input music and sound effect data to a generation AI and have the generation AI add music and sound effects.

[0039] The voice changer unit can set the voice tone and pitch for each character when using the voice changer. The voice changer unit can set the voice tone and pitch for each character when using the voice changer. The voice tone and pitch include, for example, voice characteristics and setting parameters for each character, but are not limited to these examples. For example, the voice changer unit can set a high-pitched voice to match the voice of an anime character. The voice changer unit can also set a low-pitched voice to match the voice of an animal character. The voice changer unit can also set a specific pitch to match the voice of a singer character. This allows for a wider variety of voice expression by setting different voice tones and pitches for each character. Some or all of the above-described processing in the voice changer unit can be performed using, or without, AI. For example, the voice changer unit can input character voice data into a generation AI and have the generation AI set the voice tone and pitch.

[0040] The voice changer unit can change the voice effects according to the child's speech when using the voice changer. The voice changer unit changes the voice effects according to the child's speech when using the voice changer. Voice effects include, but are not limited to, reverb, echo, and distortion. For example, when a child says, "Tell me a story," the voice changer unit can set a voice effect that matches the character in the story. Also, when a child says, "Sing," the voice changer unit can set a voice effect that matches the singer's character. Also, when a child says, "Let's play," the voice changer unit can set a voice effect that matches the character in the game. This enables a wider variety of voice expression by changing the voice effects according to the speech content. Some or all of the above-described processing in the voice changer unit may be performed using, for example, AI, or without AI. For example, the voice changer unit can input speech content data into a generation AI and have the generation AI change the voice effects.

[0041] The voice changer unit can customize the voice character according to the child's preferences when using the voice changer. The voice changer unit customizes the voice character according to the child's preferences when using the voice changer. Examples of voice characters include, but are not limited to, anime characters, animal voices, and singer voices. For example, the voice changer unit customizes the voice to the child's favorite anime character. The voice changer unit can also customize the voice to the child's favorite animal voice. The voice changer unit can also customize the voice to the child's favorite singer voice. This allows the voice character to be customized according to the child's preferences, thereby maintaining the child's interest. Some or all of the above-described processing in the voice changer unit may be performed using, or without, AI. For example, the voice changer unit can input character voice data to a generation AI and have the generation AI customize the voice character.

[0042] The control unit can instantly change the settings of each unit through the smartphone app. The control unit instantly changes the settings of each unit through the smartphone app. The settings of each unit include, but are not limited to, the sensitivity of the voice recognition unit, the response content of the natural language processing unit, and the tone and pitch of the voice synthesis unit. For example, the control unit adjusts the sensitivity of the voice recognition unit through the smartphone app. The control unit can also change the response content of the natural language processing unit through the smartphone app. The control unit can also adjust the tone and pitch of the voice synthesis unit through the smartphone app. This improves usability by changing the settings of each unit in real time through the smartphone app. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input setting change data from the smartphone app to the generation AI and cause the generation AI to change the settings of each unit.

[0043] The control unit can optimize the operation of each unit by referring to the child's speech history. The control unit optimizes the operation of each unit by referring to the child's speech history. The speech history includes, for example, past speech content, interests, and concerns, but is not limited to these examples. The control unit, for example, optimizes the sensitivity of the voice recognition unit based on the child's speech history. The control unit can also optimize the response content of the natural language processing unit based on the child's speech history. The control unit can also optimize the tone and pitch of the speech synthesis unit based on the child's speech history. In this way, the operation of each unit can be optimized by referring to the speech history. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input speech history data to a generation AI and cause the generation AI to optimize the operation of each unit.

[0044] The control unit can add new conversation patterns and functions via the Internet. The control unit can add new conversation patterns and functions via the Internet. New conversation patterns and functions include, but are not limited to, update procedures and descriptions of new functions. For example, the control unit can download and add new conversation patterns via the Internet. The control unit can also add new voice synthesis functions via the Internet. The control unit can also add new voice changer effects via the Internet. This improves the flexibility of the system by adding new conversation patterns and functions via the Internet. Some or all of the above-described processing in the control unit can be performed using AI, for example, or without AI. For example, the control unit can input new function addition data via the Internet to the generation AI and cause the generation AI to add new conversation patterns and functions.

[0045] The control unit can schedule the operation of each unit through the smartphone app. The control unit schedules the operation of each unit through the smartphone app. Scheduling the operation of each unit includes, but is not limited to, schedule setting and schedule change methods, for example. The control unit, for example, sets the operation schedule of the voice recognition unit through the smartphone app. The control unit can also set the response schedule of the natural language processing unit through the smartphone app. The control unit can also set the tone and pitch schedule of the voice synthesis unit through the smartphone app. This improves usability by scheduling the operation of each unit through the smartphone app. Some or all of the above-mentioned processing in the control unit may be performed using, or without using, AI, for example. For example, the control unit can input schedule data from the smartphone app to a generation AI and have the generation AI execute the operation schedule of each unit.

