System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a question asked by a child as audio; means for converting the received audio into textual data; means for transmitting the converted textual data to a server; means for the server to analyze the textual data and generate an appropriate answer using a generative AI model; means for converting the generated answer into audio; and means for the server to play the audio and provide the answer to the child.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the past, parents raising children had to frequently respond to their children's "why?" questions, which placed a great burden and stress on them. In particular, during the "why-why" stage that begins around the age of two, children continue to ask endless questions, draining parents' time and mental energy. This problem can also have a negative impact on parent-child relationships. The present invention aims to solve these problems and improve relationships between parents and children. [Means for solving the problem]

[0005] The present invention provides a terminal that receives questions posed by children as voice data and converts them into text data, and a means for transmitting this text data to a server. The server analyzes the received text data and generates an appropriate answer using a generative AI model. The generated answer is converted into voice data and sent back to the terminal. The terminal plays the voice data and provides the answer to the child. The server then generates the optimal answer based on the child's age and level of understanding, and the terminal uses a machine learning algorithm to continuously evolve the received voice data and improve response accuracy. This reduces the burden and stress on parents and satisfies children's intellectual curiosity.

[0006] "Audio data" is digital data that records questions uttered by children in audio format.

[0007] "Text data" is digital data that is generated by analyzing voice data and converting it into text information.

[0008] A "terminal" is a device that has the function of receiving voice data, converting it into text data, and receiving and playing voice data from a server. Specifically, it refers to a doll-shaped device.

[0009] A "server" is a computer system that has the function of receiving text data sent from a terminal via a network, generating an appropriate response using an analytical and generative AI model, and sending it back to the terminal as voice data.

[0010] A "generative AI model" is an artificial intelligence algorithm that analyzes text data and generates optimal answers based on the child's age and level of understanding.

[0011] "Speech recognition software" is a program that analyzes received voice data and converts it into text data.

[0012] A "Text-To-Speech (TTS) engine" is a program that converts generated text data into voice data.

[0013] A "machine learning algorithm" is a data processing technology that sequentially evolves the voice data received by the device to improve response accuracy.

[0014] "Intellectual curiosity" refers to a child's desire to learn and know, and the behavior of asking various questions based on that desire. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. The processing flow of the entire system and a specific example are shown below.

[0037] System configuration

[0038] The system mainly consists of the following components:

[0039] 1. Terminal (doll-like device):

[0040] It receives voice data uttered by the child and converts it into text data.

[0041] The audio data sent from the server is played back to provide the answer to the user (child).

[0042] 2. Server:

[0043] Receives and analyzes text data sent from the device.

[0044] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[0045] The converted audio data is sent to the terminal.

[0046] 3. Generative AI Model:

[0047] The text data is analyzed to generate the most appropriate answer based on the child's age and level of understanding.

[0048] Specific examples for carrying out the invention

[0049] Below is a concrete example of how the system responds to a child's question, "Why?"

[0050] Example 1: When a child asks, "Why is the sky blue?"

[0051] 1. User (child): Asks the doll-like device, "Why is the sky blue?"

[0052] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[0053] 3. The device sends the converted text data to the server.

[0054] 4. The server analyzes the received text data and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0055] 5. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[0056] 6. The device plays back the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0057] Example 2: When a child asks, "Why are leaves green?"

[0058] 1. User (child): Asks the doll-like device, "Why are leaves green?"

[0059] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[0060] 3. The device sends the converted text data to the server.

[0061] 4. The server analyzes the text data and uses a generative AI model to generate the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0062] 5. The server converts the generated answer into audio data and sends it to the device.

[0063] 6. The device plays back the received audio data and provides the user (child) with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0064] In this way, the system of the present invention satisfies children's intellectual curiosity by providing quick and accurate answers to their "why?" questions, while significantly reducing the burden on parents. The entire system processes information with high accuracy through automated processes, and is designed to function as a good partner in the home environment.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user (child) speaks a question into the doll-shaped terminal.

[0068] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[0069] Step 2:

[0070] The voice data received by the terminal is converted into text data.

[0071] Specific operation: The voice recognition software in the device converts the voice data into text data and generates the text information "Why is the sky blue?"

[0072] Step 3:

[0073] The terminal transmits the converted text data to the server.

[0074] Specific operation: The device uses an HTTP request to upload the converted text data to the server.

[0075] Step 4:

[0076] The server analyzes the received text data.

[0077] Specific operation: The server analyzes the text data "Why is the sky blue?" and inputs it into the generative AI model.

[0078] Step 5:

[0079] The generation AI on the server generates appropriate answers.

[0080] Specific behavior: The generative AI model generates the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light."

[0081] Step 6:

[0082] The server converts the generated response into audio data.

[0083] Specific operation: A TTS (Text-To-Speech) engine that converts text to speech converts the generated response into audio data.

[0084] Step 7:

[0085] The server transmits the generated voice data to the terminal.

[0086] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[0087] Step 8:

[0088] The terminal plays back the received audio data.

[0089] Specific operation: The speaker built into the device plays the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0090] In this way, through a series of processing steps, quick and friendly answers are provided to children's questions, reducing the burden on parents and satisfying children's intellectual curiosity.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] The present invention aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing prompt and appropriate answers to questions posed by children. However, conventional systems often lack the accuracy of voice data recognition and the appropriateness of answers, resulting in unsatisfactory results. It has also been difficult to generate answers that correspond to each child's level of understanding and age.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for receiving questions posed by the child as voice data, means for converting the received voice data into text data, means for transmitting the converted text data to the server, means for the server to analyze the text data and generate an appropriate answer using a generative AI model, means for converting the generated answer into voice data, means for transmitting the generated voice data to the terminal, means for the terminal to play the voice data and provide the answer to the child, means for the terminal to use a high-sensitivity microphone and voice recognition software when performing voice recognition and converting the voice data into text data, and means for the server to input a prompt sentence into the generative AI model and generate an answer. This improves the accuracy of voice recognition and the appropriateness of the answer, making it possible to provide a high-level answer according to the child's age and level of understanding.

[0096] "Means for receiving questions posed by children as voice data" refers to a system for acquiring questions posed by children verbally as digital voice data via a voice input device such as a microphone.

[0097] The "means for converting received voice data into text data" refers to a process in which voice data is analyzed using voice recognition software and converted into text data as a string of characters.

[0098] The "means for transmitting the converted text data to the server" is a method for transmitting the text data from the terminal to the server using a communication protocol (for example, HTTP or HTTPS).

[0099] "Means for the server to analyze text data and generate an appropriate answer using a generative AI model" refers to a mechanism in which received text data is processed by an analytical algorithm on the server, and an AI model (e.g., a generative model) is used to generate the optimal answer corresponding to the text data.

[0100] The "means for converting the generated answer into voice data" refers to a process for converting the generated text-based answer into voice data that can be heard by the user using a voice synthesis engine or the like.

[0101] The "means for transmitting generated voice data to the terminal" refers to a method for transferring voice data generated on the server to the terminal via a communication channel.

[0102] "Means for the device to play audio data and provide answers to the child" refers to a mechanism that uses a speaker or other audio output device built into the device to play audio data and provide verbal answers to the user (child).

[0103] "Means for a device to use a high-sensitivity microphone and speech recognition software to perform speech recognition and convert speech data into text data" refers to the process of using a high-sensitivity microphone to capture speech in detail and accurately, and then converting that speech data into text data using speech recognition software (e.g., a speech recognition engine).

[0104] "Means in which the server inputs a prompt sentence into a generative AI model to generate an answer" refers to the process in which the server inputs a prompt sentence into a generative AI model (e.g., a text generation model) to generate an appropriate answer in natural language.

[0105] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. A detailed description of the specific invention is provided below.

[0106] System configuration

[0107] This system consists of the following hardware and software:

[0108] 1. Terminal (doll-shaped device)

[0109] Hardware: High-sensitivity microphone, speaker, communication module.

[0110] Software: Install voice recognition software (e.g., Google Speech-to-Text) to convert received voice data into text data.

[0111] Function: Receives voices uttered by the child and converts them into text data. It also plays back the voice data sent from the server and provides answers to the child.

[0112] 2. Server

[0113] Hardware: A server machine equipped with a high-performance processor.

[0114] Software: Powered by generative AI models (e.g., OpenAI GPT-3) and speech synthesis engines (e.g., Amazon Polly).

[0115] Function: Analyzes text data received from the device and generates appropriate answers using a generative AI model. The generated answers are then converted into voice data and sent to the device.

[0116] 3. Generative AI Models

[0117] Function: Runs on the server, analyzes text data, and generates the most appropriate answer based on the child's age and level of understanding.

[0118] Examples of specific examples and prompts

[0119] Example 1: When a child asks, "Why is the sky blue?"

[0120] 1. The user (child) asks the doll-shaped device, "Why is the sky blue?"

[0121] 2. The device receives the voice and converts it into text data, such as "Why is the sky blue?", using a high-sensitivity microphone and Google Speech-to-Text software.

[0122] 3. The device sends the converted text data to the server.

[0123] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[0124] Your child asks, "Why is the sky blue?" He is 5 years old. Explain it gently and simply so that he can understand.

[0125] 5. The server receives the answer generated by the AI ​​model, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light," and converts it into speech data using Amazon Polly.

[0126] 6. The server sends the generated voice data to the device.

[0127] 7. The device plays back the received audio data and provides the child with the answer, "The sky appears blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0128] Example 2: When a child asks, "Why are leaves green?"

[0129] 1. The user (child) asks the doll-shaped device, "Why are leaves green?"

[0130] 2. The device receives the voice and converts it into text data, such as "Why are leaves green?", using a sensitive microphone and Google Speech-to-Text software.

[0131] 3. The device sends the converted text data to the server.

[0132] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[0133] Your child asks, "Why are leaves green?" He is 7 years old. Explain it gently and simply so that he can understand.

[0134] 5. The server receives the answer generated by the AI ​​model, "Leaves are green because they contain a pigment called chlorophyll," and converts it into speech data using Amazon Polly.

[0135] 6. The server sends the generated voice data to the device.

[0136] 7. The device plays back the received audio data and provides the child with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0137] In this way, the present invention integrates advanced technologies such as speech recognition, text generation, and speech synthesis to provide quick and accurate answers to children's questions, thereby satisfying children's intellectual curiosity while reducing the burden on parents.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1: The user (child) inputs a question as voice data

[0140] The user (child) asks a question to the doll-shaped device. For example, the child might ask, "Why is the sky blue?" This voice question is received by the device's high-sensitivity microphone.

[0141] Input: A child-uttered vocal question (e.g., "Why is the sky blue?").

[0142] Output: The question as audio data.

[0143] Step 2: Your device converts the audio data into text.

[0144] The device uses built-in voice recognition software (e.g., Google Speech-to-Text) to analyze the received voice data and convert it into text data. Specifically, the voice signal is captured as digital data and converted into a string of characters.

[0145] Input: A question as audio data (e.g., "Why is the sky blue?").

[0146] Output: The question as text data (e.g., "Why is the sky blue?").

[0147] Step 3: The device sends the text data to the server

[0148] The terminal sends the converted text data to the server via an internal network module, using the HTTP or HTTPS protocol.

[0149] Input: A question as text data (e.g., "Why is the sky blue?").

[0150] Output: The text data sent to the server.

[0151] Step 4: The server analyzes the text data and generates an answer using a generative AI model

[0152] The server analyzes the received text data and generates an answer by inputting an appropriate prompt into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt might be, "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it kindly and simply so that a child can understand." The generative AI model generates an answer based on this prompt.

[0153] Input: A question as text data (e.g., "Why is the sky blue?") and a prompt for the generative AI model (e.g., "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it gently and simply so that he can understand.").

[0154] Output: Correct answer (e.g. "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering the blue light").

[0155] Step 5: The server converts the generated answer into audio data.

[0156] The server uses a speech synthesis engine (e.g., Amazon Polly) to convert the generated text response into audio data, which is then converted into audio data (e.g., an MP3 file).

[0157] Input: Answer as text data (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering blue light").

[0158] Output: Answer as audio data (e.g. audio file).

[0159] Step 6: The server sends the audio data to the device

[0160] The server then sends the generated audio data to the device via the network, using the HTTP or HTTPS protocol.

[0161] Input: Answer as audio data (e.g. audio file).

[0162] Output: The audio data sent to the device.

[0163] Step 7: The device plays the audio data and provides it to the user (child).

[0164] The device uses a built-in speaker to play back the received voice data, allowing the user (child) to hear the answer aloud.

[0165] Input: Answer as audio data (e.g. audio file).

[0166] Output: Audio output that the user (child) can hear (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere and scatters blue light").

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] Physical stores, especially educational museums and science museums, require quick and accurate answers to visitors' questions, especially children's. However, conventional guidance systems require staff with specialized knowledge to be on-site at all times, resulting in increased labor costs and delayed responses due to staff shortages. Furthermore, answers may not be consistent or accurate, making it difficult to improve visitor satisfaction. The present invention aims to solve these problems and improve visitor experience by automatically providing highly accurate answers to questions.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes means for receiving visitor questions as voice data, means for converting the received voice data into text data, means for analyzing the converted text data and generating appropriate answers using a generative AI model, means for converting the generated answers into voice data, means for transmitting the generated voice data to a terminal and playing it back, and means for generating appropriate answers to visitor questions in a physical store and providing voice guidance. This reduces the burden on staff and makes it possible to quickly provide consistent, highly accurate answers.

[0172] "Children" or "visitors" are people who use the system to request information in specific situations, and are further subdivided based on factors such as age and whether or not they have specialized knowledge.

[0173] "Audio data" means audio signals in digital form obtained from human speech or other audio sources.

[0174] "Text data" is information of a character string obtained by analyzing voice data, and is data that includes questions and instructions.

[0175] A "server" is a computing device that processes and analyzes voice and text data and generates a response.

[0176] A "generative AI model" is a model of an artificial intelligence algorithm that analyzes received text data and generates the optimal answer to a question.

[0177] An "appropriate answer" is accurate information that matches the questioner's understanding, based on the results of analysis by the generative AI model.

[0178] A "terminal" is a device for transmitting and receiving voice data and reproducing generated responses.

[0179] "Brick and mortar stores" are physical stores or facilities that visitors actually visit, including educational facilities such as museums and science centers.

[0180] This invention includes a system for providing appropriate answers to questions of visitors in a physical store by voice. The configuration and processing of the entire system will be described in detail below.

[0181] System configuration

[0182] 1. Terminal (guide robot or information kiosk):

[0183] Receives visitor voice data using a microphone

[0184] Converts received voice data into text data using voice recognition software

[0185] Send the converted text data to the server

[0186] Play audio data received from the server on the speaker

[0187] 2. Server:

[0188] Receive and analyze text data sent from the device

[0189] Generate appropriate answers using a generative AI model (e.g., GPT-4)

[0190] Use a Text-to-Speech (TTS) engine to convert the generated answers into audio data.

[0191] Send the generated audio data to the device

[0192] 3. Generative AI Model:

[0193] Analyze text data and generate the most appropriate answer based on the visitor's age and level of understanding

[0194] For example, in response to the question "What is the name of this dinosaur?", it generates the answer "The name of this dinosaur is Tyrannosaurus."

[0195] Hardware and software used

[0196] Hardware:

[0197] Microphone: Receives the visitor's voice

[0198] Speaker: Outputs the generated audio data

[0199] Terminals (guide robots and information kiosks): Act as interfaces

[0200] software:

[0201] speech_recognition: A library that converts speech data into text data

[0202] TextToSpeech: An engine that converts text data into speech data

[0203] AnswerGenerator: Software that analyzes text data using a generative AI model (e.g., GPT-4) and generates answers.

[0204] Specific examples

[0205] Example 1: Dinosaur exhibits in museums

[0206] Question: "What is the name of this dinosaur?"

[0207] Answer: "The name of this dinosaur is Tyrannosaurus."

[0208] Example prompt sentence:

[0209] User input: "What is the name of this dinosaur?"

[0210] Constructing an Answer: The name of this dinosaur is Tyrannosaurus.

[0211] Example 2: Space exhibits at science museums

[0212] Question: "When was the launch date for Apollo 11?"

[0213] Answer: "The launch date of Apollo 11 was July 16, 1969."

[0214] Example prompt sentence:

[0215] User Input: "What is the launch date for Apollo 11?"

[0216] Constructing an Answer: The launch date of Apollo 11 was July 16, 1969.

[0217] In this way, the entire system can provide quick and accurate answers to visitor questions, reducing the burden on staff and enabling them to provide consistent, accurate answers.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] A user asks a question to a terminal (a guide robot or an information kiosk) installed in a physical store. At this time, a microphone built into the terminal receives voice data. The input here is the user's speech, and the output is the captured voice data.

[0221] Step 2:

[0222] The device processes the received voice data with speech recognition software and converts it into text data. This process uses a speech recognition library (e.g., speech_recognition). The input is voice data, and the output is text data.

[0223] Step 3:

[0224] The terminal transmits the converted text data to the server. The data transfer here is performed via network communication. The input is the text data, and the output is the text data transmitted to the server.

[0225] Step 4:

[0226] The server receives and analyzes the text data sent from the device. A generative AI model (e.g., GPT-4) is used for the analysis to generate the optimal answer based on the visitor's age and level of understanding. The input is text data, and the output is the generated answer text.

[0227] Step 5:

[0228] The server uses a TTS (Text-to-Speech) engine to convert the generated answer text data into voice data. The input is the answer text data, and the output is voice data.

[0229] Step 6:

[0230] The server transmits the generated voice data to the terminal. Again, data transfer is performed via network communication. The input is the voice data, and the output is the voice data transmitted to the terminal.

[0231] Step 7:

[0232] The device plays the received voice data and provides the answer to the user. This process uses the device's built-in speaker. The input is the voice data, and the output is the answer played back as voice.

[0233] Step 8:

[0234] The device accumulates and analyzes the received voice data and user responses, and applies machine learning algorithms to improve response accuracy in the future. The input is past voice data and user feedback, and the output is an improved voice recognition model.

[0235] These processing steps result in a system that automatically and accurately answers a series of questions.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] This system aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to children's questions such as "Why?" This system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts responses accordingly.

[0238] System configuration

[0239] The system mainly consists of the following components:

[0240] 1. Terminal (doll-like device):

[0241] It receives voice data uttered by the child and converts it into text data.

[0242] The audio data sent from the server is played back to provide the answer to the user (child).