[0046] The control unit can customize the operation of each unit based on the content of the child's utterance. The control unit customizes the operation of each unit based on the content of the child's utterance. Customizing the operation of each unit can include, for example, customization options, setting procedures, etc., but is not limited to these examples. For example, the control unit customizes the sensitivity of the voice recognition unit based on the content of the child's utterance. The control unit can also customize the response content of the natural language processing unit based on the content of the child's utterance. The control unit can also customize the tone and pitch of the voice synthesis unit based on the content of the child's utterance. This allows for more appropriate operation by customizing the operation of each unit based on the content of the utterance. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input utterance content data to a generation AI and have the generation AI customize the operation of each unit.

[0047] The control unit can remotely monitor and control the operation of each unit via the Internet. The control unit remotely monitors and controls the operation of each unit via the Internet. Remote monitoring and control of the operation of each unit can be achieved, for example, by remote access methods, monitoring tools, and the like, but is not limited to these examples. The control unit, for example, remotely monitors the operation of the speech recognition unit via the Internet and adjusts it as needed. The control unit can also remotely monitor the response content of the natural language processing unit via the Internet and adjust it as needed. The control unit can also remotely monitor the tone and pitch of the speech synthesis unit via the Internet and adjust it as needed. This remote monitoring and control of the operation of each unit via the Internet improves the flexibility of the system. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input remote monitoring and control data to the generation AI and have the generation AI monitor and control the operation of each unit.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The child-rearing support system may further include a health management unit that monitors the child's health condition. The health management unit may use sensors to measure the child's body temperature, heart rate, activity level, etc., and evaluate the child's health condition based on this data. For example, if the child has a fever, the health management unit may send a notification to the parent. If the child's activity level is low, the health management unit may also generate a message encouraging the parent to exercise. Furthermore, the health management unit may monitor the child's sleep patterns and provide advice on providing an appropriate sleeping environment. In this way, the child-rearing support system can support the child's health management and reduce the burden on parents.

[0050] The child-rearing support system can further include a learning management unit that manages a child's learning progress. The learning management unit, for example, records the content and progress of a child's learning and creates a learning plan based on this data. For example, if a child has difficulty in a particular subject, the learning management unit can advise the child to focus on studying that part. The learning management unit can also provide learning materials that match the child's learning style. Furthermore, the learning management unit can provide a learning program that incorporates game elements to increase a child's motivation to learn. In this way, the child-rearing support system can support children's learning and provide an effective learning environment.

[0051] The child-rearing support system may further include a social interaction unit for fostering a child's social skills. The social interaction unit may, for example, provide a platform for a child to safely communicate with other children. For example, the social interaction unit may provide games or activities that a child can play with friends online. The social interaction unit may also provide projects for a child to cooperate with other children to solve problems. The social interaction unit may also provide exercises that allow a child to empathize with other children. In this way, the child-rearing support system may support the development of a child's social skills.

[0052] The child-rearing support system can further include a creative support unit for fostering children's creativity. The creative support unit provides, for example, tools for children to draw pictures and create stories. For example, it can digitize pictures drawn by children and turn them into animations. The creative support unit can also read aloud stories created by children using voice synthesis technology. Furthermore, the creative support unit can provide a platform for children to share their creations with other children. In this way, the child-rearing support system can support the development of children's creativity.

[0053] The child-rearing support system may further include a security management unit to ensure the safety of children. The security management unit may, for example, provide a filtering function to allow children to use the Internet safely. For example, if a child attempts to access inappropriate content, the security management unit may block that access. The security management unit may also monitor a child's online interactions and send a notification to parents if inappropriate behavior is detected. Furthermore, the security management unit may track a child's location information and check whether the child is within a safe range. This allows the child-rearing support system to ensure the safety of children and provide parents with peace of mind.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The voice recognition unit recognizes the child's words. The child's words may include, but are not limited to, age groups, languages, and speech styles. For example, when a child speaks, the voice recognition unit uses voice recognition technology to understand the words. The voice recognition unit can also dynamically change voice recognition parameters depending on the child's speaking speed and volume. For example, if a child speaks quickly, the voice recognition processing speed can be increased to enable real-time recognition. Step 2: The natural language processing unit generates an appropriate response based on the words recognized by the speech recognition unit. Responses include, but are not limited to, voice messages, text messages, etc. For example, if a child asks, "Why is the sky blue?", the natural language processing unit generates a response including a scientific explanation. The natural language processing unit can also estimate the child's emotions and adjust the tone and content of the response based on the estimated child's emotions. For example, if the child is excited, the natural language processing unit generates a response with a cheerful and upbeat tone. Step 3: The speech synthesis unit converts the response generated by the natural language processing unit into speech. The speech synthesis unit converts the response into speech, for example, using a generation AI. The speech synthesis unit can also estimate the child's emotions and adjust the tone and pitch of the speech based on the estimated child's emotions. For example, if the child is excited, it generates a cheerful and bright tone of speech. Step 4: The voice changer unit provides the voice converted by the voice synthesis unit in a different voice for each character. For example, the voice changer unit sets a high-pitched voice to match the voice of an anime character. The voice changer unit can also estimate the child's emotions and adjust the voice changer settings based on the estimated child's emotions. For example, if the child is excited, the voice changer unit sets a lively and cheerful voice. Step 5: The control unit controls each unit via the Internet. For example, the control unit can change the settings of the doll or stuffed animal via a smartphone app. The control unit can also add new conversation patterns and functions via the Internet. For example, it can download and add new conversation patterns via the Internet.

[0056] (Example 2) A child-rearing support system according to an embodiment of the present invention recognizes a child's words, generates appropriate responses, and provides them in different voices for each character. When a child speaks to the system, the system uses speech recognition technology to understand the words and natural language processing technology to generate appropriate responses. The generated responses are reproduced in the voices of dolls or stuffed animals using speech synthesis technology. Furthermore, different voices can be provided for each character using voice changer technology. For example, when a child speaks to the system, the system uses speech recognition technology to understand the words. Then, natural language processing technology is used to generate appropriate responses. The generated responses are reproduced in the voices of dolls or stuffed animals using speech synthesis technology. Furthermore, different voices can be provided for each character using voice changer technology. This allows children to enjoy communicating with their dolls or stuffed animals while their mothers get on with their housework. Furthermore, these functions can be controlled via the Internet using IoT technology. For example, settings for dolls or stuffed animals can be changed or new conversation patterns can be added via a smartphone app. This system is expected to reduce the burden on parents and contribute to addressing the declining birthrate. This allows the child-rearing support system to support communication with children by recognizing what the child is saying, generating appropriate responses, and providing them in different voices for each character. For example, when a child speaks, the system uses voice recognition technology to understand what the child is saying and natural language processing technology to generate an appropriate response. The generated response is then played back in the voice of a doll or stuffed animal using speech synthesis technology. Furthermore, voice changer technology can be used to provide different voices for each character. This allows children to enjoy communicating with their dolls and stuffed animals, while their mothers can get on with their housework. Furthermore, using IoT technology, these functions can be controlled via the Internet. For example, a smartphone app can be used to change the settings of the doll or stuffed animal or add new conversation patterns.This is expected to reduce the burden on the child-rearing generation and contribute to measures to combat the declining birthrate.

[0057] A child-rearing support system according to an embodiment includes a voice recognition unit, a natural language processing unit, a voice synthesis unit, a voice changer unit, and a control unit. The voice recognition unit recognizes a child's words. Examples of the child's words include, but are not limited to, age groups, languages, and speech styles. The voice recognition unit understands the words spoken by the child using voice recognition technology. The voice recognition unit can also dynamically change voice recognition parameters depending on the child's speaking speed and volume. For example, if the child speaks quickly, the voice recognition processing speed can be increased to enable real-time recognition. The natural language processing unit generates an appropriate response based on the words recognized by the voice recognition unit. Examples of responses include, but are not limited to, voice messages and text messages. For example, if a child asks, "Why is the sky blue?", the natural language processing unit generates a response including a scientific explanation. The natural language processing unit can also estimate the child's emotions and adjust the tone and content of the response based on the estimated emotions. For example, if the child is excited, the natural language processing unit generates a response with a cheerful and upbeat tone. The speech synthesis unit converts the response generated by the natural language processing unit into speech. The speech synthesis unit converts the response into speech, for example, using a generative AI. The speech synthesis unit can also estimate the child's emotions and adjust the tone and pitch of the speech based on the estimated child's emotions. For example, if the child is excited, it generates a cheerful and bright tone of speech. The voice changer unit provides the speech converted by the speech synthesis unit in a different voice for each character. For example, the voice changer unit sets a high-pitched voice to match the voice of an anime character. The voice changer unit can also estimate the child's emotions and adjust the voice changer settings based on the estimated child's emotions. For example, if the child is excited, it sets the voice changer settings to a cheerful and bright voice. The control unit controls each unit via the Internet. For example, the control unit changes the settings of a doll or stuffed animal through a smartphone app. The control unit can also add new conversation patterns and functions via the Internet. For example, it downloads and adds new conversation patterns via the Internet.As a result, the child-rearing support system according to the embodiment can support communication with children by recognizing what the child is saying, generating appropriate responses, and providing them in different voices for each character.

[0058] The voice recognition unit estimates the child's emotion and adjusts the accuracy of voice recognition based on the estimated emotion. The voice recognition unit estimates the child's emotion. The child's emotion includes, but is not limited to, joy, sadness, anger, etc. For example, when the child is excited, the voice recognition unit increases the sensitivity of voice recognition to accommodate a fast speaking speed. Furthermore, when the child is calm, the voice recognition unit can increase the accuracy of voice recognition to recognize subtle nuances. Furthermore, when the child is tired, the voice recognition unit can decrease the sensitivity of voice recognition to accommodate a slower speaking speed. This allows for more accurate recognition by adjusting the accuracy of voice recognition according to the child's emotion. Some or all of the above-described processing in the voice recognition unit may be performed using, or without, AI. For example, the voice recognition unit may input the child's emotion data into a generation AI and cause the generation AI to adjust the accuracy of voice recognition.