[0243] 2. Server:

[0244] Receives and analyzes text data and emotion data sent from the device.

[0245] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[0246] The converted audio data is sent to the terminal.

[0247] 3. Generative AI Model:

[0248] It analyzes text data and emotional data to generate the most appropriate answer based on the child's age, level of understanding, and emotions.

[0249] 4. Emotion Engine:

[0250] The user's emotions are analyzed from voice and text data and fed back to the generative AI model as emotional data.

[0251] Specific examples for carrying out the invention

[0252] Below we show the specific behavior of the system in response to a child's "why?" question and their emotions.

[0253] Example 1: If a child asks, in an anxious voice, "Why is the sky blue?"

[0254] 1. User (child): "Why is the sky blue?" asks the doll-like device in an anxious voice.

[0255] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[0256] 3. The device uses an emotion engine to extract emotion data, such as anxiety, from the voice.

[0257] 4. The device sends the converted text data and emotion data to the server.

[0258] 5. The server analyzes the received text data and the emotional data of anxiety, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is completely natural."

[0259] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[0260] 7. The device plays back the received voice data and provides the user (child) with a gentle answer: "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[0261] Example 2: When a child asks, "Why are leaves green?" in an inquisitive voice.

[0262] 1. User (child): "Why are leaves green?" asks the doll-like device in an inquisitive voice.

[0263] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[0264] 3. The device uses an emotion engine to extract emotion data such as interest from the voice.

[0265] 4. The device sends the converted text data and emotion data to the server.

[0266] 5. The server analyzes the received text data and emotional interest data, and uses a generative AI model to generate an answer such as, "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things to know!"

[0267] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[0268] 7. The device plays back the received audio data and provides the user (child) with an answer in an intriguing tone: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things too!"

[0269] In this way, the system of the present invention adjusts responses to children's "why?" questions by taking emotions into account, enabling more friendly and effective communication. This reduces the burden on parents and satisfies children's intellectual curiosity. The entire system is configured to function as a good partner in the home environment by integrating various components, including the emotion engine.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The user (child) speaks a question into the doll-shaped terminal.

[0273] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[0274] Step 2:

[0275] The voice data received by the terminal is converted into text data.

[0276] Specific operation: The voice recognition software in the device converts the voice data into text data such as "Why is the sky blue?"

[0277] Step 3:

[0278] The device uses an emotion engine to extract emotional data from the child's voice.

[0279] Specific operation: The emotion engine analyzes voice data and recognizes the child's emotions, such as anxiety or interest.

[0280] Step 4:

[0281] The terminal transmits the converted text data and emotion data to the server.

[0282] Specific operation: The device uses an HTTP request to upload text data and emotion data to the server.

[0283] Step 5:

[0284] The server analyzes the received text data and emotion data.

[0285] Specific operation: The server analyzes the text data "Why is the sky blue?" and the emotion data "anxiety."

[0286] Step 6:

[0287] The generative AI on the server generates appropriate answers based on text data and emotional data.

[0288] What it does: The generative AI model generates the answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[0289] Step 7:

[0290] The server converts the generated response into audio data.

[0291] Specific operation: The TTS (Text-To-Speech) engine in the server converts the generated answer into voice data.

[0292] Step 8:

[0293] The server transmits the generated voice data to the terminal.

[0294] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[0295] Step 9:

[0296] The terminal plays back the received audio data.

[0297] What it does: The device's built-in speaker plays the received audio data. The answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing," is delivered in a gentle tone to the anxious child.

[0298] In this way, by incorporating emotional data, the system can provide answers that are in tune with the child's feelings, resulting in more friendly and effective communication, which reduces the burden on parents and satisfies the child's intellectual curiosity.

[0299] Example 2

[0300] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] It takes a lot of time and effort for parents to quickly and appropriately answer the "why?" questions that children frequently ask. Furthermore, it is extremely difficult to provide answers that reflect the child's emotions. Therefore, there is a need to develop a system that can satisfy children's intellectual curiosity while reducing the burden on parents. Another challenge is to improve the accuracy of responses to similar questions.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0303] In this invention, the server includes means for receiving questions posed by children as voice data, means for converting the received voice data into text data, and means for extracting the converted text data and emotion data. This makes it possible to generate and provide appropriate answers to questions posed by children quickly and taking emotion into consideration. Furthermore, by including means for successively evolving the voice data and emotion data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards, the quality of answers to similar questions can be continuously improved.

[0304] "Children" refers to young people who use the system and ask questions aloud.

[0305] A "question" refers to a verbal expression of a child's doubts.

[0306] "Audio data" refers to information that has been recorded as sound in response to questions uttered by children and converted into digital format.

[0307] "Text data" refers to digital data in which voice data is converted into text information using voice recognition technology.

[0308] "Server" refers to a computer system that receives and analyzes data over a network and generates appropriate answers using a generative AI model.

[0309] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers from given input data based on a pre-trained algorithm.

[0310] "Emotional data" refers to data that is generated by analyzing emotions from the user's tone of voice, words, etc., and then expressing the results in digital form.

[0311] "Speech recognition software" refers to software that receives voice data as input and converts that voice into text data.

[0312] An "emotion engine" refers to a system that analyzes user emotions from voice data and feeds this back to a generative AI model as emotional data.

[0313] "Audio data transmission means" refers to a communication means for transmitting audio data generated by the server to the terminal.

[0314] A "machine learning algorithm" is an algorithm that allows the system to self-learn based on past data and improve the accuracy of responses from the next time onwards.

[0315] This invention is a system that aims to reduce the burden on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to the "why?" questions that children often ask on a daily basis. The system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts the response accordingly. This system mainly consists of the following components:

[0316] System Configuration

[0317] 1. Terminal (doll-like device):

[0318] The system receives the voice data uttered by the child and converts it into text data. Specifically, the device's built-in microphone captures the voice and converts it into text data using voice recognition software (e.g., Google Speech-to-Text API). Furthermore, it uses an emotion engine to extract emotional data from the voice and sends it to the server.

[0319] The audio data sent from the server is played back to provide the answer to the user (child). Specifically, the audio data received by the device's built-in speaker is played back (e.g., MP3 format data generated using the Google Text-to-Speech API).

[0320] 2. Server:

[0321] The server receives and analyzes the text data and emotion data sent from the device. Specifically, the server analyzes the received JSON data and extracts the text data and emotion data.

[0322] A generative AI model is used to generate an appropriate response, which is then converted into voice data. Specifically, the server uses a generative AI model (e.g., OpenAI GPT-4) to input a prompt and generate an appropriate text response. The response is then converted into voice data using the Google Text-to-Speech API and sent to the device.

[0323] The converted audio data is sent to the device. Specifically, the audio data (MP3 format) is sent to the device via Wi-Fi.

[0324] 3. Generative AI Model:

[0325] It analyzes text and emotion data to generate the most appropriate answer based on the child's age, level of understanding, and emotions. Example prompt: "The sky appears blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a completely natural process."

[0326] Specific examples

[0327] 1. Example 1: When a child asks, in an anxious voice, "Why is the sky blue?"

[0328] When a user (child) asks "Why is the sky blue?" in an anxious voice to the doll-shaped device, the device receives the voice and converts it into text data, "Why is the sky blue?" using speech recognition software. The device then uses an emotion engine to extract emotion data, such as anxiety, from the voice and sends the converted text data and emotion data to the server. The server then analyzes the received text data and emotion data, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing." The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in a gentle tone.

[0329] 2. Example 2: When a child asks, in an inquisitive voice, "Why are leaves green?"

[0330] When a user (child) asks the doll-shaped device in an inquisitive voice, "Why are leaves green?", the device receives the voice and uses speech recognition software to convert it into text data: "Why are leaves green?". The device then uses an emotion engine to extract emotional interest data from the voice and sends the converted text data and emotional data to the server. The server analyzes the received text data and emotional interest data and uses a generative AI model to generate an answer: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things about them!" The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in an inquisitive tone.

[0331] As described above, this invention aims to effectively utilize the emotion engine and generative AI model to function as a good partner in the home environment, reducing the burden on parents and satisfying children's intellectual curiosity.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] The user (child) asks a question

[0335] The user (child) speaks a question into the terminal.

[0336] Specific action: A child asks the device, "Why is the sky blue?"

[0337] Input: Voice question

[0338] Output: Audio data

[0339] Step 2:

[0340] The device receives the voice data and converts it into text data.

[0341] The terminal receives the user's voice data and converts it into text data using voice recognition software.

[0342] Specific operation: The microphone on the device captures the voice and uses speech recognition software (e.g., Google Speech-to-Text API) to generate text data such as "Why is the sky blue?"

[0343] Input: Audio data

[0344] Output: Text data

[0345] Step 3:

[0346] The device uses an emotion engine to extract emotion data such as anxiety.

[0347] The terminal inputs the voice data into an emotion engine and extracts emotion data.

[0348] Specific operation: The device analyzes the emotion "anxiety" from the voice using an emotion engine (e.g., OpenAI's emotion analysis model).

[0349] Input: Audio data

[0350] Output: Emotion data (anxiety)

[0351] Step 4:

[0352] The device sends text data and emotion data to the server.

[0353] The device transmits the generated text data and emotion data to the server.

[0354] Specific operation: The device uses Wi-Fi to send the text data "Why is the sky blue?" and the emotion data "anxiety" in JSON format to the server.

[0355] Input: Text data, emotion data

[0356] Output: JSON data

[0357] Step 5:

[0358] The server receives and analyzes the text data and emotion data.

[0359] The server receives the JSON data sent from the device and analyzes the text data and emotion data.

[0360] Specific operation: The server analyzes the received JSON data and extracts the text data "Why is the sky blue?" and the emotion data "Anxiety."

[0361] Input: JSON data

[0362] Output: Text data, emotion data

[0363] Step 6:

[0364] The server uses the generative AI model to generate the appropriate answer

[0365] The server inputs text data and emotional data into the generative AI model and generates an appropriate answer.

[0366] What it does: The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer like, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[0367] Input: Text data, emotion data

[0368] Output: Answer text

[0369] Step 7:

[0370] The server converts the generated answer into audio data.

[0371] The server converts the generated text response into audio data.

[0372] Specific operation: The server uses the Google Text-to-Speech API to convert the generated text response into audio data (MP3 format).

[0373] Input: Answer text

[0374] Output: Audio data

[0375] Step 8:

[0376] The server sends the audio data to the device.

[0377] The server transmits the generated voice data to the terminal.

[0378] Specific operation: The server sends audio data to the device via Wi-Fi.

[0379] Input: Audio data

[0380] Output: Transmitted audio data

[0381] Step 9:

[0382] The device plays back the received voice data and provides the child with a response.

[0383] The terminal plays back the voice data received from the server and provides the answer to the user (child).

[0384] What it does: The speaker on the device plays a gentle voice saying, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[0385] Input: Transmitted audio data

[0386] Output: Answer provided through speech

[0387] (Application example 2)

[0388] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0389] In today's brick-and-mortar stores, it is difficult to consistently provide high-quality answers and suggestions to the diverse questions and requests customers ask. It is also necessary to properly recognize customer emotions and adjust responses accordingly, but current systems are not sufficient in this regard. Furthermore, while it is important to achieve friendly and effective communication in customer service, existing systems also have limitations in this regard. It is necessary to solve these problems and provide services that will satisfy customers.

[0390] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0391] In this invention, the server includes means for analyzing text data and generating appropriate answers using a generative AI model, means for analyzing emotion data and generating answers based on the emotions, and means for converting the generated answers into voice data. This makes it possible to improve the quality of customer service in physical stores and increase customer satisfaction by recognizing customer emotions and adjusting answers accordingly.

[0392] "Audio data" refers to data that has been recorded and stored in digital format.

[0393] "Text data" refers to data in which character information is recorded and stored in digital format.

[0394] A "server" is a computer or system that manages and processes data on a network.

[0395] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate appropriate answers and information from given data.

[0396] "Emotional data" is data extracted from a user's voice or text that indicates their emotions or emotional state.

[0397] A "terminal" is a device used by a user that receives and plays audio data.

[0398] An "emotion engine" is an algorithm or system that analyzes emotions from voice and text data and generates emotional data.

[0399] A "machine learning algorithm" is a method of learning patterns and rules from large amounts of data and making predictions and classifications for new data.

[0400] MODE FOR CARRYING OUT THE INVENTION

[0401] The system of the present invention is primarily designed to improve customer service in brick-and-mortar stores, and specific embodiments thereof are described below.

[0402] Overall system configuration

[0403] The overall system consists of the following main components:

[0404] 1. Terminal

[0405] 2. Server

[0406] 3. Generative AI Models

[0407] 4. Emotion Engine

[0408] 5. Machine Learning Algorithms

[0409] Detailed System Description

[0410] Terminal

[0411] The terminal is a customer service robot installed in a brick-and-mortar store that receives voice input from customers. The terminal converts the received voice data into text data using voice recognition software and sends this text data to a server. The terminal also receives the answer generated by the generative AI model as voice data and provides it to the customer as voice output.

[0412] server

[0413] The server receives and analyzes the text data and emotion data sent from the device. Using a generative AI model and emotion engine, the server generates an appropriate answer to the customer's question and converts it into voice data. This converted voice data is then sent back to the device.

[0414] Generative AI Models

[0415] A generative AI model uses machine learning algorithms to generate appropriate responses based on text data and emotional data sent from a device. Generative AI models learn from past interaction data to improve response accuracy.

[0416] Emotion Engine

[0417] The emotion engine is an algorithm that analyzes customer emotions from voice and text data and feeds them back to the generative AI model as emotion data. The emotion engine analyzes the tone and content of the customer's voice when they ask a question, extracting emotions such as anxiety, excitement, and interest.

[0418] Machine Learning Algorithms

[0419] A machine learning algorithm is an algorithm that learns patterns and rules from large amounts of data and makes predictions and classifications for new data. In the present invention, it is used to sequentially learn from interactions with customers and improve response accuracy from the next time onwards.

[0420] Specific use cases

[0421] A specific example of use is shown below.

[0422] Example 1: When a customer excitedly asks, "Do you have this item in stock?"

[0423] 1. User (customer): "Do you have this item in stock?" asks the robot terminal excitedly.

[0424] 2. The device receives the voice and converts it into text using speech recognition software.

[0425] 3. The device uses an emotion engine to extract the emotion data "excitement" from the voice.

[0426] 4. The device sends the converted text data and emotion data to the server.

[0427] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate the answer, "Yes, we have it in stock. We'll show you here."

[0428] 6. The server converts the generated answer into voice data and sends it to the device.

[0429] 7. The terminal plays back the received voice data and provides the answer to the customer.

[0430] Example prompt sentence:

[0431] When a customer excitedly asks, "Do you have this item in stock?", you can respond with, "Yes, we do have it in stock. Let me show you."

[0432] Example 2: When a customer anxiously asks, "How do I use this product?"

[0433] 1. User (customer): "How do I use this product?" he asks anxiously to the robot terminal.

[0434] 2. The device receives the voice and converts it into text using speech recognition software.

[0435] 3. The device uses an emotion engine to extract the emotion data "anxiety" from the voice.

[0436] 4. The device sends the converted text data and emotion data to the server.

[0437] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate an answer such as, "This is how to use this product. Please also refer to the instructions."

[0438] 6. The server converts the generated answer into voice data and sends it to the device.

[0439] 7. The terminal plays back the received voice data and provides the answer to the customer.

[0440] Example prompt sentence:

[0441] If a customer asks anxiously, "How do I use this product?", answer, "This is how you use this product. Please read the instructions."

[0442] In this way, by using the system of the present invention, the quality of customer service in physical stores can be improved and customer satisfaction can be increased.

[0443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0444] Step 1:

[0445] The user (customer) performs voice input. The customer dictates their question or request. The input of this process is the customer's voice data, and the output is the voice data received by the terminal.

[0446] What happens: A customer asks, "Do you have this item in stock?"

[0447] Step 2:

[0448] The terminal receives the voice data and converts it into text data using voice recognition software.

[0449] The input of this process is audio data, and the output is text data. Specific operation: The device transcribes the audio using a speech recognition library (e.g., speech_recognition).

[0450] Step 3:

[0451] The terminal uses an emotion engine to analyze emotions from the voice data and extract emotion data.

[0452] The input of this process is voice data, and the output is emotion data. Specific operation: The device analyzes the voice data using an emotion analysis algorithm (e.g., EmotionRecognition) to identify the emotion.

[0453] Step 4:

[0454] The terminal transmits the converted text data and emotion data to the server.

[0455] The input of this process is text data and emotion data, and the output is the data sent to the server. Specific operation: The device sends the text data and emotion data to the server as an HTTP request.

[0456] Step 5:

[0457] The server analyzes the received text data and emotional data and generates an appropriate answer using a generative AI model.

[0458] The input of this process is text data and emotion data, and the output is the generated answer text. Specific operation: The server inputs the prompt sentence into the generative AI model (e.g., GPT-3) and generates an appropriate answer.

[0459] Step 6:

[0460] The server converts the generated response into audio data.

[0461] The input of this process is the answer text, and the output is audio data. Specific operation: The server converts the answer into audio data using a text-to-speech synthesis engine (e.g., pyttsx3).

[0462] Step 7:

[0463] The server transmits the generated voice data to the terminal.

[0464] The input of this process is audio data, and the output is the audio data sent to the terminal. Specific operation: The server sends the audio data to the terminal as an HTTP response.

[0465] Step 8:

[0466] The terminal plays back the received voice data and provides the answer to the customer.

[0467] The input of this process is voice data, and the output is voice information provided to the customer. Specific operation: The terminal plays the voice data using the speaker and responds, "Yes, we have it in stock. I'll show you here."

[0468] 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.

[0469] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0470] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0471] [Second embodiment]

[0472] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0473] 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.

[0474] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0475] 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.

[0476] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0477] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0478] 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.

[0479] 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.

[0480] 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 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.

[0481] 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.

[0482] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0483] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0484] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. The processing flow of the entire system and a specific example are shown below.

[0485] System configuration

[0486] The system mainly consists of the following components:

[0487] 1. Terminal (doll-like device):

[0488] It receives voice data uttered by the child and converts it into text data.

[0489] The audio data sent from the server is played back to provide the answer to the user (child).

[0490] 2. Server:

[0491] Receives and analyzes text data sent from the device.

[0492] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[0493] The converted audio data is sent to the terminal.