[0059] The voice recognition unit can change voice recognition parameters according to the child's speaking rate and volume. The voice recognition unit changes voice recognition parameters according to the child's speaking rate and volume. The speaking rate includes, for example, but is not limited to, the number of words per second and the number of syllables per second. The volume includes, for example, but is not limited to, decibels (dB). For example, when a child speaks quickly, the voice recognition unit can increase the voice recognition processing speed to enable real-time recognition. Furthermore, when a child speaks softly, the voice recognition unit can increase the voice recognition sensitivity to enable accurate recognition even at low volumes. Furthermore, when a child speaks loudly, the voice recognition unit can decrease the voice recognition sensitivity to enable recognition while reducing noise. In this way, various speaking situations can be accommodated by dynamically changing voice recognition parameters according to the child's speaking rate and volume. Some or all of the above-described processing in the voice recognition unit may be performed, for example, using AI or without AI. For example, the voice recognition unit can input the child's speaking rate and volume data into the generation AI and cause the generation AI to change the voice recognition parameters.

[0060] The voice recognition unit can prioritize recognition of specific keywords based on the child's speech. The voice recognition unit prioritizes recognition of specific keywords based on the child's speech. Specific keywords include, but are not limited to, frequently occurring words and important words. For example, if a child says "Mom," the voice recognition unit can prioritize recognition of the word and generate a related response. Furthermore, if a child says "Let's play," the voice recognition unit can prioritize recognition of the word and generate a response related to play. Furthermore, if a child says "I'm hungry," the voice recognition unit can prioritize recognition of the word and generate a response related to eating. Thus, by prioritizing recognition of specific keywords, it is possible to quickly respond to important speech content. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the voice recognition unit can input specific keyword data to a generation AI and cause the generation AI to prioritize keyword recognition.

[0061] The voice recognition unit can filter voice recognition according to the child's speaking environment. The voice recognition unit filters voice recognition according to the child's speaking environment. The speaking environment includes, but is not limited to, for example, background sound and noise level. For example, when the background sound is loud, the voice recognition unit strengthens noise cancellation to maintain voice recognition accuracy. Furthermore, in a quiet environment, the voice recognition unit can minimize filtering to recognize natural voices. Furthermore, when sudden noise occurs, the voice recognition unit can remove the noise and perform voice recognition. In this way, by filtering according to the speaking environment, voice recognition with the influence of noise minimized can be performed. Some or all of the above-described processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input speaking environment data to a generation AI and cause the generation AI to perform voice recognition filtering.

[0062] The natural language processing unit can estimate the child's emotions and adjust the tone and content of a response based on the estimated child's emotions. The natural language processing unit estimates the child's emotions. Examples of the child's emotions include, but are not limited to, joy, sadness, and anger. For example, if the child is excited, the natural language processing unit can generate a response with a cheerful and upbeat tone. If the child is calm, the natural language processing unit can also generate a response with a calm and gentle tone. If the child is sad, the natural language processing unit can also generate a response with a comforting tone. This enables more appropriate communication by generating a response with a tone and content that corresponds to the child's emotions. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the tone and content of the response.

[0063] The natural language processing unit can generate a response including an educational element based on the child's utterance. The natural language processing unit generates a response including an educational element based on the child's utterance. The educational element includes, but is not limited to, learning goals and educational content. For example, when a child asks, "Why is the sky blue?", the natural language processing unit can generate a response including a scientific explanation. Furthermore, when a child asks, "Why do I have to study?", the natural language processing unit can generate a response explaining the importance of learning. Furthermore, when a child asks, "How do I make friends?", the natural language processing unit can generate a response that teaches social skills. Thus, by generating a response including an educational element, it is possible to support the child's learning and development. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input educational element data into a generation AI and cause the generation AI to generate a response including an educational element.

[0064] The natural language processing unit can generate an individually customized response by referring to the child's speech history. The natural language processing unit generates an individually customized response by referring to the child's speech history. The speech history includes, for example, past speech content, interests, and concerns, but is not limited to these examples. The natural language processing unit generates a relevant response based on, for example, content that the child has spoken in the past. The natural language processing unit can also generate a customized response based on the child's interests and concerns. The natural language processing unit can also analyze the child's speech history and generate a response based on a specific pattern. In this way, an individually customized response can be generated by referring to the speech history. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input speech history data to a generation AI and cause the generation AI to generate a customized response.

[0065] The natural language processing unit can generate replies that support the progress of the story and game based on the content of the child's utterances. The natural language processing unit generates replies that support the progress of the story and game based on the content of the child's utterances. The progress of the story and game includes, but is not limited to, branching scenarios and game rules. For example, when a child says, "Tell me the story," the natural language processing unit can generate a continuation of the story. Furthermore, when a child says, "Let's play a game," the natural language processing unit can generate a replies that explain the rules and progress of the game. Furthermore, when a child asks, "What do we do next?" the natural language processing unit can generate a replies that explain the next step in the story or game. In this way, by generating replies that support the progress of the story or game, it is possible to keep the child interested. Some or all of the above-described processing in the natural language processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the natural language processing unit can input story and game progress data into a generation AI and cause the generation AI to generate replies that support the progress.

[0066] The voice synthesis unit can estimate the child's emotion and adjust the tone and pitch of the voice based on the estimated emotion. The voice synthesis unit estimates the child's emotion. The child's emotion includes, but is not limited to, joy, sadness, anger, etc. For example, if the child is excited, the voice synthesis unit can generate a voice with a lively and cheerful tone. If the child is calm, the voice synthesis unit can also generate a voice with a calm and gentle tone. If the child is sad, the voice synthesis unit can also generate a voice with a comforting tone. This enables more appropriate voice expression by generating a voice with a tone and pitch that corresponds to the child's emotion. Some or all of the above-described processing in the voice synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice synthesis unit can input the child's emotion data into a generation AI and cause the generation AI to adjust the tone and pitch of the voice.

[0067] The voice synthesis unit can select a character voice according to the child's preferences when synthesizing voice. The voice synthesis unit can select a character voice according to the child's preferences when synthesizing voice. Character voices include, but are not limited to, for example, the voices of anime characters, animals, and singers. For example, the voice synthesis unit can select the voice of an anime character that the child likes to generate voice. The voice synthesis unit can also select the voice of an animal that the child likes to generate voice. The voice synthesis unit can also select the voice of a singer that the child likes to generate voice. In this way, by selecting a character voice according to the child's preferences, it is possible to keep the child interested. Some or all of the above-described processing in the voice synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice synthesis unit can input character voice data to a generation AI and cause the generation AI to select a character voice.

[0068] The speech synthesis unit can adjust the speed and rhythm of the speech during speech synthesis according to the content of the child's speech. The speech synthesis unit can adjust the speed and rhythm of the speech during speech synthesis according to the content of the child's speech. Examples of the speed and rhythm of the speech include, but are not limited to, changing the tempo and selecting a rhythm pattern. For example, if the child speaks quickly, the speech synthesis unit can synthesize the speech at a faster speed. Furthermore, if the child speaks slowly, the speech synthesis unit can synthesize the speech at a slower speed. Furthermore, if the child speaks rhythmically, the speech synthesis unit can synthesize the speech by adjusting the rhythm of the speech. This enables more natural speech expression by generating speech with a speed and rhythm according to the content of the speech. Some or all of the above-described processing in the speech synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech synthesis unit can input speech content data to a generation AI and cause the generation AI to adjust the speed and rhythm of the speech.

[0069] The voice synthesis unit can add music and sound effects based on the child's speech during voice synthesis. The voice synthesis unit can add music and sound effects based on the child's speech during voice synthesis. Examples of music and sound effects include, but are not limited to, a music library and types of sound effects. For example, when a child says "sing me," the voice synthesis unit can add music to the voice synthesis to generate a song. Furthermore, when a child says "tell me a story," the voice synthesis unit can add sound effects to the voice synthesis to generate a story. Furthermore, when a child says "let's play," the voice synthesis unit can add game sound effects to the voice synthesis to create a playful atmosphere. By adding music and sound effects, more enjoyable voice expression is possible. Some or all of the above-described processing in the voice synthesis unit may be performed using, or without, AI. For example, the voice synthesis unit can input music and sound effect data to a generation AI and have the generation AI add music and sound effects.

[0070] The voice changer unit can estimate the child's emotions and adjust the voice changer settings based on the estimated child's emotions. The voice changer unit estimates the child's emotions. The child's emotions include, but are not limited to, joy, sadness, and anger. For example, if the child is excited, the voice changer unit can set the voice to a cheerful and bright voice. If the child is calm, the voice changer unit can also set the voice to a calm and gentle voice. If the child is sad, the voice changer unit can also set the voice to a comforting voice. This allows for more appropriate voice expression by adjusting the voice changer settings according to the child's emotions. Some or all of the above-described processing in the voice changer unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice changer unit can input the child's emotion data into a generation AI and have the generation AI adjust the voice changer settings.

[0071] The voice changer unit can set the voice tone and pitch for each character when using the voice changer. The voice changer unit can set the voice tone and pitch for each character when using the voice changer. The voice tone and pitch include, for example, voice characteristics and setting parameters for each character, but are not limited to these examples. For example, the voice changer unit can set a high-pitched voice to match the voice of an anime character. The voice changer unit can also set a low-pitched voice to match the voice of an animal character. The voice changer unit can also set a specific pitch to match the voice of a singer character. This allows for a wider variety of voice expression by setting different voice tones and pitches for each character. Some or all of the above-described processing in the voice changer unit can be performed using, or without, AI. For example, the voice changer unit can input character voice data into a generation AI and have the generation AI set the voice tone and pitch.

[0072] The voice changer unit can change the voice effects according to the child's speech when using the voice changer. The voice changer unit changes the voice effects according to the child's speech when using the voice changer. Voice effects include, but are not limited to, reverb, echo, and distortion. For example, when a child says, "Tell me a story," the voice changer unit can set a voice effect that matches the character in the story. Also, when a child says, "Sing," the voice changer unit can set a voice effect that matches the singer's character. Also, when a child says, "Let's play," the voice changer unit can set a voice effect that matches the character in the game. This enables a wider variety of voice expression by changing the voice effects according to the speech content. Some or all of the above-described processing in the voice changer unit may be performed using, for example, AI, or without AI. For example, the voice changer unit can input speech content data into a generation AI and have the generation AI change the voice effects.