[0494] 3. Generative AI Model:

[0495] The text data is analyzed to generate the most appropriate answer based on the child's age and level of understanding.

[0496] Specific examples for carrying out the invention

[0497] Below is a concrete example of how the system responds to a child's question, "Why?"

[0498] Example 1: When a child asks, "Why is the sky blue?"

[0499] 1. User (child): Asks the doll-like device, "Why is the sky blue?"

[0500] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[0501] 3. The device sends the converted text data to the server.

[0502] 4. The server analyzes the received text data and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0503] 5. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[0504] 6. The device plays back the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0505] Example 2: When a child asks, "Why are leaves green?"

[0506] 1. User (child): Asks the doll-like device, "Why are leaves green?"

[0507] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[0508] 3. The device sends the converted text data to the server.

[0509] 4. The server analyzes the text data and uses a generative AI model to generate the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0510] 5. The server converts the generated answer into audio data and sends it to the device.

[0511] 6. The device plays back the received audio data and provides the user (child) with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0512] In this way, the system of the present invention satisfies children's intellectual curiosity by providing quick and accurate answers to their "why?" questions, while significantly reducing the burden on parents. The entire system processes information with high accuracy through automated processes, and is designed to function as a good partner in the home environment.

[0513] The processing flow will be explained below.

[0514] Step 1:

[0515] The user (child) speaks a question into the doll-shaped terminal.

[0516] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[0517] Step 2:

[0518] The voice data received by the terminal is converted into text data.

[0519] Specific operation: The voice recognition software in the device converts the voice data into text data and generates the text information "Why is the sky blue?"

[0520] Step 3:

[0521] The terminal transmits the converted text data to the server.

[0522] Specific operation: The device uses an HTTP request to upload the converted text data to the server.

[0523] Step 4:

[0524] The server analyzes the received text data.

[0525] Specific operation: The server analyzes the text data "Why is the sky blue?" and inputs it into the generative AI model.

[0526] Step 5:

[0527] The generation AI on the server generates appropriate answers.

[0528] Specific behavior: The generative AI model generates the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light."

[0529] Step 6:

[0530] The server converts the generated response into audio data.

[0531] Specific operation: A TTS (Text-To-Speech) engine that converts text to speech converts the generated response into audio data.

[0532] Step 7:

[0533] The server transmits the generated voice data to the terminal.

[0534] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[0535] Step 8:

[0536] The terminal plays back the received audio data.

[0537] Specific operation: The speaker built into the device plays the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0538] In this way, through a series of processing steps, quick and friendly answers are provided to children's questions, reducing the burden on parents and satisfying children's intellectual curiosity.

[0539] Example 1

[0540] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0541] The present invention aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing prompt and appropriate answers to questions posed by children. However, conventional systems often lack the accuracy of voice data recognition and the appropriateness of answers, resulting in unsatisfactory results. It has also been difficult to generate answers that correspond to each child's level of understanding and age.

[0542] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0543] In this invention, the server includes means for receiving questions posed by the child as voice data, means for converting the received voice data into text data, means for transmitting the converted text data to the server, means for the server to analyze the text data and generate an appropriate answer using a generative AI model, means for converting the generated answer into voice data, means for transmitting the generated voice data to the terminal, means for the terminal to play the voice data and provide the answer to the child, means for the terminal to use a high-sensitivity microphone and voice recognition software when performing voice recognition and converting the voice data into text data, and means for the server to input a prompt sentence into the generative AI model and generate an answer. This improves the accuracy of voice recognition and the appropriateness of the answer, making it possible to provide a high-level answer according to the child's age and level of understanding.

[0544] "Means for receiving questions posed by children as voice data" refers to a system for acquiring questions posed by children verbally as digital voice data via a voice input device such as a microphone.

[0545] The "means for converting received voice data into text data" refers to a process in which voice data is analyzed using voice recognition software and converted into text data as a string of characters.

[0546] The "means for transmitting the converted text data to the server" is a method for transmitting the text data from the terminal to the server using a communication protocol (for example, HTTP or HTTPS).

[0547] "Means for the server to analyze text data and generate an appropriate answer using a generative AI model" refers to a mechanism in which received text data is processed by an analytical algorithm on the server, and an AI model (e.g., a generative model) is used to generate the optimal answer corresponding to the text data.

[0548] The "means for converting the generated answer into voice data" refers to a process for converting the generated text-based answer into voice data that can be heard by the user using a voice synthesis engine or the like.

[0549] The "means for transmitting generated voice data to the terminal" refers to a method for transferring voice data generated on the server to the terminal via a communication channel.

[0550] "Means for the device to play audio data and provide answers to the child" refers to a mechanism that uses a speaker or other audio output device built into the device to play audio data and provide verbal answers to the user (child).

[0551] "Means for a device to use a high-sensitivity microphone and speech recognition software to perform speech recognition and convert speech data into text data" refers to the process of using a high-sensitivity microphone to capture speech in detail and accurately, and then converting that speech data into text data using speech recognition software (e.g., a speech recognition engine).

[0552] "Means in which the server inputs a prompt sentence into a generative AI model to generate an answer" refers to the process in which the server inputs a prompt sentence into a generative AI model (e.g., a text generation model) to generate an appropriate answer in natural language.

[0553] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. A detailed description of the specific invention is provided below.

[0554] System configuration

[0555] This system consists of the following hardware and software:

[0556] 1. Terminal (doll-shaped device)

[0557] Hardware: High-sensitivity microphone, speaker, communication module.

[0558] Software: Install voice recognition software (e.g., Google Speech-to-Text) to convert received voice data into text data.

[0559] Function: Receives voices uttered by the child and converts them into text data. It also plays back the voice data sent from the server and provides answers to the child.

[0560] 2. Server

[0561] Hardware: A server machine equipped with a high-performance processor.

[0562] Software: Powered by generative AI models (e.g., OpenAI GPT-3) and speech synthesis engines (e.g., Amazon Polly).

[0563] Function: Analyzes text data received from the device and generates appropriate answers using a generative AI model. The generated answers are then converted into voice data and sent to the device.

[0564] 3. Generative AI Models

[0565] Function: Runs on the server, analyzes text data, and generates the most appropriate answer based on the child's age and level of understanding.

[0566] Examples of specific examples and prompts

[0567] Example 1: When a child asks, "Why is the sky blue?"

[0568] 1. The user (child) asks the doll-shaped device, "Why is the sky blue?"

[0569] 2. The device receives the voice and converts it into text data, such as "Why is the sky blue?", using a high-sensitivity microphone and Google Speech-to-Text software.

[0570] 3. The device sends the converted text data to the server.

[0571] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[0572] Your child asks, "Why is the sky blue?" He is 5 years old. Explain it gently and simply so that he can understand.

[0573] 5. The server receives the answer generated by the AI ​​model, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light," and converts it into speech data using Amazon Polly.

[0574] 6. The server sends the generated voice data to the device.

[0575] 7. The device plays back the received audio data and provides the child with the answer, "The sky appears blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0576] Example 2: When a child asks, "Why are leaves green?"

[0577] 1. The user (child) asks the doll-shaped device, "Why are leaves green?"

[0578] 2. The device receives the voice and converts it into text data, such as "Why are leaves green?", using a sensitive microphone and Google Speech-to-Text software.

[0579] 3. The device sends the converted text data to the server.

[0580] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[0581] Your child asks, "Why are leaves green?" He is 7 years old. Explain it gently and simply so that he can understand.

[0582] 5. The server receives the answer generated by the AI ​​model, "Leaves are green because they contain a pigment called chlorophyll," and converts it into speech data using Amazon Polly.

[0583] 6. The server sends the generated voice data to the device.

[0584] 7. The device plays back the received audio data and provides the child with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0585] In this way, the present invention integrates advanced technologies such as speech recognition, text generation, and speech synthesis to provide quick and accurate answers to children's questions, thereby satisfying children's intellectual curiosity while reducing the burden on parents.

[0586] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0587] Step 1: The user (child) inputs a question as voice data

[0588] The user (child) asks a question to the doll-shaped device. For example, the child might ask, "Why is the sky blue?" This voice question is received by the device's high-sensitivity microphone.

[0589] Input: A child-uttered vocal question (e.g., "Why is the sky blue?").

[0590] Output: The question as audio data.

[0591] Step 2: Your device converts the audio data into text.

[0592] The device uses built-in voice recognition software (e.g., Google Speech-to-Text) to analyze the received voice data and convert it into text data. Specifically, the voice signal is captured as digital data and converted into a string of characters.

[0593] Input: A question as audio data (e.g., "Why is the sky blue?").

[0594] Output: The question as text data (e.g., "Why is the sky blue?").

[0595] Step 3: The device sends the text data to the server

[0596] The terminal sends the converted text data to the server via an internal network module, using the HTTP or HTTPS protocol.

[0597] Input: A question as text data (e.g., "Why is the sky blue?").

[0598] Output: The text data sent to the server.

[0599] Step 4: The server analyzes the text data and generates an answer using a generative AI model

[0600] The server analyzes the received text data and generates an answer by inputting an appropriate prompt into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt might be, "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it kindly and simply so that a child can understand." The generative AI model generates an answer based on this prompt.

[0601] Input: A question as text data (e.g., "Why is the sky blue?") and a prompt for the generative AI model (e.g., "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it gently and simply so that he can understand.").

[0602] Output: Correct answer (e.g. "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering the blue light").

[0603] Step 5: The server converts the generated answer into audio data.

[0604] The server uses a speech synthesis engine (e.g., Amazon Polly) to convert the generated text response into audio data, which is then converted into audio data (e.g., an MP3 file).

[0605] Input: Answer as text data (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering blue light").

[0606] Output: Answer as audio data (e.g. audio file).

[0607] Step 6: The server sends the audio data to the device

[0608] The server then sends the generated audio data to the device via the network, using the HTTP or HTTPS protocol.

[0609] Input: Answer as audio data (e.g. audio file).

[0610] Output: The audio data sent to the device.

[0611] Step 7: The device plays the audio data and provides it to the user (child).

[0612] The device uses a built-in speaker to play back the received voice data, allowing the user (child) to hear the answer aloud.

[0613] Input: Answer as audio data (e.g. audio file).

[0614] Output: Audio output that the user (child) can hear (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere and scatters blue light").

[0615] (Application example 1)

[0616] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0617] Physical stores, especially educational museums and science museums, require quick and accurate answers to visitors' questions, especially children's. However, conventional guidance systems require staff with specialized knowledge to be on-site at all times, resulting in increased labor costs and delayed responses due to staff shortages. Furthermore, answers may not be consistent or accurate, making it difficult to improve visitor satisfaction. The present invention aims to solve these problems and improve visitor experience by automatically providing highly accurate answers to questions.

[0618] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0619] In this invention, the server includes means for receiving visitor questions as voice data, means for converting the received voice data into text data, means for analyzing the converted text data and generating appropriate answers using a generative AI model, means for converting the generated answers into voice data, means for transmitting the generated voice data to a terminal and playing it back, and means for generating appropriate answers to visitor questions in a physical store and providing voice guidance. This reduces the burden on staff and makes it possible to quickly provide consistent, highly accurate answers.

[0620] "Children" or "visitors" are people who use the system to request information in specific situations, and are further subdivided based on factors such as age and whether or not they have specialized knowledge.

[0621] "Audio data" means audio signals in digital form obtained from human speech or other audio sources.

[0622] "Text data" is information of a character string obtained by analyzing voice data, and is data that includes questions and instructions.

[0623] A "server" is a computing device that processes and analyzes voice and text data and generates a response.

[0624] A "generative AI model" is a model of an artificial intelligence algorithm that analyzes received text data and generates the optimal answer to a question.

[0625] An "appropriate answer" is accurate information that matches the questioner's understanding, based on the results of analysis by the generative AI model.

[0626] A "terminal" is a device for transmitting and receiving voice data and reproducing generated responses.

[0627] "Brick and mortar stores" are physical stores or facilities that visitors actually visit, including educational facilities such as museums and science centers.

[0628] This invention includes a system for providing appropriate answers to questions of visitors in a physical store by voice. The configuration and processing of the entire system will be described in detail below.

[0629] System configuration

[0630] 1. Terminal (guide robot or information kiosk):

[0631] Receives visitor voice data using a microphone

[0632] Converts received voice data into text data using voice recognition software

[0633] Send the converted text data to the server

[0634] Play audio data received from the server on the speaker

[0635] 2. Server:

[0636] Receive and analyze text data sent from the device

[0637] Generate appropriate answers using a generative AI model (e.g., GPT-4)

[0638] Use a Text-to-Speech (TTS) engine to convert the generated answers into audio data.

[0639] Send the generated audio data to the device

[0640] 3. Generative AI Model:

[0641] Analyze text data and generate the most appropriate answer based on the visitor's age and level of understanding

[0642] For example, in response to the question "What is the name of this dinosaur?", it generates the answer "The name of this dinosaur is Tyrannosaurus."

[0643] Hardware and software used

[0644] Hardware:

[0645] Microphone: Receives the visitor's voice

[0646] Speaker: Outputs the generated audio data

[0647] Terminals (guide robots and information kiosks): Act as interfaces

[0648] software:

[0649] speech_recognition: A library that converts speech data into text data

[0650] TextToSpeech: An engine that converts text data into speech data

[0651] AnswerGenerator: Software that analyzes text data using a generative AI model (e.g., GPT-4) and generates answers.

[0652] Specific examples

[0653] Example 1: Dinosaur exhibits in museums

[0654] Question: "What is the name of this dinosaur?"

[0655] Answer: "The name of this dinosaur is Tyrannosaurus."

[0656] Example prompt sentence:

[0657] User input: "What is the name of this dinosaur?"

[0658] Constructing an Answer: The name of this dinosaur is Tyrannosaurus.

[0659] Example 2: Space exhibits at science museums

[0660] Question: "When was the launch date for Apollo 11?"

[0661] Answer: "The launch date of Apollo 11 was July 16, 1969."

[0662] Example prompt sentence:

[0663] User Input: "What is the launch date for Apollo 11?"

[0664] Constructing an Answer: The launch date of Apollo 11 was July 16, 1969.

[0665] In this way, the entire system can provide quick and accurate answers to visitor questions, reducing the burden on staff and enabling them to provide consistent, accurate answers.

[0666] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0667] Step 1:

[0668] A user asks a question to a terminal (a guide robot or an information kiosk) installed in a physical store. At this time, a microphone built into the terminal receives voice data. The input here is the user's speech, and the output is the captured voice data.

[0669] Step 2:

[0670] The device processes the received voice data with speech recognition software and converts it into text data. This process uses a speech recognition library (e.g., speech_recognition). The input is voice data, and the output is text data.

[0671] Step 3:

[0672] The terminal transmits the converted text data to the server. The data transfer here is performed via network communication. The input is the text data, and the output is the text data transmitted to the server.

[0673] Step 4:

[0674] The server receives and analyzes the text data sent from the device. A generative AI model (e.g., GPT-4) is used for the analysis to generate the optimal answer based on the visitor's age and level of understanding. The input is text data, and the output is the generated answer text.

[0675] Step 5:

[0676] The server uses a TTS (Text-to-Speech) engine to convert the generated answer text data into voice data. The input is the answer text data, and the output is voice data.

[0677] Step 6:

[0678] The server transmits the generated voice data to the terminal. Again, data transfer is performed via network communication. The input is the voice data, and the output is the voice data transmitted to the terminal.

[0679] Step 7:

[0680] The device plays the received voice data and provides the answer to the user. This process uses the device's built-in speaker. The input is the voice data, and the output is the answer played back as voice.

[0681] Step 8:

[0682] The device accumulates and analyzes the received voice data and user responses, and applies machine learning algorithms to improve response accuracy in the future. The input is past voice data and user feedback, and the output is an improved voice recognition model.

[0683] These processing steps result in a system that automatically and accurately answers a series of questions.

[0684] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0685] This system aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to children's questions such as "Why?" This system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts responses accordingly.

[0686] System configuration

[0687] The system mainly consists of the following components:

[0688] 1. Terminal (doll-like device):

[0689] It receives voice data uttered by the child and converts it into text data.

[0690] The audio data sent from the server is played back to provide the answer to the user (child).

[0691] 2. Server:

[0692] Receives and analyzes text data and emotion data sent from the device.

[0693] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[0694] The converted audio data is sent to the terminal.

[0695] 3. Generative AI Model:

[0696] It analyzes text data and emotional data to generate the most appropriate answer based on the child's age, level of understanding, and emotions.

[0697] 4. Emotion Engine:

[0698] The user's emotions are analyzed from voice and text data and fed back to the generative AI model as emotional data.

[0699] Specific examples for carrying out the invention

[0700] Below we show the specific behavior of the system in response to a child's "why?" question and their emotions.

[0701] Example 1: If a child asks, in an anxious voice, "Why is the sky blue?"

[0702] 1. User (child): "Why is the sky blue?" asks the doll-like device in an anxious voice.

[0703] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[0704] 3. The device uses an emotion engine to extract emotion data, such as anxiety, from the voice.

[0705] 4. The device sends the converted text data and emotion data to the server.

[0706] 5. The server analyzes the received text data and the emotional data of anxiety, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is completely natural."

[0707] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[0708] 7. The device plays back the received voice data and provides the user (child) with a gentle answer: "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[0709] Example 2: When a child asks, "Why are leaves green?" in an inquisitive voice.

[0710] 1. User (child): "Why are leaves green?" asks the doll-like device in an inquisitive voice.

[0711] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[0712] 3. The device uses an emotion engine to extract emotion data such as interest from the voice.

[0713] 4. The device sends the converted text data and emotion data to the server.

[0714] 5. The server analyzes the received text data and emotional interest data, and uses a generative AI model to generate an answer such as, "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things to know!"

[0715] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[0716] 7. The device plays back the received audio data and provides the user (child) with an answer in an intriguing tone: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things too!"

[0717] In this way, the system of the present invention adjusts responses to children's "why?" questions by taking emotions into account, enabling more friendly and effective communication. This reduces the burden on parents and satisfies children's intellectual curiosity. The entire system is configured to function as a good partner in the home environment by integrating various components, including the emotion engine.

[0718] The processing flow will be explained below.

[0719] Step 1:

[0720] The user (child) speaks a question into the doll-shaped terminal.

[0721] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[0722] Step 2:

[0723] The voice data received by the terminal is converted into text data.

[0724] Specific operation: The voice recognition software in the device converts the voice data into text data such as "Why is the sky blue?"