[0073] The voice changer unit can customize the voice character according to the child's preferences when using the voice changer. The voice changer unit customizes the voice character according to the child's preferences when using the voice changer. Examples of voice characters include, but are not limited to, anime characters, animal voices, and singer voices. For example, the voice changer unit customizes the voice to the child's favorite anime character. The voice changer unit can also customize the voice to the child's favorite animal voice. The voice changer unit can also customize the voice to the child's favorite singer voice. This allows the voice character to be customized according to the child's preferences, thereby maintaining the child's interest. Some or all of the above-described processing in the voice changer unit may be performed using, or without, AI. For example, the voice changer unit can input character voice data to a generation AI and have the generation AI customize the voice character.

[0074] The control unit can estimate the child's emotions and adjust the operation of each unit based on the estimated child's emotions. The control unit estimates the child's emotions. Examples of the child's emotions include, but are not limited to, joy, sadness, and anger. For example, the control unit can increase the sensitivity of the voice recognition unit when the child is excited. The control unit can also soften the response of the natural language processing unit when the child is calm. The control unit can also soften the tone of the voice synthesis unit when the child is sad. This allows for more appropriate operation by adjusting the operation of each unit according to the child's emotions. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the child's emotion data into the generation AI and have the generation AI adjust the operation of each unit.

[0075] The control unit can instantly change the settings of each unit through the smartphone app. The control unit instantly changes the settings of each unit through the smartphone app. The settings of each unit include, but are not limited to, the sensitivity of the voice recognition unit, the response content of the natural language processing unit, and the tone and pitch of the voice synthesis unit. For example, the control unit adjusts the sensitivity of the voice recognition unit through the smartphone app. The control unit can also change the response content of the natural language processing unit through the smartphone app. The control unit can also adjust the tone and pitch of the voice synthesis unit through the smartphone app. This improves usability by changing the settings of each unit in real time through the smartphone app. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input setting change data from the smartphone app to the generation AI and cause the generation AI to change the settings of each unit.

[0076] The control unit can optimize the operation of each unit by referring to the child's speech history. The control unit optimizes the operation of each unit by referring to the child's speech history. The speech history includes, for example, past speech content, interests, and concerns, but is not limited to these examples. The control unit, for example, optimizes the sensitivity of the voice recognition unit based on the child's speech history. The control unit can also optimize the response content of the natural language processing unit based on the child's speech history. The control unit can also optimize the tone and pitch of the speech synthesis unit based on the child's speech history. In this way, the operation of each unit can be optimized by referring to the speech history. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input speech history data to a generation AI and cause the generation AI to optimize the operation of each unit.

[0077] The control unit can add new conversation patterns and functions via the Internet. The control unit can add new conversation patterns and functions via the Internet. New conversation patterns and functions include, but are not limited to, update procedures and descriptions of new functions. For example, the control unit can download and add new conversation patterns via the Internet. The control unit can also add new voice synthesis functions via the Internet. The control unit can also add new voice changer effects via the Internet. This improves the flexibility of the system by adding new conversation patterns and functions via the Internet. Some or all of the above-described processing in the control unit can be performed using AI, for example, or without AI. For example, the control unit can input new function addition data via the Internet to the generation AI and cause the generation AI to add new conversation patterns and functions.

[0078] The control unit can estimate the child's emotions and prioritize the operations of each unit based on the estimated child's emotions. The control unit estimates the child's emotions. The child's emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the child is excited, the control unit can adjust the sensitivity of the voice recognition unit with the highest priority. Also, if the child is calm, the control unit can adjust the response content of the natural language processing unit with the highest priority. Also, if the child is sad, the control unit can adjust the tone of the voice synthesis unit with the highest priority. This enables more appropriate operation by prioritizing the operations of each unit according to the child's emotions. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the child's emotion data to the generation AI and cause the generation AI to prioritize the operations of each unit.

[0079] The control unit can schedule the operation of each unit through the smartphone app. The control unit schedules the operation of each unit through the smartphone app. Scheduling the operation of each unit includes, but is not limited to, schedule setting and schedule change methods, for example. The control unit, for example, sets the operation schedule of the voice recognition unit through the smartphone app. The control unit can also set the response schedule of the natural language processing unit through the smartphone app. The control unit can also set the tone and pitch schedule of the voice synthesis unit through the smartphone app. This improves usability by scheduling the operation of each unit through the smartphone app. Some or all of the above-mentioned processing in the control unit may be performed using, or without using, AI, for example. For example, the control unit can input schedule data from the smartphone app to a generation AI and have the generation AI execute the operation schedule of each unit.

[0080] The control unit can customize the operation of each unit based on the content of the child's utterance. The control unit customizes the operation of each unit based on the content of the child's utterance. Customizing the operation of each unit can include, for example, customization options, setting procedures, etc., but is not limited to these examples. For example, the control unit customizes the sensitivity of the voice recognition unit based on the content of the child's utterance. The control unit can also customize the response content of the natural language processing unit based on the content of the child's utterance. The control unit can also customize the tone and pitch of the voice synthesis unit based on the content of the child's utterance. This allows for more appropriate operation by customizing the operation of each unit based on the content of the utterance. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input utterance content data to a generation AI and have the generation AI customize the operation of each unit.

[0081] The control unit can remotely monitor and control the operation of each unit via the Internet. The control unit remotely monitors and controls the operation of each unit via the Internet. Remote monitoring and control of the operation of each unit can be achieved, for example, by remote access methods, monitoring tools, and the like, but is not limited to these examples. The control unit, for example, remotely monitors the operation of the speech recognition unit via the Internet and adjusts it as needed. The control unit can also remotely monitor the response content of the natural language processing unit via the Internet and adjust it as needed. The control unit can also remotely monitor the tone and pitch of the speech synthesis unit via the Internet and adjust it as needed. This remote monitoring and control of the operation of each unit via the Internet improves the flexibility of the system. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or without AI. For example, the control unit can input remote monitoring and control data to the generation AI and have the generation AI monitor and control the operation of each unit. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned voice recognition unit, natural language processing unit, voice synthesis unit, voice changer unit, and control unit, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the voice recognition unit recognizes a child's words using the microphone 38B of the smart device 14 and executes voice recognition technology using the control unit 46A. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response based on the recognized words. The voice synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and converts the generated response into voice. The voice changer unit is realized by the control unit 46A of the smart device 14 and provides a different voice for each character. The control unit is realized by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12 and controls each unit via the Internet. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned voice recognition unit, natural language processing unit, voice synthesis unit, voice changer unit, and control unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition unit recognizes a child's words using the microphone 238 of the smart glasses 214 and executes voice recognition technology using the control unit 46A. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response based on the recognized words. The voice synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and converts the generated response into voice. The voice changer unit is realized by the control unit 46A of the smart glasses 214 and provides a different voice for each character. The control unit is realized by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12 and controls each unit via the Internet. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned voice recognition unit, natural language processing unit, voice synthesis unit, voice changer unit, and control unit, is realized, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the voice recognition unit recognizes a child's words using the microphone 238 of the headset-type terminal 314 and executes voice recognition technology using the control unit 46A. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response based on the recognized words. The voice synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and converts the generated response into voice. The voice changer unit is realized by the control unit 46A of the headset-type terminal 314 and provides a different voice for each character. The control unit is realized by the control unit 46A of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12 and controls each unit via the Internet. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned voice recognition unit, natural language processing unit, voice synthesis unit, voice changer unit, and control unit, is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the voice recognition unit recognizes the child's words using the microphone 238 of the robot 414 and executes voice recognition technology using the control unit 46A. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate response based on the recognized words. The voice synthesis unit is realized by the specific processing unit 290 of the data processing device 12 and converts the generated response into voice. The voice changer unit is realized by the control unit 46A of the robot 414 and provides a different voice for each character. The control unit is realized by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12 and controls each unit via the Internet.

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

[0083] The child-rearing support system may further include a health management unit that monitors the child's health condition. The health management unit may use sensors to measure the child's body temperature, heart rate, activity level, etc., and evaluate the child's health condition based on this data. For example, if the child has a fever, the health management unit may send a notification to the parent. If the child's activity level is low, the health management unit may also generate a message encouraging the parent to exercise. Furthermore, the health management unit may monitor the child's sleep patterns and provide advice on providing an appropriate sleeping environment. In this way, the child-rearing support system can support the child's health management and reduce the burden on parents.

[0084] The child-rearing support system can further include a learning management unit that manages a child's learning progress. The learning management unit, for example, records the content and progress of a child's learning and creates a learning plan based on this data. For example, if a child has difficulty in a particular subject, the learning management unit can advise the child to focus on studying that part. The learning management unit can also provide learning materials that match the child's learning style. Furthermore, the learning management unit can provide a learning program that incorporates game elements to increase a child's motivation to learn. In this way, the child-rearing support system can support children's learning and provide an effective learning environment.

[0085] The child-rearing support system may further include a social interaction unit for fostering a child's social skills. The social interaction unit may, for example, provide a platform for a child to safely communicate with other children. For example, the social interaction unit may provide games or activities that a child can play with friends online. The social interaction unit may also provide projects for a child to cooperate with other children to solve problems. The social interaction unit may also provide exercises that allow a child to empathize with other children. In this way, the child-rearing support system may support the development of a child's social skills.

[0086] The child-rearing support system can further include a creative support unit for fostering children's creativity. The creative support unit provides, for example, tools for children to draw pictures and create stories. For example, it can digitize pictures drawn by children and turn them into animations. The creative support unit can also read aloud stories created by children using voice synthesis technology. Furthermore, the creative support unit can provide a platform for children to share their creations with other children. In this way, the child-rearing support system can support the development of children's creativity.

[0087] The child-rearing support system may further include a security management unit to ensure the safety of children. The security management unit may, for example, provide a filtering function to allow children to use the Internet safely. For example, if a child attempts to access inappropriate content, the security management unit may block that access. The security management unit may also monitor a child's online interactions and send a notification to parents if inappropriate behavior is detected. Furthermore, the security management unit may track a child's location information and check whether the child is within a safe range. This allows the child-rearing support system to ensure the safety of children and provide parents with peace of mind.