[0725] Step 3:

[0726] The device uses an emotion engine to extract emotional data from the child's voice.

[0727] Specific operation: The emotion engine analyzes voice data and recognizes the child's emotions, such as anxiety or interest.

[0728] Step 4:

[0729] The terminal transmits the converted text data and emotion data to the server.

[0730] Specific operation: The device uses an HTTP request to upload text data and emotion data to the server.

[0731] Step 5:

[0732] The server analyzes the received text data and emotion data.

[0733] Specific operation: The server analyzes the text data "Why is the sky blue?" and the emotion data "anxiety."

[0734] Step 6:

[0735] The generative AI on the server generates appropriate answers based on text data and emotional data.

[0736] What it does: The generative AI model generates the answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[0737] Step 7:

[0738] The server converts the generated response into audio data.

[0739] Specific operation: The TTS (Text-To-Speech) engine in the server converts the generated answer into voice data.

[0740] Step 8:

[0741] The server transmits the generated voice data to the terminal.

[0742] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[0743] Step 9:

[0744] The terminal plays back the received audio data.

[0745] What it does: The device's built-in speaker plays the received audio data. The answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing," is delivered in a gentle tone to the anxious child.

[0746] In this way, by incorporating emotional data, the system can provide answers that are in tune with the child's feelings, resulting in more friendly and effective communication, which reduces the burden on parents and satisfies the child's intellectual curiosity.

[0747] Example 2

[0748] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0749] It takes a lot of time and effort for parents to quickly and appropriately answer the "why?" questions that children frequently ask. Furthermore, it is extremely difficult to provide answers that reflect the child's emotions. Therefore, there is a need to develop a system that can satisfy children's intellectual curiosity while reducing the burden on parents. Another challenge is to improve the accuracy of responses to similar questions.

[0750] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0751] In this invention, the server includes means for receiving questions posed by children as voice data, means for converting the received voice data into text data, and means for extracting the converted text data and emotion data. This makes it possible to generate and provide appropriate answers to questions posed by children quickly and taking emotion into consideration. Furthermore, by including means for successively evolving the voice data and emotion data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards, the quality of answers to similar questions can be continuously improved.

[0752] "Children" refers to young people who use the system and ask questions aloud.

[0753] A "question" refers to a verbal expression of a child's doubts.

[0754] "Audio data" refers to information that has been recorded as sound in response to questions uttered by children and converted into digital format.

[0755] "Text data" refers to digital data in which voice data is converted into text information using voice recognition technology.

[0756] "Server" refers to a computer system that receives and analyzes data over a network and generates appropriate answers using a generative AI model.

[0757] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers from given input data based on a pre-trained algorithm.

[0758] "Emotional data" refers to data that is generated by analyzing emotions from the user's tone of voice, words, etc., and then expressing the results in digital form.

[0759] "Speech recognition software" refers to software that receives voice data as input and converts that voice into text data.

[0760] An "emotion engine" refers to a system that analyzes user emotions from voice data and feeds this back to a generative AI model as emotional data.

[0761] "Audio data transmission means" refers to a communication means for transmitting audio data generated by the server to the terminal.

[0762] A "machine learning algorithm" is an algorithm that allows the system to self-learn based on past data and improve the accuracy of responses from the next time onwards.

[0763] This invention is a system that aims to reduce the burden on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to the "why?" questions that children often ask on a daily basis. The system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts the response accordingly. This system mainly consists of the following components:

[0764] System Configuration

[0765] 1. Terminal (doll-like device):

[0766] The system receives the voice data uttered by the child and converts it into text data. Specifically, the device's built-in microphone captures the voice and converts it into text data using voice recognition software (e.g., Google Speech-to-Text API). Furthermore, it uses an emotion engine to extract emotional data from the voice and sends it to the server.

[0767] The audio data sent from the server is played back to provide the answer to the user (child). Specifically, the audio data received by the device's built-in speaker is played back (e.g., MP3 format data generated using the Google Text-to-Speech API).

[0768] 2. Server:

[0769] The server receives and analyzes the text data and emotion data sent from the device. Specifically, the server analyzes the received JSON data and extracts the text data and emotion data.

[0770] A generative AI model is used to generate an appropriate response, which is then converted into voice data. Specifically, the server uses a generative AI model (e.g., OpenAI GPT-4) to input a prompt and generate an appropriate text response. The response is then converted into voice data using the Google Text-to-Speech API and sent to the device.

[0771] The converted audio data is sent to the device. Specifically, the audio data (MP3 format) is sent to the device via Wi-Fi.

[0772] 3. Generative AI Model:

[0773] It analyzes text and emotion data to generate the most appropriate answer based on the child's age, level of understanding, and emotions. Example prompt: "The sky appears blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a completely natural process."

[0774] Specific examples

[0775] 1. Example 1: When a child asks, in an anxious voice, "Why is the sky blue?"

[0776] When a user (child) asks "Why is the sky blue?" in an anxious voice to the doll-shaped device, the device receives the voice and converts it into text data, "Why is the sky blue?" using speech recognition software. The device then uses an emotion engine to extract emotion data, such as anxiety, from the voice and sends the converted text data and emotion data to the server. The server then analyzes the received text data and emotion data, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing." The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in a gentle tone.

[0777] 2. Example 2: When a child asks, in an inquisitive voice, "Why are leaves green?"

[0778] When a user (child) asks the doll-shaped device in an inquisitive voice, "Why are leaves green?", the device receives the voice and uses speech recognition software to convert it into text data: "Why are leaves green?". The device then uses an emotion engine to extract emotional interest data from the voice and sends the converted text data and emotional data to the server. The server analyzes the received text data and emotional interest data and uses a generative AI model to generate an answer: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things about them!" The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in an inquisitive tone.

[0779] As described above, this invention aims to effectively utilize the emotion engine and generative AI model to function as a good partner in the home environment, reducing the burden on parents and satisfying children's intellectual curiosity.

[0780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0781] Step 1:

[0782] The user (child) asks a question

[0783] The user (child) speaks a question into the terminal.

[0784] Specific action: A child asks the device, "Why is the sky blue?"

[0785] Input: Voice question

[0786] Output: Audio data

[0787] Step 2:

[0788] The device receives the voice data and converts it into text data.

[0789] The terminal receives the user's voice data and converts it into text data using voice recognition software.

[0790] Specific operation: The microphone on the device captures the voice and uses speech recognition software (e.g., Google Speech-to-Text API) to generate text data such as "Why is the sky blue?"

[0791] Input: Audio data

[0792] Output: Text data

[0793] Step 3:

[0794] The device uses an emotion engine to extract emotion data such as anxiety.

[0795] The terminal inputs the voice data into an emotion engine and extracts emotion data.

[0796] Specific operation: The device analyzes the emotion "anxiety" from the voice using an emotion engine (e.g., OpenAI's emotion analysis model).

[0797] Input: Audio data

[0798] Output: Emotion data (anxiety)

[0799] Step 4:

[0800] The device sends text data and emotion data to the server.

[0801] The device transmits the generated text data and emotion data to the server.

[0802] Specific operation: The device uses Wi-Fi to send the text data "Why is the sky blue?" and the emotion data "anxiety" in JSON format to the server.

[0803] Input: Text data, emotion data

[0804] Output: JSON data

[0805] Step 5:

[0806] The server receives and analyzes the text data and emotion data.

[0807] The server receives the JSON data sent from the device and analyzes the text data and emotion data.

[0808] Specific operation: The server analyzes the received JSON data and extracts the text data "Why is the sky blue?" and the emotion data "Anxiety."

[0809] Input: JSON data

[0810] Output: Text data, emotion data

[0811] Step 6:

[0812] The server uses the generative AI model to generate the appropriate answer

[0813] The server inputs text data and emotional data into the generative AI model and generates an appropriate answer.

[0814] What it does: The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer like, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[0815] Input: Text data, emotion data

[0816] Output: Answer text

[0817] Step 7:

[0818] The server converts the generated answer into audio data.

[0819] The server converts the generated text response into audio data.

[0820] Specific operation: The server uses the Google Text-to-Speech API to convert the generated text response into audio data (MP3 format).

[0821] Input: Answer text

[0822] Output: Audio data

[0823] Step 8:

[0824] The server sends the audio data to the device.

[0825] The server transmits the generated voice data to the terminal.

[0826] Specific operation: The server sends audio data to the device via Wi-Fi.

[0827] Input: Audio data

[0828] Output: Transmitted audio data

[0829] Step 9:

[0830] The device plays back the received voice data and provides the child with a response.

[0831] The terminal plays back the voice data received from the server and provides the answer to the user (child).

[0832] What it does: The speaker on the device plays a gentle voice saying, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[0833] Input: Transmitted audio data

[0834] Output: Answer provided through speech

[0835] (Application example 2)

[0836] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0837] In today's brick-and-mortar stores, it is difficult to consistently provide high-quality answers and suggestions to the diverse questions and requests customers ask. It is also necessary to properly recognize customer emotions and adjust responses accordingly, but current systems are not sufficient in this regard. Furthermore, while it is important to achieve friendly and effective communication in customer service, existing systems also have limitations in this regard. It is necessary to solve these problems and provide services that will satisfy customers.

[0838] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0839] In this invention, the server includes means for analyzing text data and generating appropriate answers using a generative AI model, means for analyzing emotion data and generating answers based on the emotions, and means for converting the generated answers into voice data. This makes it possible to improve the quality of customer service in physical stores and increase customer satisfaction by recognizing customer emotions and adjusting answers accordingly.

[0840] "Audio data" refers to data that has been recorded and stored in digital format.

[0841] "Text data" refers to data in which character information is recorded and stored in digital format.

[0842] A "server" is a computer or system that manages and processes data on a network.

[0843] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate appropriate answers and information from given data.

[0844] "Emotional data" is data extracted from a user's voice or text that indicates their emotions or emotional state.

[0845] A "terminal" is a device used by a user that receives and plays audio data.

[0846] An "emotion engine" is an algorithm or system that analyzes emotions from voice and text data and generates emotional data.

[0847] A "machine learning algorithm" is a method of learning patterns and rules from large amounts of data and making predictions and classifications for new data.

[0848] MODE FOR CARRYING OUT THE INVENTION

[0849] The system of the present invention is primarily designed to improve customer service in brick-and-mortar stores, and specific embodiments thereof are described below.

[0850] Overall system configuration

[0851] The overall system consists of the following main components:

[0852] 1. Terminal

[0853] 2. Server

[0854] 3. Generative AI Models

[0855] 4. Emotion Engine

[0856] 5. Machine Learning Algorithms

[0857] Detailed System Description

[0858] Terminal

[0859] The terminal is a customer service robot installed in a brick-and-mortar store that receives voice input from customers. The terminal converts the received voice data into text data using voice recognition software and sends this text data to a server. The terminal also receives the answer generated by the generative AI model as voice data and provides it to the customer as voice output.

[0860] server

[0861] The server receives and analyzes the text data and emotion data sent from the device. Using a generative AI model and emotion engine, the server generates an appropriate answer to the customer's question and converts it into voice data. This converted voice data is then sent back to the device.

[0862] Generative AI Models

[0863] A generative AI model uses machine learning algorithms to generate appropriate responses based on text data and emotional data sent from a device. Generative AI models learn from past interaction data to improve response accuracy.

[0864] Emotion Engine

[0865] The emotion engine is an algorithm that analyzes customer emotions from voice and text data and feeds them back to the generative AI model as emotion data. The emotion engine analyzes the tone and content of the customer's voice when they ask a question, extracting emotions such as anxiety, excitement, and interest.

[0866] Machine Learning Algorithms

[0867] A machine learning algorithm is an algorithm that learns patterns and rules from large amounts of data and makes predictions and classifications for new data. In the present invention, it is used to sequentially learn from interactions with customers and improve response accuracy from the next time onwards.

[0868] Specific use cases

[0869] A specific example of use is shown below.

[0870] Example 1: When a customer excitedly asks, "Do you have this item in stock?"

[0871] 1. User (customer): "Do you have this item in stock?" asks the robot terminal excitedly.

[0872] 2. The device receives the voice and converts it into text using speech recognition software.

[0873] 3. The device uses an emotion engine to extract the emotion data "excitement" from the voice.

[0874] 4. The device sends the converted text data and emotion data to the server.

[0875] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate the answer, "Yes, we have it in stock. We'll show you here."

[0876] 6. The server converts the generated answer into voice data and sends it to the device.

[0877] 7. The terminal plays back the received voice data and provides the answer to the customer.

[0878] Example prompt sentence:

[0879] When a customer excitedly asks, "Do you have this item in stock?", you can respond with, "Yes, we do have it in stock. Let me show you."

[0880] Example 2: When a customer anxiously asks, "How do I use this product?"

[0881] 1. User (customer): "How do I use this product?" he asks anxiously to the robot terminal.

[0882] 2. The device receives the voice and converts it into text using speech recognition software.

[0883] 3. The device uses an emotion engine to extract the emotion data "anxiety" from the voice.

[0884] 4. The device sends the converted text data and emotion data to the server.

[0885] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate an answer such as, "This is how to use this product. Please also refer to the instructions."

[0886] 6. The server converts the generated answer into voice data and sends it to the device.

[0887] 7. The terminal plays back the received voice data and provides the answer to the customer.

[0888] Example prompt sentence:

[0889] If a customer asks anxiously, "How do I use this product?", answer, "This is how you use this product. Please read the instructions."

[0890] In this way, by using the system of the present invention, the quality of customer service in physical stores can be improved and customer satisfaction can be increased.

[0891] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0892] Step 1:

[0893] The user (customer) performs voice input. The customer dictates their question or request. The input of this process is the customer's voice data, and the output is the voice data received by the terminal.

[0894] What happens: A customer asks, "Do you have this item in stock?"

[0895] Step 2:

[0896] The terminal receives the voice data and converts it into text data using voice recognition software.

[0897] The input of this process is audio data, and the output is text data. Specific operation: The device transcribes the audio using a speech recognition library (e.g., speech_recognition).

[0898] Step 3:

[0899] The terminal uses an emotion engine to analyze emotions from the voice data and extract emotion data.

[0900] The input of this process is voice data, and the output is emotion data. Specific operation: The device analyzes the voice data using an emotion analysis algorithm (e.g., EmotionRecognition) to identify the emotion.

[0901] Step 4:

[0902] The terminal transmits the converted text data and emotion data to the server.

[0903] The input of this process is text data and emotion data, and the output is the data sent to the server. Specific operation: The device sends the text data and emotion data to the server as an HTTP request.

[0904] Step 5:

[0905] The server analyzes the received text data and emotional data and generates an appropriate answer using a generative AI model.

[0906] The input of this process is text data and emotion data, and the output is the generated answer text. Specific operation: The server inputs the prompt sentence into the generative AI model (e.g., GPT-3) and generates an appropriate answer.

[0907] Step 6:

[0908] The server converts the generated response into audio data.

[0909] The input of this process is the answer text, and the output is audio data. Specific operation: The server converts the answer into audio data using a text-to-speech synthesis engine (e.g., pyttsx3).

[0910] Step 7:

[0911] The server transmits the generated voice data to the terminal.

[0912] The input of this process is audio data, and the output is the audio data sent to the terminal. Specific operation: The server sends the audio data to the terminal as an HTTP response.

[0913] Step 8:

[0914] The terminal plays back the received voice data and provides the answer to the customer.

[0915] The input of this process is voice data, and the output is voice information provided to the customer. Specific operation: The terminal plays the voice data using the speaker and responds, "Yes, we have it in stock. I'll show you here."

[0916] 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.

[0917] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0918] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0919] [Third embodiment]

[0920] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0921] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0922] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0923] 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.

[0924] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0925] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0926] 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.

[0927] 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.

[0928] 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 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.

[0929] 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.

[0930] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0931] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0932] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. The processing flow of the entire system and a specific example are shown below.

[0933] System configuration

[0934] The system mainly consists of the following components:

[0935] 1. Terminal (doll-like device):

[0936] It receives voice data uttered by the child and converts it into text data.

[0937] The audio data sent from the server is played back to provide the answer to the user (child).

[0938] 2. Server:

[0939] Receives and analyzes text data sent from the device.

[0940] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[0941] The converted audio data is sent to the terminal.

[0942] 3. Generative AI Model:

[0943] The text data is analyzed to generate the most appropriate answer based on the child's age and level of understanding.

[0944] Specific examples for carrying out the invention

[0945] Below is a concrete example of how the system responds to a child's question, "Why?"

[0946] Example 1: When a child asks, "Why is the sky blue?"

[0947] 1. User (child): Asks the doll-like device, "Why is the sky blue?"

[0948] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[0949] 3. The device sends the converted text data to the server.

[0950] 4. The server analyzes the received text data and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0951] 5. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[0952] 6. The device plays back the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0953] Example 2: When a child asks, "Why are leaves green?"

[0954] 1. User (child): Asks the doll-like device, "Why are leaves green?"

[0955] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[0956] 3. The device sends the converted text data to the server.

[0957] 4. The server analyzes the text data and uses a generative AI model to generate the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0958] 5. The server converts the generated answer into audio data and sends it to the device.

[0959] 6. The device plays back the received audio data and provides the user (child) with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[0960] In this way, the system of the present invention satisfies children's intellectual curiosity by providing quick and accurate answers to their "why?" questions, while significantly reducing the burden on parents. The entire system processes information with high accuracy through automated processes, and is designed to function as a good partner in the home environment.

[0961] The processing flow will be explained below.

[0962] Step 1:

[0963] The user (child) speaks a question into the doll-shaped terminal.

[0964] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[0965] Step 2:

[0966] The voice data received by the terminal is converted into text data.

[0967] Specific operation: The voice recognition software in the device converts the voice data into text data and generates the text information "Why is the sky blue?"

[0968] Step 3:

[0969] The terminal transmits the converted text data to the server.

[0970] Specific operation: The device uses an HTTP request to upload the converted text data to the server.

[0971] Step 4:

[0972] The server analyzes the received text data.

[0973] Specific operation: The server analyzes the text data "Why is the sky blue?" and inputs it into the generative AI model.

[0974] Step 5:

[0975] The generation AI on the server generates appropriate answers.

[0976] Specific behavior: The generative AI model generates the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light."

[0977] Step 6:

[0978] The server converts the generated response into audio data.

[0979] Specific operation: A TTS (Text-To-Speech) engine that converts text to speech converts the generated response into audio data.

[0980] Step 7:

[0981] The server transmits the generated voice data to the terminal.

[0982] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[0983] Step 8:

[0984] The terminal plays back the received audio data.

[0985] Specific operation: The speaker built into the device plays the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[0986] In this way, through a series of processing steps, quick and friendly answers are provided to children's questions, reducing the burden on parents and satisfying children's intellectual curiosity.

[0987] Example 1

[0988] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0989] The present invention aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing prompt and appropriate answers to questions posed by children. However, conventional systems often lack the accuracy of voice data recognition and the appropriateness of answers, resulting in unsatisfactory results. It has also been difficult to generate answers that correspond to each child's level of understanding and age.

[0990] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0991] In this invention, the server includes means for receiving questions posed by the child as voice data, means for converting the received voice data into text data, means for transmitting the converted text data to the server, means for the server to analyze the text data and generate an appropriate answer using a generative AI model, means for converting the generated answer into voice data, means for transmitting the generated voice data to the terminal, means for the terminal to play the voice data and provide the answer to the child, means for the terminal to use a high-sensitivity microphone and voice recognition software when performing voice recognition and converting the voice data into text data, and means for the server to input a prompt sentence into the generative AI model and generate an answer. This improves the accuracy of voice recognition and the appropriateness of the answer, making it possible to provide a high-level answer according to the child's age and level of understanding.

[0992] "Means for receiving questions posed by children as voice data" refers to a system for acquiring questions posed by children verbally as digital voice data via a voice input device such as a microphone.

[0993] The "means for converting received voice data into text data" refers to a process in which voice data is analyzed using voice recognition software and converted into text data as a string of characters.

[0994] The "means for transmitting the converted text data to the server" is a method for transmitting the text data from the terminal to the server using a communication protocol (for example, HTTP or HTTPS).

[0995] "Means for the server to analyze text data and generate an appropriate answer using a generative AI model" refers to a mechanism in which received text data is processed by an analytical algorithm on the server, and an AI model (e.g., a generative model) is used to generate the optimal answer corresponding to the text data.

[0996] The "means for converting the generated answer into voice data" refers to a process for converting the generated text-based answer into voice data that can be heard by the user using a voice synthesis engine or the like.

[0997] The "means for transmitting generated voice data to the terminal" refers to a method for transferring voice data generated on the server to the terminal via a communication channel.

[0998] "Means for the device to play audio data and provide answers to the child" refers to a mechanism that uses a speaker or other audio output device built into the device to play audio data and provide verbal answers to the user (child).

[0999] "Means for a device to use a high-sensitivity microphone and speech recognition software to perform speech recognition and convert speech data into text data" refers to the process of using a high-sensitivity microphone to capture speech in detail and accurately, and then converting that speech data into text data using speech recognition software (e.g., a speech recognition engine).

[1000] "Means in which the server inputs a prompt sentence into a generative AI model to generate an answer" refers to the process in which the server inputs a prompt sentence into a generative AI model (e.g., a text generation model) to generate an appropriate answer in natural language.

[1001] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. A detailed description of the specific invention is provided below.

[1002] System configuration

[1003] This system consists of the following hardware and software:

[1004] 1. Terminal (doll-shaped device)

[1005] Hardware: High-sensitivity microphone, speaker, communication module.

[1006] Software: Install voice recognition software (e.g., Google Speech-to-Text) to convert received voice data into text data.

[1007] Function: Receives voices uttered by the child and converts them into text data. It also plays back the voice data sent from the server and provides answers to the child.

[1008] 2. Server

[1009] Hardware: A server machine equipped with a high-performance processor.

[1010] Software: Powered by generative AI models (e.g., OpenAI GPT-3) and speech synthesis engines (e.g., Amazon Polly).

[1011] Function: Analyzes text data received from the device and generates appropriate answers using a generative AI model. The generated answers are then converted into voice data and sent to the device.

[1012] 3. Generative AI Models

[1013] Function: Runs on the server, analyzes text data, and generates the most appropriate answer based on the child's age and level of understanding.

[1014] Examples of specific examples and prompts

[1015] Example 1: When a child asks, "Why is the sky blue?"

[1016] 1. The user (child) asks the doll-shaped device, "Why is the sky blue?"

[1017] 2. The device receives the voice and converts it into text data, such as "Why is the sky blue?", using a high-sensitivity microphone and Google Speech-to-Text software.

[1018] 3. The device sends the converted text data to the server.

[1019] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[1020] Your child asks, "Why is the sky blue?" He is 5 years old. Explain it gently and simply so that he can understand.

[1021] 5. The server receives the answer generated by the AI ​​model, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light," and converts it into speech data using Amazon Polly.

[1022] 6. The server sends the generated voice data to the device.

[1023] 7. The device plays back the received audio data and provides the child with the answer, "The sky appears blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[1024] Example 2: When a child asks, "Why are leaves green?"

[1025] 1. The user (child) asks the doll-shaped device, "Why are leaves green?"

[1026] 2. The device receives the voice and converts it into text data, such as "Why are leaves green?", using a sensitive microphone and Google Speech-to-Text software.

[1027] 3. The device sends the converted text data to the server.

[1028] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[1029] Your child asks, "Why are leaves green?" He is 7 years old. Explain it gently and simply so that he can understand.

[1030] 5. The server receives the answer generated by the AI ​​model, "Leaves are green because they contain a pigment called chlorophyll," and converts it into speech data using Amazon Polly.

[1031] 6. The server sends the generated voice data to the device.

[1032] 7. The device plays back the received audio data and provides the child with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[1033] In this way, the present invention integrates advanced technologies such as speech recognition, text generation, and speech synthesis to provide quick and accurate answers to children's questions, thereby satisfying children's intellectual curiosity while reducing the burden on parents.

[1034] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1035] Step 1: The user (child) inputs a question as voice data

[1036] The user (child) asks a question to the doll-shaped device. For example, the child might ask, "Why is the sky blue?" This voice question is received by the device's high-sensitivity microphone.

[1037] Input: A child-uttered vocal question (e.g., "Why is the sky blue?").

[1038] Output: The question as audio data.

[1039] Step 2: Your device converts the audio data into text.

[1040] The device uses built-in voice recognition software (e.g., Google Speech-to-Text) to analyze the received voice data and convert it into text data. Specifically, the voice signal is captured as digital data and converted into a string of characters.

[1041] Input: A question as audio data (e.g., "Why is the sky blue?").

[1042] Output: The question as text data (e.g., "Why is the sky blue?").

[1043] Step 3: The device sends the text data to the server

[1044] The terminal sends the converted text data to the server via an internal network module, using the HTTP or HTTPS protocol.

[1045] Input: A question as text data (e.g., "Why is the sky blue?").

[1046] Output: The text data sent to the server.

[1047] Step 4: The server analyzes the text data and generates an answer using a generative AI model

[1048] The server analyzes the received text data and generates an answer by inputting an appropriate prompt into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt might be, "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it kindly and simply so that a child can understand." The generative AI model generates an answer based on this prompt.

[1049] Input: A question as text data (e.g., "Why is the sky blue?") and a prompt for the generative AI model (e.g., "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it gently and simply so that he can understand.").

[1050] Output: Correct answer (e.g. "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering the blue light").

[1051] Step 5: The server converts the generated answer into audio data.

[1052] The server uses a speech synthesis engine (e.g., Amazon Polly) to convert the generated text response into audio data, which is then converted into audio data (e.g., an MP3 file).

[1053] Input: Answer as text data (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering blue light").

[1054] Output: Answer as audio data (e.g. audio file).

[1055] Step 6: The server sends the audio data to the device

[1056] The server then sends the generated audio data to the device via the network, using the HTTP or HTTPS protocol.

[1057] Input: Answer as audio data (e.g. audio file).

[1058] Output: The audio data sent to the device.

[1059] Step 7: The device plays the audio data and provides it to the user (child).

[1060] The device uses a built-in speaker to play back the received voice data, allowing the user (child) to hear the answer aloud.

[1061] Input: Answer as audio data (e.g. audio file).

[1062] Output: Audio output that the user (child) can hear (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere and scatters blue light").

[1063] (Application example 1)

[1064] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1065] Physical stores, especially educational museums and science museums, require quick and accurate answers to visitors' questions, especially children's. However, conventional guidance systems require staff with specialized knowledge to be on-site at all times, resulting in increased labor costs and delayed responses due to staff shortages. Furthermore, answers may not be consistent or accurate, making it difficult to improve visitor satisfaction. The present invention aims to solve these problems and improve visitor experience by automatically providing highly accurate answers to questions.

[1066] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1067] In this invention, the server includes means for receiving visitor questions as voice data, means for converting the received voice data into text data, means for analyzing the converted text data and generating appropriate answers using a generative AI model, means for converting the generated answers into voice data, means for transmitting the generated voice data to a terminal and playing it back, and means for generating appropriate answers to visitor questions in a physical store and providing voice guidance. This reduces the burden on staff and makes it possible to quickly provide consistent, highly accurate answers.

[1068] "Children" or "visitors" are people who use the system to request information in specific situations, and are further subdivided based on factors such as age and whether or not they have specialized knowledge.

[1069] "Audio data" means audio signals in digital form obtained from human speech or other audio sources.

[1070] "Text data" is information of a character string obtained by analyzing voice data, and is data that includes questions and instructions.

[1071] A "server" is a computing device that processes and analyzes voice and text data and generates a response.

[1072] A "generative AI model" is a model of an artificial intelligence algorithm that analyzes received text data and generates the optimal answer to a question.

[1073] An "appropriate answer" is accurate information that matches the questioner's understanding, based on the results of analysis by the generative AI model.

[1074] A "terminal" is a device for transmitting and receiving voice data and reproducing generated responses.

[1075] "Brick and mortar stores" are physical stores or facilities that visitors actually visit, including educational facilities such as museums and science centers.

[1076] This invention includes a system for providing appropriate answers to questions of visitors in a physical store by voice. The configuration and processing of the entire system will be described in detail below.

[1077] System configuration

[1078] 1. Terminal (guide robot or information kiosk):

[1079] Receives visitor voice data using a microphone

[1080] Converts received voice data into text data using voice recognition software

[1081] Send the converted text data to the server

[1082] Play audio data received from the server on the speaker

[1083] 2. Server:

[1084] Receive and analyze text data sent from the device

[1085] Generate appropriate answers using a generative AI model (e.g., GPT-4)

[1086] Use a Text-to-Speech (TTS) engine to convert the generated answers into audio data.

[1087] Send the generated audio data to the device

[1088] 3. Generative AI Model:

[1089] Analyze text data and generate the most appropriate answer based on the visitor's age and level of understanding

[1090] For example, in response to the question "What is the name of this dinosaur?", it generates the answer "The name of this dinosaur is Tyrannosaurus."

[1091] Hardware and software used

[1092] Hardware:

[1093] Microphone: Receives the visitor's voice

[1094] Speaker: Outputs the generated audio data

[1095] Terminals (guide robots and information kiosks): Act as interfaces

[1096] software:

[1097] speech_recognition: A library that converts speech data into text data

[1098] TextToSpeech: An engine that converts text data into speech data

[1099] AnswerGenerator: Software that analyzes text data using a generative AI model (e.g., GPT-4) and generates answers.

[1100] Specific examples

[1101] Example 1: Dinosaur exhibits in museums

[1102] Question: "What is the name of this dinosaur?"

[1103] Answer: "The name of this dinosaur is Tyrannosaurus."

[1104] Example prompt sentence:

[1105] User input: "What is the name of this dinosaur?"

[1106] Constructing an Answer: The name of this dinosaur is Tyrannosaurus.

[1107] Example 2: Space exhibits at science museums

[1108] Question: "When was the launch date for Apollo 11?"

[1109] Answer: "The launch date of Apollo 11 was July 16, 1969."

[1110] Example prompt sentence:

[1111] User Input: "What is the launch date for Apollo 11?"

[1112] Constructing an Answer: The launch date of Apollo 11 was July 16, 1969.

[1113] In this way, the entire system can provide quick and accurate answers to visitor questions, reducing the burden on staff and enabling them to provide consistent, accurate answers.

[1114] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1115] Step 1:

[1116] A user asks a question to a terminal (a guide robot or an information kiosk) installed in a physical store. At this time, a microphone built into the terminal receives voice data. The input here is the user's speech, and the output is the captured voice data.

[1117] Step 2:

[1118] The device processes the received voice data with speech recognition software and converts it into text data. This process uses a speech recognition library (e.g., speech_recognition). The input is voice data, and the output is text data.

[1119] Step 3:

[1120] The terminal transmits the converted text data to the server. The data transfer here is performed via network communication. The input is the text data, and the output is the text data transmitted to the server.

[1121] Step 4:

[1122] The server receives and analyzes the text data sent from the device. A generative AI model (e.g., GPT-4) is used for the analysis to generate the optimal answer based on the visitor's age and level of understanding. The input is text data, and the output is the generated answer text.

[1123] Step 5:

[1124] The server uses a TTS (Text-to-Speech) engine to convert the generated answer text data into voice data. The input is the answer text data, and the output is voice data.

[1125] Step 6:

[1126] The server transmits the generated voice data to the terminal. Again, data transfer is performed via network communication. The input is the voice data, and the output is the voice data transmitted to the terminal.

[1127] Step 7:

[1128] The device plays the received voice data and provides the answer to the user. This process uses the device's built-in speaker. The input is the voice data, and the output is the answer played back as voice.

[1129] Step 8:

[1130] The device accumulates and analyzes the received voice data and user responses, and applies machine learning algorithms to improve response accuracy in the future. The input is past voice data and user feedback, and the output is an improved voice recognition model.

[1131] These processing steps result in a system that automatically and accurately answers a series of questions.

[1132] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1133] This system aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to children's questions such as "Why?" This system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts responses accordingly.

[1134] System configuration

[1135] The system mainly consists of the following components:

[1136] 1. Terminal (doll-like device):

[1137] It receives voice data uttered by the child and converts it into text data.

[1138] The audio data sent from the server is played back to provide the answer to the user (child).

[1139] 2. Server:

[1140] Receives and analyzes text data and emotion data sent from the device.

[1141] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[1142] The converted audio data is sent to the terminal.

[1143] 3. Generative AI Model:

[1144] It analyzes text data and emotional data to generate the most appropriate answer based on the child's age, level of understanding, and emotions.

[1145] 4. Emotion Engine:

[1146] The user's emotions are analyzed from voice and text data and fed back to the generative AI model as emotional data.

[1147] Specific examples for carrying out the invention

[1148] Below we show the specific behavior of the system in response to a child's "why?" question and their emotions.

[1149] Example 1: If a child asks, in an anxious voice, "Why is the sky blue?"

[1150] 1. User (child): "Why is the sky blue?" asks the doll-like device in an anxious voice.

[1151] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[1152] 3. The device uses an emotion engine to extract emotion data, such as anxiety, from the voice.

[1153] 4. The device sends the converted text data and emotion data to the server.

[1154] 5. The server analyzes the received text data and the emotional data of anxiety, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is completely natural."

[1155] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[1156] 7. The device plays back the received voice data and provides the user (child) with a gentle answer: "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[1157] Example 2: When a child asks, "Why are leaves green?" in an inquisitive voice.

[1158] 1. User (child): "Why are leaves green?" asks the doll-like device in an inquisitive voice.

[1159] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[1160] 3. The device uses an emotion engine to extract emotion data such as interest from the voice.

[1161] 4. The device sends the converted text data and emotion data to the server.

[1162] 5. The server analyzes the received text data and emotional interest data, and uses a generative AI model to generate an answer such as, "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things to know!"

[1163] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[1164] 7. The device plays back the received audio data and provides the user (child) with an answer in an intriguing tone: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things too!"

[1165] In this way, the system of the present invention adjusts responses to children's "why?" questions by taking emotions into account, enabling more friendly and effective communication. This reduces the burden on parents and satisfies children's intellectual curiosity. The entire system is configured to function as a good partner in the home environment by integrating various components, including the emotion engine.

[1166] The processing flow will be explained below.

[1167] Step 1:

[1168] The user (child) speaks a question into the doll-shaped terminal.

[1169] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[1170] Step 2:

[1171] The voice data received by the terminal is converted into text data.

[1172] Specific operation: The voice recognition software in the device converts the voice data into text data such as "Why is the sky blue?"

[1173] Step 3:

[1174] The device uses an emotion engine to extract emotional data from the child's voice.

[1175] Specific operation: The emotion engine analyzes voice data and recognizes the child's emotions, such as anxiety or interest.

[1176] Step 4:

[1177] The terminal transmits the converted text data and emotion data to the server.

[1178] Specific operation: The device uses an HTTP request to upload text data and emotion data to the server.

[1179] Step 5:

[1180] The server analyzes the received text data and emotion data.

[1181] Specific operation: The server analyzes the text data "Why is the sky blue?" and the emotion data "anxiety."

[1182] Step 6:

[1183] The generative AI on the server generates appropriate answers based on text data and emotional data.

[1184] What it does: The generative AI model generates the answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[1185] Step 7:

[1186] The server converts the generated response into audio data.

[1187] Specific operation: The TTS (Text-To-Speech) engine in the server converts the generated answer into voice data.

[1188] Step 8:

[1189] The server transmits the generated voice data to the terminal.

[1190] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[1191] Step 9:

[1192] The terminal plays back the received audio data.

[1193] What it does: The device's built-in speaker plays the received audio data. The answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing," is delivered in a gentle tone to the anxious child.

[1194] In this way, by incorporating emotional data, the system can provide answers that are in tune with the child's feelings, resulting in more friendly and effective communication, which reduces the burden on parents and satisfies the child's intellectual curiosity.

[1195] Example 2

[1196] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1197] It takes a lot of time and effort for parents to quickly and appropriately answer the "why?" questions that children frequently ask. Furthermore, it is extremely difficult to provide answers that reflect the child's emotions. Therefore, there is a need to develop a system that can satisfy children's intellectual curiosity while reducing the burden on parents. Another challenge is to improve the accuracy of responses to similar questions.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1199] In this invention, the server includes means for receiving questions posed by children as voice data, means for converting the received voice data into text data, and means for extracting the converted text data and emotion data. This makes it possible to generate and provide appropriate answers to questions posed by children quickly and taking emotion into consideration. Furthermore, by including means for successively evolving the voice data and emotion data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards, the quality of answers to similar questions can be continuously improved.

[1200] "Children" refers to young people who use the system and ask questions aloud.

[1201] A "question" refers to a verbal expression of a child's doubts.