[0088] The child-rearing support system may further include a music providing unit that estimates a child's emotions and provides appropriate music based on the estimated emotions. For example, the music providing unit may play comforting music when the child is sad. Also, the music providing unit may play encouraging music when the child is excited. Furthermore, the music providing unit may play calming music when the child is relaxed. In this way, the child-rearing support system can provide music according to the child's emotions and support emotional regulation.

[0089] The child-rearing support system can further include an activity suggestion unit that estimates a child's emotions and suggests appropriate activities based on the estimated emotions. For example, if a child is bored, the activity suggestion unit can suggest a new game or learning activity. Also, if a child is feeling stressed, the activity suggestion unit can suggest a relaxing activity. Furthermore, if a child is excited, the activity suggestion unit can suggest an activity to release energy. In this way, the child-rearing support system can suggest activities according to a child's emotions and support emotional regulation.

[0090] The child-rearing support system may further include a storytelling unit that estimates a child's emotions and tells an appropriate story based on the estimated emotions. For example, the storytelling unit may tell a comforting story if the child is sad. Alternatively, the storytelling unit may tell an adventurous story if the child is excited. Furthermore, the storytelling unit may tell a calming story if the child is relaxed. In this way, the child-rearing support system can provide a story according to the child's emotions and support emotional regulation.

[0091] The child-rearing support system can further include a feedback unit that estimates the child's emotions and provides appropriate feedback based on the estimated emotions. For example, the feedback unit can provide praising feedback when the child succeeds. Also, it can provide encouraging feedback when the child fails. Furthermore, the feedback unit can provide encouraging feedback when the child is challenging themselves. In this way, the child-rearing support system can provide feedback according to the child's emotions and support emotional regulation.

[0092] The child-rearing support system may further include an advice unit that estimates a child's emotions and provides appropriate advice based on the estimated emotions. For example, the advice unit may provide advice suggesting a solution when a child is in trouble. Also, the advice unit may provide advice showing options when a child is unsure. Furthermore, the advice unit may provide encouraging advice when a child is challenging themselves. In this way, the child-rearing support system can provide advice according to a child's emotions and support emotional regulation.

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

[0094] Step 1: The voice recognition unit recognizes the child's words. The child's words may include, but are not limited to, age groups, languages, and speech styles. For example, when a child speaks, the voice recognition unit uses voice recognition technology to understand the words. The voice recognition unit can also dynamically change voice recognition parameters depending on the child's speaking speed and volume. For example, if a child speaks quickly, the voice recognition processing speed can be increased to enable real-time recognition. Step 2: The natural language processing unit generates an appropriate response based on the words recognized by the speech recognition unit. Responses include, but are not limited to, voice messages, text messages, etc. For example, if a child asks, "Why is the sky blue?", the natural language processing unit generates a response including a scientific explanation. The natural language processing unit can also estimate the child's emotions and adjust the tone and content of the response based on the estimated child's emotions. For example, if the child is excited, the natural language processing unit generates a response with a cheerful and upbeat tone. Step 3: The speech synthesis unit converts the response generated by the natural language processing unit into speech. The speech synthesis unit converts the response into speech, for example, using a generation AI. The speech synthesis unit can also estimate the child's emotions and adjust the tone and pitch of the speech based on the estimated child's emotions. For example, if the child is excited, it generates a cheerful and bright tone of speech. Step 4: The voice changer unit provides the voice converted by the voice synthesis unit in a different voice for each character. For example, the voice changer unit sets a high-pitched voice to match the voice of an anime character. The voice changer unit can also estimate the child's emotions and adjust the voice changer settings based on the estimated child's emotions. For example, if the child is excited, the voice changer unit sets a lively and cheerful voice. Step 5: The control unit controls each unit via the Internet. For example, the control unit can change the settings of the doll or stuffed animal via a smartphone app. The control unit can also add new conversation patterns and functions via the Internet. For example, it can download and add new conversation patterns via the Internet.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a speech recognition unit that recognizes children's words; a natural language processing unit that generates a response based on the words recognized by the speech recognition unit; a speech synthesis unit that converts the response generated by the natural language processing unit into speech; a voice changer unit that changes the voice converted by the voice synthesis unit for each character; a control unit that controls each of the units via the Internet; A system characterized by:

2. The voice recognition unit Estimate the child's emotions and adjust the accuracy of speech recognition based on the estimated emotions.

2. The system of claim 1.

3. The voice recognition unit Change speech recognition parameters according to the child's speaking rate and volume 2. The system of claim 1.

4. The voice recognition unit Prioritize recognition of specific keywords based on the child's speech 2. The system of claim 1.

5. The voice recognition unit Filtering speech recognition according to the child's speaking environment 2. The system of claim 1.

6. The natural language processing unit Estimate the child's feelings and adjust the tone and content of your response based on those feelings 2. The system of claim 1.

7. The natural language processing unit Generate educational responses based on what the child says 2. The system of claim 1.

8. The natural language processing unit References the child's speech history to generate personalized responses 2. The system of claim 1.

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