[1202] "Audio data" refers to information that has been recorded as sound in response to questions uttered by children and converted into digital format.

[1203] "Text data" refers to digital data in which voice data is converted into text information using voice recognition technology.

[1204] "Server" refers to a computer system that receives and analyzes data over a network and generates appropriate answers using a generative AI model.

[1205] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers from given input data based on a pre-trained algorithm.

[1206] "Emotional data" refers to data that is generated by analyzing emotions from the user's tone of voice, words, etc., and then expressing the results in digital form.

[1207] "Speech recognition software" refers to software that receives voice data as input and converts that voice into text data.

[1208] An "emotion engine" refers to a system that analyzes user emotions from voice data and feeds this back to a generative AI model as emotional data.

[1209] "Audio data transmission means" refers to a communication means for transmitting audio data generated by the server to the terminal.

[1210] A "machine learning algorithm" is an algorithm that allows the system to self-learn based on past data and improve the accuracy of responses from the next time onwards.

[1211] This invention is a system that aims to reduce the burden on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to the "why?" questions that children often ask on a daily basis. The system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts the response accordingly. This system mainly consists of the following components:

[1212] System Configuration

[1213] 1. Terminal (doll-like device):

[1214] The system receives the voice data uttered by the child and converts it into text data. Specifically, the device's built-in microphone captures the voice and converts it into text data using voice recognition software (e.g., Google Speech-to-Text API). Furthermore, it uses an emotion engine to extract emotional data from the voice and sends it to the server.

[1215] The audio data sent from the server is played back to provide the answer to the user (child). Specifically, the audio data received by the device's built-in speaker is played back (e.g., MP3 format data generated using the Google Text-to-Speech API).

[1216] 2. Server:

[1217] The server receives and analyzes the text data and emotion data sent from the device. Specifically, the server analyzes the received JSON data and extracts the text data and emotion data.

[1218] A generative AI model is used to generate an appropriate response, which is then converted into voice data. Specifically, the server uses a generative AI model (e.g., OpenAI GPT-4) to input a prompt and generate an appropriate text response. The response is then converted into voice data using the Google Text-to-Speech API and sent to the device.

[1219] The converted audio data is sent to the device. Specifically, the audio data (MP3 format) is sent to the device via Wi-Fi.

[1220] 3. Generative AI Model:

[1221] It analyzes text and emotion data to generate the most appropriate answer based on the child's age, level of understanding, and emotions. Example prompt: "The sky appears blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a completely natural process."

[1222] Specific examples

[1223] 1. Example 1: When a child asks, in an anxious voice, "Why is the sky blue?"

[1224] When a user (child) asks "Why is the sky blue?" in an anxious voice to the doll-shaped device, the device receives the voice and converts it into text data, "Why is the sky blue?" using speech recognition software. The device then uses an emotion engine to extract emotion data, such as anxiety, from the voice and sends the converted text data and emotion data to the server. The server then analyzes the received text data and emotion data, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing." The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in a gentle tone.

[1225] 2. Example 2: When a child asks, in an inquisitive voice, "Why are leaves green?"

[1226] When a user (child) asks the doll-shaped device in an inquisitive voice, "Why are leaves green?", the device receives the voice and uses speech recognition software to convert it into text data: "Why are leaves green?". The device then uses an emotion engine to extract emotional interest data from the voice and sends the converted text data and emotional data to the server. The server analyzes the received text data and emotional interest data and uses a generative AI model to generate an answer: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things about them!" The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in an inquisitive tone.

[1227] As described above, this invention aims to effectively utilize the emotion engine and generative AI model to function as a good partner in the home environment, reducing the burden on parents and satisfying children's intellectual curiosity.

[1228] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1229] Step 1:

[1230] The user (child) asks a question

[1231] The user (child) speaks a question into the terminal.

[1232] Specific action: A child asks the device, "Why is the sky blue?"

[1233] Input: Voice question

[1234] Output: Audio data

[1235] Step 2:

[1236] The device receives the voice data and converts it into text data.

[1237] The terminal receives the user's voice data and converts it into text data using voice recognition software.

[1238] Specific operation: The microphone on the device captures the voice and uses speech recognition software (e.g., Google Speech-to-Text API) to generate text data such as "Why is the sky blue?"

[1239] Input: Audio data

[1240] Output: Text data

[1241] Step 3:

[1242] The device uses an emotion engine to extract emotion data such as anxiety.

[1243] The terminal inputs the voice data into an emotion engine and extracts emotion data.

[1244] Specific operation: The device analyzes the emotion "anxiety" from the voice using an emotion engine (e.g., OpenAI's emotion analysis model).

[1245] Input: Audio data

[1246] Output: Emotion data (anxiety)

[1247] Step 4:

[1248] The device sends text data and emotion data to the server.

[1249] The device transmits the generated text data and emotion data to the server.

[1250] Specific operation: The device uses Wi-Fi to send the text data "Why is the sky blue?" and the emotion data "anxiety" in JSON format to the server.

[1251] Input: Text data, emotion data

[1252] Output: JSON data

[1253] Step 5:

[1254] The server receives and analyzes the text data and emotion data.

[1255] The server receives the JSON data sent from the device and analyzes the text data and emotion data.

[1256] Specific operation: The server analyzes the received JSON data and extracts the text data "Why is the sky blue?" and the emotion data "Anxiety."

[1257] Input: JSON data

[1258] Output: Text data, emotion data

[1259] Step 6:

[1260] The server uses the generative AI model to generate the appropriate answer

[1261] The server inputs text data and emotional data into the generative AI model and generates an appropriate answer.

[1262] What it does: The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer like, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[1263] Input: Text data, emotion data

[1264] Output: Answer text

[1265] Step 7:

[1266] The server converts the generated answer into audio data.

[1267] The server converts the generated text response into audio data.

[1268] Specific operation: The server uses the Google Text-to-Speech API to convert the generated text response into audio data (MP3 format).

[1269] Input: Answer text

[1270] Output: Audio data

[1271] Step 8:

[1272] The server sends the audio data to the device.

[1273] The server transmits the generated voice data to the terminal.

[1274] Specific operation: The server sends audio data to the device via Wi-Fi.

[1275] Input: Audio data

[1276] Output: Transmitted audio data

[1277] Step 9:

[1278] The device plays back the received voice data and provides the child with a response.

[1279] The terminal plays back the voice data received from the server and provides the answer to the user (child).

[1280] What it does: The speaker on the device plays a gentle voice saying, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[1281] Input: Transmitted audio data

[1282] Output: Answer provided through speech

[1283] (Application example 2)

[1284] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1285] In today's brick-and-mortar stores, it is difficult to consistently provide high-quality answers and suggestions to the diverse questions and requests customers ask. It is also necessary to properly recognize customer emotions and adjust responses accordingly, but current systems are not sufficient in this regard. Furthermore, while it is important to achieve friendly and effective communication in customer service, existing systems also have limitations in this regard. It is necessary to solve these problems and provide services that will satisfy customers.

[1286] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1287] In this invention, the server includes means for analyzing text data and generating appropriate answers using a generative AI model, means for analyzing emotion data and generating answers based on the emotions, and means for converting the generated answers into voice data. This makes it possible to improve the quality of customer service in physical stores and increase customer satisfaction by recognizing customer emotions and adjusting answers accordingly.

[1288] "Audio data" refers to data that has been recorded and stored in digital format.

[1289] "Text data" refers to data in which character information is recorded and stored in digital format.

[1290] A "server" is a computer or system that manages and processes data on a network.

[1291] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate appropriate answers and information from given data.

[1292] "Emotional data" is data extracted from a user's voice or text that indicates their emotions or emotional state.

[1293] A "terminal" is a device used by a user that receives and plays audio data.

[1294] An "emotion engine" is an algorithm or system that analyzes emotions from voice and text data and generates emotional data.

[1295] A "machine learning algorithm" is a method of learning patterns and rules from large amounts of data and making predictions and classifications for new data.

[1296] MODE FOR CARRYING OUT THE INVENTION

[1297] The system of the present invention is primarily designed to improve customer service in brick-and-mortar stores, and specific embodiments thereof are described below.

[1298] Overall system configuration

[1299] The overall system consists of the following main components:

[1300] 1. Terminal

[1301] 2. Server

[1302] 3. Generative AI Models

[1303] 4. Emotion Engine

[1304] 5. Machine Learning Algorithms

[1305] Detailed System Description

[1306] Terminal

[1307] The terminal is a customer service robot installed in a brick-and-mortar store that receives voice input from customers. The terminal converts the received voice data into text data using voice recognition software and sends this text data to a server. The terminal also receives the answer generated by the generative AI model as voice data and provides it to the customer as voice output.

[1308] server

[1309] The server receives and analyzes the text data and emotion data sent from the device. Using a generative AI model and emotion engine, the server generates an appropriate answer to the customer's question and converts it into voice data. This converted voice data is then sent back to the device.

[1310] Generative AI Models

[1311] A generative AI model uses machine learning algorithms to generate appropriate responses based on text data and emotional data sent from a device. Generative AI models learn from past interaction data to improve response accuracy.

[1312] Emotion Engine

[1313] The emotion engine is an algorithm that analyzes customer emotions from voice and text data and feeds them back to the generative AI model as emotion data. The emotion engine analyzes the tone and content of the customer's voice when they ask a question, extracting emotions such as anxiety, excitement, and interest.

[1314] Machine Learning Algorithms

[1315] A machine learning algorithm is an algorithm that learns patterns and rules from large amounts of data and makes predictions and classifications for new data. In the present invention, it is used to sequentially learn from interactions with customers and improve response accuracy from the next time onwards.

[1316] Specific use cases

[1317] A specific example of use is shown below.

[1318] Example 1: When a customer excitedly asks, "Do you have this item in stock?"

[1319] 1. User (customer): "Do you have this item in stock?" asks the robot terminal excitedly.

[1320] 2. The device receives the voice and converts it into text using speech recognition software.

[1321] 3. The device uses an emotion engine to extract the emotion data "excitement" from the voice.

[1322] 4. The device sends the converted text data and emotion data to the server.

[1323] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate the answer, "Yes, we have it in stock. We'll show you here."

[1324] 6. The server converts the generated answer into voice data and sends it to the device.

[1325] 7. The terminal plays back the received voice data and provides the answer to the customer.

[1326] Example prompt sentence:

[1327] When a customer excitedly asks, "Do you have this item in stock?", you can respond with, "Yes, we do have it in stock. Let me show you."

[1328] Example 2: When a customer anxiously asks, "How do I use this product?"

[1329] 1. User (customer): "How do I use this product?" he asks anxiously to the robot terminal.

[1330] 2. The device receives the voice and converts it into text using speech recognition software.

[1331] 3. The device uses an emotion engine to extract the emotion data "anxiety" from the voice.

[1332] 4. The device sends the converted text data and emotion data to the server.

[1333] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate an answer such as, "This is how to use this product. Please also refer to the instructions."

[1334] 6. The server converts the generated answer into voice data and sends it to the device.

[1335] 7. The terminal plays back the received voice data and provides the answer to the customer.

[1336] Example prompt sentence:

[1337] If a customer asks anxiously, "How do I use this product?", answer, "This is how you use this product. Please read the instructions."

[1338] In this way, by using the system of the present invention, the quality of customer service in physical stores can be improved and customer satisfaction can be increased.

[1339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1340] Step 1:

[1341] The user (customer) performs voice input. The customer dictates their question or request. The input of this process is the customer's voice data, and the output is the voice data received by the terminal.

[1342] What happens: A customer asks, "Do you have this item in stock?"

[1343] Step 2:

[1344] The terminal receives the voice data and converts it into text data using voice recognition software.

[1345] The input of this process is audio data, and the output is text data. Specific operation: The device transcribes the audio using a speech recognition library (e.g., speech_recognition).

[1346] Step 3:

[1347] The terminal uses an emotion engine to analyze emotions from the voice data and extract emotion data.

[1348] The input of this process is voice data, and the output is emotion data. Specific operation: The device analyzes the voice data using an emotion analysis algorithm (e.g., EmotionRecognition) to identify the emotion.

[1349] Step 4:

[1350] The terminal transmits the converted text data and emotion data to the server.

[1351] The input of this process is text data and emotion data, and the output is the data sent to the server. Specific operation: The device sends the text data and emotion data to the server as an HTTP request.

[1352] Step 5:

[1353] The server analyzes the received text data and emotional data and generates an appropriate answer using a generative AI model.

[1354] The input of this process is text data and emotion data, and the output is the generated answer text. Specific operation: The server inputs the prompt sentence into the generative AI model (e.g., GPT-3) and generates an appropriate answer.

[1355] Step 6:

[1356] The server converts the generated response into audio data.

[1357] The input of this process is the answer text, and the output is audio data. Specific operation: The server converts the answer into audio data using a text-to-speech synthesis engine (e.g., pyttsx3).

[1358] Step 7:

[1359] The server transmits the generated voice data to the terminal.

[1360] The input of this process is audio data, and the output is the audio data sent to the terminal. Specific operation: The server sends the audio data to the terminal as an HTTP response.

[1361] Step 8:

[1362] The terminal plays back the received voice data and provides the answer to the customer.

[1363] The input of this process is voice data, and the output is voice information provided to the customer. Specific operation: The terminal plays the voice data using the speaker and responds, "Yes, we have it in stock. I'll show you here."

[1364] 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.

[1365] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1366] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1367] [Fourth embodiment]

[1368] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1369] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1370] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1371] 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.

[1372] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1373] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1374] 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.

[1375] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1376] 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.

[1377] 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 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.

[1378] 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.

[1379] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1380] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1381] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. The processing flow of the entire system and a specific example are shown below.

[1382] System configuration

[1383] The system mainly consists of the following components:

[1384] 1. Terminal (doll-like device):

[1385] It receives voice data uttered by the child and converts it into text data.

[1386] The audio data sent from the server is played back to provide the answer to the user (child).

[1387] 2. Server:

[1388] Receives and analyzes text data sent from the device.

[1389] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[1390] The converted audio data is sent to the terminal.

[1391] 3. Generative AI Model:

[1392] The text data is analyzed to generate the most appropriate answer based on the child's age and level of understanding.

[1393] Specific examples for carrying out the invention

[1394] Below is a concrete example of how the system responds to a child's question, "Why?"

[1395] Example 1: When a child asks, "Why is the sky blue?"

[1396] 1. User (child): Asks the doll-like device, "Why is the sky blue?"

[1397] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[1398] 3. The device sends the converted text data to the server.

[1399] 4. The server analyzes the received text data and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[1400] 5. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[1401] 6. The device plays back the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[1402] Example 2: When a child asks, "Why are leaves green?"

[1403] 1. User (child): Asks the doll-like device, "Why are leaves green?"

[1404] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[1405] 3. The device sends the converted text data to the server.

[1406] 4. The server analyzes the text data and uses a generative AI model to generate the answer, "Leaves are green because they contain a pigment called chlorophyll."

[1407] 5. The server converts the generated answer into audio data and sends it to the device.

[1408] 6. The device plays back the received audio data and provides the user (child) with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[1409] In this way, the system of the present invention satisfies children's intellectual curiosity by providing quick and accurate answers to their "why?" questions, while significantly reducing the burden on parents. The entire system processes information with high accuracy through automated processes, and is designed to function as a good partner in the home environment.

[1410] The processing flow will be explained below.

[1411] Step 1:

[1412] The user (child) speaks a question into the doll-shaped terminal.

[1413] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[1414] Step 2:

[1415] The voice data received by the terminal is converted into text data.

[1416] Specific operation: The voice recognition software in the device converts the voice data into text data and generates the text information "Why is the sky blue?"

[1417] Step 3:

[1418] The terminal transmits the converted text data to the server.

[1419] Specific operation: The device uses an HTTP request to upload the converted text data to the server.

[1420] Step 4:

[1421] The server analyzes the received text data.

[1422] Specific operation: The server analyzes the text data "Why is the sky blue?" and inputs it into the generative AI model.

[1423] Step 5:

[1424] The generation AI on the server generates appropriate answers.

[1425] Specific behavior: The generative AI model generates the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light."

[1426] Step 6:

[1427] The server converts the generated response into audio data.

[1428] Specific operation: A TTS (Text-To-Speech) engine that converts text to speech converts the generated response into audio data.

[1429] Step 7:

[1430] The server transmits the generated voice data to the terminal.

[1431] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[1432] Step 8:

[1433] The terminal plays back the received audio data.

[1434] Specific operation: The speaker built into the device plays the received audio data and provides the user (child) with the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[1435] In this way, through a series of processing steps, quick and friendly answers are provided to children's questions, reducing the burden on parents and satisfying children's intellectual curiosity.

[1436] Example 1

[1437] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1438] The present invention aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing prompt and appropriate answers to questions posed by children. However, conventional systems often lack the accuracy of voice data recognition and the appropriateness of answers, resulting in unsatisfactory results. It has also been difficult to generate answers that correspond to each child's level of understanding and age.

[1439] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1440] In this invention, the server includes means for receiving questions posed by the child as voice data, means for converting the received voice data into text data, means for transmitting the converted text data to the server, means for the server to analyze the text data and generate an appropriate answer using a generative AI model, means for converting the generated answer into voice data, means for transmitting the generated voice data to the terminal, means for the terminal to play the voice data and provide the answer to the child, means for the terminal to use a high-sensitivity microphone and voice recognition software when performing voice recognition and converting the voice data into text data, and means for the server to input a prompt sentence into the generative AI model and generate an answer. This improves the accuracy of voice recognition and the appropriateness of the answer, making it possible to provide a high-level answer according to the child's age and level of understanding.

[1441] "Means for receiving questions posed by children as voice data" refers to a system for acquiring questions posed by children verbally as digital voice data via a voice input device such as a microphone.

[1442] The "means for converting received voice data into text data" refers to a process in which voice data is analyzed using voice recognition software and converted into text data as a string of characters.

[1443] The "means for transmitting the converted text data to the server" is a method for transmitting the text data from the terminal to the server using a communication protocol (for example, HTTP or HTTPS).

[1444] "Means for the server to analyze text data and generate an appropriate answer using a generative AI model" refers to a mechanism in which received text data is processed by an analytical algorithm on the server, and an AI model (e.g., a generative model) is used to generate the optimal answer corresponding to the text data.

[1445] The "means for converting the generated answer into voice data" refers to a process for converting the generated text-based answer into voice data that can be heard by the user using a voice synthesis engine or the like.

[1446] The "means for transmitting generated voice data to the terminal" refers to a method for transferring voice data generated on the server to the terminal via a communication channel.

[1447] "Means for the device to play audio data and provide answers to the child" refers to a mechanism that uses a speaker or other audio output device built into the device to play audio data and provide verbal answers to the user (child).

[1448] "Means for a device to use a high-sensitivity microphone and speech recognition software to perform speech recognition and convert speech data into text data" refers to the process of using a high-sensitivity microphone to capture speech in detail and accurately, and then converting that speech data into text data using speech recognition software (e.g., a speech recognition engine).

[1449] "Means in which the server inputs a prompt sentence into a generative AI model to generate an answer" refers to the process in which the server inputs a prompt sentence into a generative AI model (e.g., a text generation model) to generate an appropriate answer in natural language.

[1450] The present invention is a system that receives questions posed by children as voice data and generates appropriate answers to those questions, thereby reducing the burden and stress on parents and satisfying the intellectual curiosity of children. A detailed description of the specific invention is provided below.

[1451] System configuration

[1452] This system consists of the following hardware and software:

[1453] 1. Terminal (doll-shaped device)

[1454] Hardware: High-sensitivity microphone, speaker, communication module.

[1455] Software: Install voice recognition software (e.g., Google Speech-to-Text) to convert received voice data into text data.

[1456] Function: Receives voices uttered by the child and converts them into text data. It also plays back the voice data sent from the server and provides answers to the child.

[1457] 2. Server

[1458] Hardware: A server machine equipped with a high-performance processor.

[1459] Software: Powered by generative AI models (e.g., OpenAI GPT-3) and speech synthesis engines (e.g., Amazon Polly).

[1460] Function: Analyzes text data received from the device and generates appropriate answers using a generative AI model. The generated answers are then converted into voice data and sent to the device.

[1461] 3. Generative AI Models

[1462] Function: Runs on the server, analyzes text data, and generates the most appropriate answer based on the child's age and level of understanding.

[1463] Examples of specific examples and prompts

[1464] Example 1: When a child asks, "Why is the sky blue?"

[1465] 1. The user (child) asks the doll-shaped device, "Why is the sky blue?"

[1466] 2. The device receives the voice and converts it into text data, such as "Why is the sky blue?", using a high-sensitivity microphone and Google Speech-to-Text software.

[1467] 3. The device sends the converted text data to the server.

[1468] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[1469] Your child asks, "Why is the sky blue?" He is 5 years old. Explain it gently and simply so that he can understand.

[1470] 5. The server receives the answer generated by the AI ​​model, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light," and converts it into speech data using Amazon Polly.

[1471] 6. The server sends the generated voice data to the device.

[1472] 7. The device plays back the received audio data and provides the child with the answer, "The sky appears blue because sunlight reacts with molecules in the atmosphere and scatters blue light."

[1473] Example 2: When a child asks, "Why are leaves green?"

[1474] 1. The user (child) asks the doll-shaped device, "Why are leaves green?"

[1475] 2. The device receives the voice and converts it into text data, such as "Why are leaves green?", using a sensitive microphone and Google Speech-to-Text software.

[1476] 3. The device sends the converted text data to the server.

[1477] 4. The server analyzes the received text data and inputs the following prompt sentence into the generative AI model:

[1478] Your child asks, "Why are leaves green?" He is 7 years old. Explain it gently and simply so that he can understand.

[1479] 5. The server receives the answer generated by the AI ​​model, "Leaves are green because they contain a pigment called chlorophyll," and converts it into speech data using Amazon Polly.

[1480] 6. The server sends the generated voice data to the device.

[1481] 7. The device plays back the received audio data and provides the child with the answer, "Leaves are green because they contain a pigment called chlorophyll."

[1482] In this way, the present invention integrates advanced technologies such as speech recognition, text generation, and speech synthesis to provide quick and accurate answers to children's questions, thereby satisfying children's intellectual curiosity while reducing the burden on parents.

[1483] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1484] Step 1: The user (child) inputs a question as voice data

[1485] The user (child) asks a question to the doll-shaped device. For example, the child might ask, "Why is the sky blue?" This voice question is received by the device's high-sensitivity microphone.

[1486] Input: A child-uttered vocal question (e.g., "Why is the sky blue?").

[1487] Output: The question as audio data.

[1488] Step 2: Your device converts the audio data into text.

[1489] The device uses built-in voice recognition software (e.g., Google Speech-to-Text) to analyze the received voice data and convert it into text data. Specifically, the voice signal is captured as digital data and converted into a string of characters.

[1490] Input: A question as audio data (e.g., "Why is the sky blue?").

[1491] Output: The question as text data (e.g., "Why is the sky blue?").

[1492] Step 3: The device sends the text data to the server

[1493] The terminal sends the converted text data to the server via an internal network module, using the HTTP or HTTPS protocol.

[1494] Input: A question as text data (e.g., "Why is the sky blue?").

[1495] Output: The text data sent to the server.

[1496] Step 4: The server analyzes the text data and generates an answer using a generative AI model

[1497] The server analyzes the received text data and generates an answer by inputting an appropriate prompt into a generative AI model (e.g., OpenAI GPT-3). An example of a prompt might be, "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it kindly and simply so that a child can understand." The generative AI model generates an answer based on this prompt.

[1498] Input: A question as text data (e.g., "Why is the sky blue?") and a prompt for the generative AI model (e.g., "My child asked me, 'Why is the sky blue?' He is 5 years old. Please explain it gently and simply so that he can understand.").

[1499] Output: Correct answer (e.g. "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering the blue light").

[1500] Step 5: The server converts the generated answer into audio data.

[1501] The server uses a speech synthesis engine (e.g., Amazon Polly) to convert the generated text response into audio data, which is then converted into audio data (e.g., an MP3 file).

[1502] Input: Answer as text data (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere, scattering blue light").

[1503] Output: Answer as audio data (e.g. audio file).

[1504] Step 6: The server sends the audio data to the device

[1505] The server then sends the generated audio data to the device via the network, using the HTTP or HTTPS protocol.

[1506] Input: Answer as audio data (e.g. audio file).

[1507] Output: The audio data sent to the device.

[1508] Step 7: The device plays the audio data and provides it to the user (child).

[1509] The device uses a built-in speaker to play back the received voice data, allowing the user (child) to hear the answer aloud.

[1510] Input: Answer as audio data (e.g. audio file).

[1511] Output: Audio output that the user (child) can hear (e.g., "The sky is blue because sunlight interacts with molecules in the atmosphere and scatters blue light").

[1512] (Application example 1)

[1513] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1514] Physical stores, especially educational museums and science museums, require quick and accurate answers to visitors' questions, especially children's. However, conventional guidance systems require staff with specialized knowledge to be on-site at all times, resulting in increased labor costs and delayed responses due to staff shortages. Furthermore, answers may not be consistent or accurate, making it difficult to improve visitor satisfaction. The present invention aims to solve these problems and improve visitor experience by automatically providing highly accurate answers to questions.

[1515] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1516] In this invention, the server includes means for receiving visitor questions as voice data, means for converting the received voice data into text data, means for analyzing the converted text data and generating appropriate answers using a generative AI model, means for converting the generated answers into voice data, means for transmitting the generated voice data to a terminal and playing it back, and means for generating appropriate answers to visitor questions in a physical store and providing voice guidance. This reduces the burden on staff and makes it possible to quickly provide consistent, highly accurate answers.

[1517] "Children" or "visitors" are people who use the system to request information in specific situations, and are further subdivided based on factors such as age and whether or not they have specialized knowledge.

[1518] "Audio data" means audio signals in digital form obtained from human speech or other audio sources.

[1519] "Text data" is information of a character string obtained by analyzing voice data, and is data that includes questions and instructions.

[1520] A "server" is a computing device that processes and analyzes voice and text data and generates a response.

[1521] A "generative AI model" is a model of an artificial intelligence algorithm that analyzes received text data and generates the optimal answer to a question.

[1522] An "appropriate answer" is accurate information that matches the questioner's understanding, based on the results of analysis by the generative AI model.

[1523] A "terminal" is a device for transmitting and receiving voice data and reproducing generated responses.

[1524] "Brick and mortar stores" are physical stores or facilities that visitors actually visit, including educational facilities such as museums and science centers.

[1525] This invention includes a system for providing appropriate answers to questions of visitors in a physical store by voice. The configuration and processing of the entire system will be described in detail below.

[1526] System configuration

[1527] 1. Terminal (guide robot or information kiosk):

[1528] Receives visitor voice data using a microphone

[1529] Converts received voice data into text data using voice recognition software

[1530] Send the converted text data to the server

[1531] Play audio data received from the server on the speaker

[1532] 2. Server:

[1533] Receive and analyze text data sent from the device

[1534] Generate appropriate answers using a generative AI model (e.g., GPT-4)

[1535] Use a Text-to-Speech (TTS) engine to convert the generated answers into audio data.

[1536] Send the generated audio data to the device

[1537] 3. Generative AI Model:

[1538] Analyze text data and generate the most appropriate answer based on the visitor's age and level of understanding

[1539] For example, in response to the question "What is the name of this dinosaur?", it generates the answer "The name of this dinosaur is Tyrannosaurus."

[1540] Hardware and software used

[1541] Hardware:

[1542] Microphone: Receives the visitor's voice

[1543] Speaker: Outputs the generated audio data

[1544] Terminals (guide robots and information kiosks): Act as interfaces

[1545] software:

[1546] speech_recognition: A library that converts speech data into text data

[1547] TextToSpeech: An engine that converts text data into speech data

[1548] AnswerGenerator: Software that analyzes text data using a generative AI model (e.g., GPT-4) and generates answers.

[1549] Specific examples

[1550] Example 1: Dinosaur exhibits in museums

[1551] Question: "What is the name of this dinosaur?"

[1552] Answer: "The name of this dinosaur is Tyrannosaurus."

[1553] Example prompt sentence:

[1554] User input: "What is the name of this dinosaur?"

[1555] Constructing an Answer: The name of this dinosaur is Tyrannosaurus.

[1556] Example 2: Space exhibits at science museums

[1557] Question: "When was the launch date for Apollo 11?"

[1558] Answer: "The launch date of Apollo 11 was July 16, 1969."

[1559] Example prompt sentence:

[1560] User Input: "What is the launch date for Apollo 11?"

[1561] Constructing an Answer: The launch date of Apollo 11 was July 16, 1969.

[1562] In this way, the entire system can provide quick and accurate answers to visitor questions, reducing the burden on staff and enabling them to provide consistent, accurate answers.

[1563] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1564] Step 1:

[1565] A user asks a question to a terminal (a guide robot or an information kiosk) installed in a physical store. At this time, a microphone built into the terminal receives voice data. The input here is the user's speech, and the output is the captured voice data.

[1566] Step 2:

[1567] The device processes the received voice data with speech recognition software and converts it into text data. This process uses a speech recognition library (e.g., speech_recognition). The input is voice data, and the output is text data.

[1568] Step 3:

[1569] The terminal transmits the converted text data to the server. The data transfer here is performed via network communication. The input is the text data, and the output is the text data transmitted to the server.

[1570] Step 4:

[1571] The server receives and analyzes the text data sent from the device. A generative AI model (e.g., GPT-4) is used for the analysis to generate the optimal answer based on the visitor's age and level of understanding. The input is text data, and the output is the generated answer text.

[1572] Step 5:

[1573] The server uses a TTS (Text-to-Speech) engine to convert the generated answer text data into voice data. The input is the answer text data, and the output is voice data.

[1574] Step 6:

[1575] The server transmits the generated voice data to the terminal. Again, data transfer is performed via network communication. The input is the voice data, and the output is the voice data transmitted to the terminal.

[1576] Step 7:

[1577] The device plays the received voice data and provides the answer to the user. This process uses the device's built-in speaker. The input is the voice data, and the output is the answer played back as voice.

[1578] Step 8:

[1579] The device accumulates and analyzes the received voice data and user responses, and applies machine learning algorithms to improve response accuracy in the future. The input is past voice data and user feedback, and the output is an improved voice recognition model.

[1580] These processing steps result in a system that automatically and accurately answers a series of questions.

[1581] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1582] This system aims to reduce the burden and stress on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to children's questions such as "Why?" This system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts responses accordingly.

[1583] System configuration

[1584] The system mainly consists of the following components:

[1585] 1. Terminal (doll-like device):

[1586] It receives voice data uttered by the child and converts it into text data.

[1587] The audio data sent from the server is played back to provide the answer to the user (child).

[1588] 2. Server:

[1589] Receives and analyzes text data and emotion data sent from the device.

[1590] A generative AI model is used to generate appropriate answers, which are then converted into audio data.

[1591] The converted audio data is sent to the terminal.

[1592] 3. Generative AI Model:

[1593] It analyzes text data and emotional data to generate the most appropriate answer based on the child's age, level of understanding, and emotions.

[1594] 4. Emotion Engine:

[1595] The user's emotions are analyzed from voice and text data and fed back to the generative AI model as emotional data.

[1596] Specific examples for carrying out the invention

[1597] Below we show the specific behavior of the system in response to a child's "why?" question and their emotions.

[1598] Example 1: If a child asks, in an anxious voice, "Why is the sky blue?"

[1599] 1. User (child): "Why is the sky blue?" asks the doll-like device in an anxious voice.

[1600] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why is the sky blue?"

[1601] 3. The device uses an emotion engine to extract emotion data, such as anxiety, from the voice.

[1602] 4. The device sends the converted text data and emotion data to the server.

[1603] 5. The server analyzes the received text data and the emotional data of anxiety, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is completely natural."

[1604] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[1605] 7. The device plays back the received voice data and provides the user (child) with a gentle answer: "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[1606] Example 2: When a child asks, "Why are leaves green?" in an inquisitive voice.

[1607] 1. User (child): "Why are leaves green?" asks the doll-like device in an inquisitive voice.

[1608] 2. The device receives the voice and uses speech recognition software to convert it into text: "Why are leaves green?"

[1609] 3. The device uses an emotion engine to extract emotion data such as interest from the voice.

[1610] 4. The device sends the converted text data and emotion data to the server.

[1611] 5. The server analyzes the received text data and emotional interest data, and uses a generative AI model to generate an answer such as, "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things to know!"

[1612] 6. The server converts the generated answer into voice data (using a TTS engine) and sends it to the device.

[1613] 7. The device plays back the received audio data and provides the user (child) with an answer in an intriguing tone: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things too!"

[1614] In this way, the system of the present invention adjusts responses to children's "why?" questions by taking emotions into account, enabling more friendly and effective communication. This reduces the burden on parents and satisfies children's intellectual curiosity. The entire system is configured to function as a good partner in the home environment by integrating various components, including the emotion engine.

[1615] The processing flow will be explained below.

[1616] Step 1:

[1617] The user (child) speaks a question into the doll-shaped terminal.

[1618] Specific action: A child asks, "Why is the sky blue?" The device's built-in microphone picks up the voice.

[1619] Step 2:

[1620] The voice data received by the terminal is converted into text data.

[1621] Specific operation: The voice recognition software in the device converts the voice data into text data such as "Why is the sky blue?"

[1622] Step 3:

[1623] The device uses an emotion engine to extract emotional data from the child's voice.

[1624] Specific operation: The emotion engine analyzes voice data and recognizes the child's emotions, such as anxiety or interest.

[1625] Step 4:

[1626] The terminal transmits the converted text data and emotion data to the server.

[1627] Specific operation: The device uses an HTTP request to upload text data and emotion data to the server.

[1628] Step 5:

[1629] The server analyzes the received text data and emotion data.

[1630] Specific operation: The server analyzes the text data "Why is the sky blue?" and the emotion data "anxiety."

[1631] Step 6:

[1632] The generative AI on the server generates appropriate answers based on text data and emotional data.

[1633] What it does: The generative AI model generates the answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[1634] Step 7:

[1635] The server converts the generated response into audio data.

[1636] Specific operation: The TTS (Text-To-Speech) engine in the server converts the generated answer into voice data.

[1637] Step 8:

[1638] The server transmits the generated voice data to the terminal.

[1639] Specific operation: The generated audio data is sent to the terminal via an HTTP response.

[1640] Step 9:

[1641] The terminal plays back the received audio data.

[1642] What it does: The device's built-in speaker plays the received audio data. The answer, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing," is delivered in a gentle tone to the anxious child.

[1643] In this way, by incorporating emotional data, the system can provide answers that are in tune with the child's feelings, resulting in more friendly and effective communication, which reduces the burden on parents and satisfies the child's intellectual curiosity.

[1644] Example 2

[1645] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1646] It takes a lot of time and effort for parents to quickly and appropriately answer the "why?" questions that children frequently ask. Furthermore, it is extremely difficult to provide answers that reflect the child's emotions. Therefore, there is a need to develop a system that can satisfy children's intellectual curiosity while reducing the burden on parents. Another challenge is to improve the accuracy of responses to similar questions.

[1647] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1648] In this invention, the server includes means for receiving questions posed by children as voice data, means for converting the received voice data into text data, and means for extracting the converted text data and emotion data. This makes it possible to generate and provide appropriate answers to questions posed by children quickly and taking emotion into consideration. Furthermore, by including means for successively evolving the voice data and emotion data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards, the quality of answers to similar questions can be continuously improved.

[1649] "Children" refers to young people who use the system and ask questions aloud.

[1650] A "question" refers to a verbal expression of a child's doubts.

[1651] "Audio data" refers to information that has been recorded as sound in response to questions uttered by children and converted into digital format.

[1652] "Text data" refers to digital data in which voice data is converted into text information using voice recognition technology.

[1653] "Server" refers to a computer system that receives and analyzes data over a network and generates appropriate answers using a generative AI model.

[1654] A "generative AI model" refers to an artificial intelligence model that generates appropriate answers from given input data based on a pre-trained algorithm.

[1655] "Emotional data" refers to data that is generated by analyzing emotions from the user's tone of voice, words, etc., and then expressing the results in digital form.

[1656] "Speech recognition software" refers to software that receives voice data as input and converts that voice into text data.

[1657] An "emotion engine" refers to a system that analyzes user emotions from voice data and feeds this back to a generative AI model as emotional data.

[1658] "Audio data transmission means" refers to a communication means for transmitting audio data generated by the server to the terminal.

[1659] A "machine learning algorithm" is an algorithm that allows the system to self-learn based on past data and improve the accuracy of responses from the next time onwards.

[1660] This invention is a system that aims to reduce the burden on parents and satisfy children's intellectual curiosity by providing quick and appropriate answers to the "why?" questions that children often ask on a daily basis. The system incorporates an emotion engine that recognizes the user's (child's) emotions and adjusts the response accordingly. This system mainly consists of the following components:

[1661] System Configuration

[1662] 1. Terminal (doll-like device):

[1663] The system receives the voice data uttered by the child and converts it into text data. Specifically, the device's built-in microphone captures the voice and converts it into text data using voice recognition software (e.g., Google Speech-to-Text API). Furthermore, it uses an emotion engine to extract emotional data from the voice and sends it to the server.

[1664] The audio data sent from the server is played back to provide the answer to the user (child). Specifically, the audio data received by the device's built-in speaker is played back (e.g., MP3 format data generated using the Google Text-to-Speech API).

[1665] 2. Server:

[1666] The server receives and analyzes the text data and emotion data sent from the device. Specifically, the server analyzes the received JSON data and extracts the text data and emotion data.

[1667] A generative AI model is used to generate an appropriate response, which is then converted into voice data. Specifically, the server uses a generative AI model (e.g., OpenAI GPT-4) to input a prompt and generate an appropriate text response. The response is then converted into voice data using the Google Text-to-Speech API and sent to the device.

[1668] The converted audio data is sent to the device. Specifically, the audio data (MP3 format) is sent to the device via Wi-Fi.

[1669] 3. Generative AI Model:

[1670] It analyzes text and emotion data to generate the most appropriate answer based on the child's age, level of understanding, and emotions. Example prompt: "The sky appears blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a completely natural process."

[1671] Specific examples

[1672] 1. Example 1: When a child asks, in an anxious voice, "Why is the sky blue?"

[1673] When a user (child) asks "Why is the sky blue?" in an anxious voice to the doll-shaped device, the device receives the voice and converts it into text data, "Why is the sky blue?" using speech recognition software. The device then uses an emotion engine to extract emotion data, such as anxiety, from the voice and sends the converted text data and emotion data to the server. The server then analyzes the received text data and emotion data, and uses a generative AI model to generate the answer, "The sky looks blue because sunlight reacts with molecules in the atmosphere, scattering blue light. Don't worry, this is a very natural thing." The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in a gentle tone.

[1674] 2. Example 2: When a child asks, in an inquisitive voice, "Why are leaves green?"

[1675] When a user (child) asks the doll-shaped device in an inquisitive voice, "Why are leaves green?", the device receives the voice and uses speech recognition software to convert it into text data: "Why are leaves green?". The device then uses an emotion engine to extract emotional interest data from the voice and sends the converted text data and emotional data to the server. The server analyzes the received text data and emotional interest data and uses a generative AI model to generate an answer: "Leaves are green because they contain a pigment called chlorophyll. Want to know more? There are lots of other interesting things about them!" The generated answer is then converted into voice data (using a TTS engine) and sent to the device. The device then plays back the received voice data and provides the answer to the user (child) in an inquisitive tone.

[1676] As described above, this invention aims to effectively utilize the emotion engine and generative AI model to function as a good partner in the home environment, reducing the burden on parents and satisfying children's intellectual curiosity.

[1677] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1678] Step 1:

[1679] The user (child) asks a question

[1680] The user (child) speaks a question into the terminal.

[1681] Specific action: A child asks the device, "Why is the sky blue?"

[1682] Input: Voice question

[1683] Output: Audio data

[1684] Step 2:

[1685] The device receives the voice data and converts it into text data.

[1686] The terminal receives the user's voice data and converts it into text data using voice recognition software.

[1687] Specific operation: The microphone on the device captures the voice and uses speech recognition software (e.g., Google Speech-to-Text API) to generate text data such as "Why is the sky blue?"

[1688] Input: Audio data

[1689] Output: Text data

[1690] Step 3:

[1691] The device uses an emotion engine to extract emotion data such as anxiety.

[1692] The terminal inputs the voice data into an emotion engine and extracts emotion data.

[1693] Specific operation: The device analyzes the emotion "anxiety" from the voice using an emotion engine (e.g., OpenAI's emotion analysis model).

[1694] Input: Audio data

[1695] Output: Emotion data (anxiety)

[1696] Step 4:

[1697] The device sends text data and emotion data to the server.

[1698] The device transmits the generated text data and emotion data to the server.

[1699] Specific operation: The device uses Wi-Fi to send the text data "Why is the sky blue?" and the emotion data "anxiety" in JSON format to the server.

[1700] Input: Text data, emotion data

[1701] Output: JSON data

[1702] Step 5:

[1703] The server receives and analyzes the text data and emotion data.

[1704] The server receives the JSON data sent from the device and analyzes the text data and emotion data.

[1705] Specific operation: The server analyzes the received JSON data and extracts the text data "Why is the sky blue?" and the emotion data "Anxiety."

[1706] Input: JSON data

[1707] Output: Text data, emotion data

[1708] Step 6:

[1709] The server uses the generative AI model to generate the appropriate answer

[1710] The server inputs text data and emotional data into the generative AI model and generates an appropriate answer.

[1711] What it does: The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer like, "The sky looks blue because sunlight interacts with molecules in the atmosphere, scattering blue light. Don't worry, this is perfectly natural."

[1712] Input: Text data, emotion data

[1713] Output: Answer text

[1714] Step 7:

[1715] The server converts the generated answer into audio data.

[1716] The server converts the generated text response into audio data.

[1717] Specific operation: The server uses the Google Text-to-Speech API to convert the generated text response into audio data (MP3 format).

[1718] Input: Answer text

[1719] Output: Audio data

[1720] Step 8:

[1721] The server sends the audio data to the device.

[1722] The server transmits the generated voice data to the terminal.

[1723] Specific operation: The server sends audio data to the device via Wi-Fi.

[1724] Input: Audio data

[1725] Output: Transmitted audio data

[1726] Step 9:

[1727] The device plays back the received voice data and provides the child with a response.

[1728] The terminal plays back the voice data received from the server and provides the answer to the user (child).

[1729] What it does: The speaker on the device plays a gentle voice saying, "The sky looks blue because sunlight reacts with molecules in the atmosphere and scatters blue light. Don't worry, this is a very natural thing."

[1730] Input: Transmitted audio data

[1731] Output: Answer provided through speech

[1732] (Application example 2)

[1733] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1734] In today's brick-and-mortar stores, it is difficult to consistently provide high-quality answers and suggestions to the diverse questions and requests customers ask. It is also necessary to properly recognize customer emotions and adjust responses accordingly, but current systems are not sufficient in this regard. Furthermore, while it is important to achieve friendly and effective communication in customer service, existing systems also have limitations in this regard. It is necessary to solve these problems and provide services that will satisfy customers.

[1735] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1736] In this invention, the server includes means for analyzing text data and generating appropriate answers using a generative AI model, means for analyzing emotion data and generating answers based on the emotions, and means for converting the generated answers into voice data. This makes it possible to improve the quality of customer service in physical stores and increase customer satisfaction by recognizing customer emotions and adjusting answers accordingly.

[1737] "Audio data" refers to data that has been recorded and stored in digital format.

[1738] "Text data" refers to data in which character information is recorded and stored in digital format.

[1739] A "server" is a computer or system that manages and processes data on a network.

[1740] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate appropriate answers and information from given data.

[1741] "Emotional data" is data extracted from a user's voice or text that indicates their emotions or emotional state.

[1742] A "terminal" is a device used by a user that receives and plays audio data.

[1743] An "emotion engine" is an algorithm or system that analyzes emotions from voice and text data and generates emotional data.

[1744] A "machine learning algorithm" is a method of learning patterns and rules from large amounts of data and making predictions and classifications for new data.

[1745] MODE FOR CARRYING OUT THE INVENTION

[1746] The system of the present invention is primarily designed to improve customer service in brick-and-mortar stores, and specific embodiments thereof are described below.

[1747] Overall system configuration

[1748] The overall system consists of the following main components:

[1749] 1. Terminal

[1750] 2. Server

[1751] 3. Generative AI Models

[1752] 4. Emotion Engine

[1753] 5. Machine Learning Algorithms

[1754] Detailed System Description

[1755] Terminal

[1756] The terminal is a customer service robot installed in a brick-and-mortar store that receives voice input from customers. The terminal converts the received voice data into text data using voice recognition software and sends this text data to a server. The terminal also receives the answer generated by the generative AI model as voice data and provides it to the customer as voice output.

[1757] server

[1758] The server receives and analyzes the text data and emotion data sent from the device. Using a generative AI model and emotion engine, the server generates an appropriate answer to the customer's question and converts it into voice data. This converted voice data is then sent back to the device.

[1759] Generative AI Models

[1760] A generative AI model uses machine learning algorithms to generate appropriate responses based on text data and emotional data sent from a device. Generative AI models learn from past interaction data to improve response accuracy.

[1761] Emotion Engine

[1762] The emotion engine is an algorithm that analyzes customer emotions from voice and text data and feeds them back to the generative AI model as emotion data. The emotion engine analyzes the tone and content of the customer's voice when they ask a question, extracting emotions such as anxiety, excitement, and interest.

[1763] Machine Learning Algorithms

[1764] A machine learning algorithm is an algorithm that learns patterns and rules from large amounts of data and makes predictions and classifications for new data. In the present invention, it is used to sequentially learn from interactions with customers and improve response accuracy from the next time onwards.

[1765] Specific use cases

[1766] A specific example of use is shown below.

[1767] Example 1: When a customer excitedly asks, "Do you have this item in stock?"

[1768] 1. User (customer): "Do you have this item in stock?" asks the robot terminal excitedly.

[1769] 2. The device receives the voice and converts it into text using speech recognition software.

[1770] 3. The device uses an emotion engine to extract the emotion data "excitement" from the voice.

[1771] 4. The device sends the converted text data and emotion data to the server.

[1772] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate the answer, "Yes, we have it in stock. We'll show you here."

[1773] 6. The server converts the generated answer into voice data and sends it to the device.

[1774] 7. The terminal plays back the received voice data and provides the answer to the customer.

[1775] Example prompt sentence:

[1776] When a customer excitedly asks, "Do you have this item in stock?", you can respond with, "Yes, we do have it in stock. Let me show you."

[1777] Example 2: When a customer anxiously asks, "How do I use this product?"

[1778] 1. User (customer): "How do I use this product?" he asks anxiously to the robot terminal.

[1779] 2. The device receives the voice and converts it into text using speech recognition software.

[1780] 3. The device uses an emotion engine to extract the emotion data "anxiety" from the voice.

[1781] 4. The device sends the converted text data and emotion data to the server.

[1782] 5. The server analyzes the received text data and emotion data and uses a generative AI model to generate an answer such as, "This is how to use this product. Please also refer to the instructions."

[1783] 6. The server converts the generated answer into voice data and sends it to the device.

[1784] 7. The terminal plays back the received voice data and provides the answer to the customer.

[1785] Example prompt sentence:

[1786] If a customer asks anxiously, "How do I use this product?", answer, "This is how you use this product. Please read the instructions."

[1787] In this way, by using the system of the present invention, the quality of customer service in physical stores can be improved and customer satisfaction can be increased.

[1788] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1789] Step 1:

[1790] The user (customer) performs voice input. The customer dictates their question or request. The input of this process is the customer's voice data, and the output is the voice data received by the terminal.

[1791] What happens: A customer asks, "Do you have this item in stock?"

[1792] Step 2:

[1793] The terminal receives the voice data and converts it into text data using voice recognition software.

[1794] The input of this process is audio data, and the output is text data. Specific operation: The device transcribes the audio using a speech recognition library (e.g., speech_recognition).

[1795] Step 3:

[1796] The terminal uses an emotion engine to analyze emotions from the voice data and extract emotion data.

[1797] The input of this process is voice data, and the output is emotion data. Specific operation: The device analyzes the voice data using an emotion analysis algorithm (e.g., EmotionRecognition) to identify the emotion.

[1798] Step 4:

[1799] The terminal transmits the converted text data and emotion data to the server.

[1800] The input of this process is text data and emotion data, and the output is the data sent to the server. Specific operation: The device sends the text data and emotion data to the server as an HTTP request.

[1801] Step 5:

[1802] The server analyzes the received text data and emotional data and generates an appropriate answer using a generative AI model.

[1803] The input of this process is text data and emotion data, and the output is the generated answer text. Specific operation: The server inputs the prompt sentence into the generative AI model (e.g., GPT-3) and generates an appropriate answer.

[1804] Step 6:

[1805] The server converts the generated response into audio data.

[1806] The input of this process is the answer text, and the output is audio data. Specific operation: The server converts the answer into audio data using a text-to-speech synthesis engine (e.g., pyttsx3).

[1807] Step 7:

[1808] The server transmits the generated voice data to the terminal.

[1809] The input of this process is audio data, and the output is the audio data sent to the terminal. Specific operation: The server sends the audio data to the terminal as an HTTP response.

[1810] Step 8:

[1811] The terminal plays back the received voice data and provides the answer to the customer.

[1812] The input of this process is voice data, and the output is voice information provided to the customer. Specific operation: The terminal plays the voice data using the speaker and responds, "Yes, we have it in stock. I'll show you here."

[1813] 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.

[1814] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1815] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1816] 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.

[1817] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1818] 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.

[1819] 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).

[1820] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1821] 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."

[1822] 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.

[1823] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1824] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1825] 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.

[1826] 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.

[1827] 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.

[1828] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1829] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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.

[1834] The following is further disclosed regarding the above embodiment.

[1835] (Claim 1)

[1836] means for receiving questions uttered by a child as voice data;

[1837] means for converting received voice data into text data;

[1838] means for transmitting the converted text data to a server;

[1839] A means for the server to analyze the text data and generate an appropriate answer using a generative AI model;

[1840] means for converting the generated answers into audio data;

[1841] means for transmitting the generated voice data to a terminal;

[1842] a means for the device to play back the audio data and provide the child with a response;

[1843] A system including:

[1844] (Claim 2)

[1845] The system of claim 1, including a generative AI model that generates optimal answers based on the child's age and level of understanding when the server analyzes the text data.

[1846] (Claim 3)

[1847] The system according to claim 1, further comprising means for successively evolving the voice data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards.

[1848] "Example 1"

[1849] (Claim 1)

[1850] means for receiving questions uttered by a child as voice data;

[1851] means for converting received voice data into text data;

[1852] means for transmitting the converted text data to a server;

[1853] A means for the server to analyze the text data and generate an appropriate answer using a generative AI model;

[1854] means for converting the generated answers into audio data;

[1855] means for transmitting the generated voice data to a terminal;

[1856] a means for the device to play back the audio data and provide the child with a response;

[1857] A means for utilizing a highly sensitive microphone and speech recognition software when the terminal performs speech recognition and converts the speech data into text data;

[1858] A means for the server to input a prompt sentence into the generative AI model and generate an answer;

[1859] A system including:

[1860] (Claim 2)

[1861] The system of claim 1, including a generative AI model that generates optimal answers based on the child's age and level of understanding when the server analyzes the text data.

[1862] (Claim 3)

[1863] The system according to claim 1, further comprising means for successively evolving the voice data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards.

[1864] "Application Example 1"

[1865] (Claim 1)

[1866] means for receiving questions uttered by a child as voice data;

[1867] means for converting received voice data into text data;

[1868] means for transmitting the converted text data to a server;

[1869] A means for the server to analyze the text data and generate an appropriate answer using a generative AI model;

[1870] means for converting the generated answers into audio data;

[1871] means for transmitting the generated voice data to a terminal;

[1872] a means for the device to play back the audio data and provide the child with a response;

[1873] A means for generating appropriate answers to questions of visitors in a physical store and providing audio guidance;

[1874] A system including:

[1875] (Claim 2)

[1876] The system of claim 1, including a generative AI model that generates optimal answers based on the visitor's age and level of understanding when the server analyzes the text data.

[1877] (Claim 3)

[1878] The system according to claim 1, further comprising means for successively evolving the voice data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards.

[1879] "Example 2: Combining Emotion Engines"

[1880] (Claim 1)

[1881] means for receiving questions uttered by a child as voice data;

[1882] means for converting received voice data into text data;

[1883] means for extracting the converted text data and emotion data;

[1884] means for transmitting text data and emotion data to a server;

[1885] a means for the server to analyze the text data and sentiment data and generate an appropriate response using a generative AI model;

[1886] means for converting the generated answers into audio data;

[1887] means for transmitting the converted voice data to a terminal;

[1888] a means for the device to play back the audio data and provide the child with a response;

[1889] A system including:

[1890] (Claim 2)

[1891] The system of claim 1, wherein the server includes a generative AI model that analyzes the text data and emotional data and generates optimal answers based on the child's age, level of understanding, and emotions.

[1892] (Claim 3)

[1893] The system according to claim 1, further comprising means for successively evolving the voice data and emotion data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards.

[1894] "Application example 2 when combining emotion engines"

[1895] (Claim 1)

[1896] means for receiving questions uttered by a child as voice data;

[1897] means for converting received voice data into text data;

[1898] means for transmitting the converted text data to a server;

[1899] A means for the server to analyze the text data and generate an appropriate answer using a generative AI model;

[1900] means for converting the generated answers into audio data;

[1901] means for transmitting the generated voice data to a terminal;

[1902] a means for the device to play back the audio data and provide the child with a response;

[1903] A means for extracting emotion data from the voice data received by the terminal and transmitting the emotion data to a server;

[1904] A means for the server to generate an answer based on the emotion data using a generative AI model;

[1905] A system including:

[1906] (Claim 2)

[1907] The system of claim 1, including a generative AI model that generates optimal answers based on the child's age and level of understanding when the server analyzes the text data.

[1908] (Claim 3)

[1909] The system according to claim 1, further comprising means for successively evolving the voice data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards. [Explanation of symbols]

[1910] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving questions uttered by a child as voice data; means for converting received voice data into text data; means for transmitting the converted text data to a server; A means for the server to analyze the text data and generate an appropriate answer using a generative AI model; means for converting the generated answers into audio data; means for transmitting the generated voice data to a terminal; a means for the device to play back the audio data and provide the child with a response; A system including:

2. The system of claim 1, including a generative AI model that generates optimal answers according to the child's age and level of understanding when the server analyzes the text data.

3. The system according to claim 1, further comprising means for successively evolving the voice data received by the terminal using a machine learning algorithm to improve response accuracy from the next time onwards.

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