system

Abstract

We provide the system. [Solution] A terminal device capable of recognizing voice in real time and converting it into digital data, A server means that generates appropriate conversations using voice data analyzed based on the user's age and interests, A transmission means for transmitting the generated conversation and related video content to the user's mobile device or television, A learning method that collects user feedback and incorporates it into future conversation generation, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern shared households, there is a problem that it is difficult for parents to sufficiently provide an appropriate educational environment and opportunities for knowledge provision for their children. Also, it is difficult for busy parents to immediately provide information tailored to their children's interests and developmental stages. As a result, there is a situation where communication between parents and children is insufficient and children's curiosity cannot be sufficiently nurtured.

Means for Solving the Problems

[0005] This invention provides a system that can instantly analyze user speech by using terminal means that recognize voice in real time and convert it into digital data. Furthermore, it incorporates server means that generate conversations that take into account the user's age and interests, promoting natural communication between parents and children. In addition, a transmission means that sends the generated conversations and related images to a mobile device or television allows for the provision of visual information without interruption. Moreover, by incorporating a learning means that utilizes user feedback to improve the accuracy of conversations in the future, the aim is to continuously stimulate children's intellectual curiosity and enhance educational effectiveness.

[0006] "Sound" refers to the voices that humans produce, that is, sounds transmitted through air vibrations.

[0007] "Real-time" refers to processes that occur simultaneously or are processed as quickly as possible.

[0008] "Recognition" refers to the process of grasping and understanding information or stimuli, and in the case of computers, it specifically refers to the process of analyzing sounds and images to extract meaning.

[0009] "Digital data" refers to information represented by discrete numerical values, and is the result of converting analog information such as audio and images into a digital format.

[0010] "Terminal" refers to information devices that users directly operate, and includes devices such as smart speakers and mobile phones.

[0011] "User" refers to the entity that uses the system, and in this context, it mainly refers to parents and children.

[0012] "Age" is an indicator that shows the number of years a person has lived over time, and it reflects the degree of growth and maturity.

[0013] "Interest" refers to the feeling or concern one feels towards a subject, and is a particularly motivating factor in learning and information acquisition.

[0014] "Analysis" refers to the process of decomposing data and understanding its structure and content in detail.

[0015] "Server" is a computer system that provides services on a network and is responsible for data processing and file management.

[0016] "Generation" refers to the act of newly creating, and here it means the process of creating answers or conversations from data and information.

[0017] "Content" refers to information and materials provided through media, including videos, audio, articles, etc.

[0018] "Transmission" refers to the act of transferring information or signals to other devices or systems.

[0019] "Mobile terminal" is a small computer device that can be carried around and includes smartphones and tablets.

[0020] "TV" refers to a device that receives and displays video and audio and is used for viewing video content.

[0021] "Feedback" is the response or opinion obtained from users and is information that serves as a guide for improving the performance and development of the system.

[0022] "Learning" refers to the acquisition of new knowledge and abilities, and in a system, it means the process of improving a model from data.

Brief Explanation of Drawings

[0023] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0024] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0025] First, the language used in the following description will be explained.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 1, the 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.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0037] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] This invention provides an interface system that enables natural and fluent communication between a user and a computer system. It supports communication between children and parents using voice and provides appropriate information tailored to the child's interests. Specific embodiments of this system are described below.

[0045] First, the user speaks to the smart speaker. For example, consider a child asking, "What kind of bird is this?" The voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the processed audio data to a server over the network.

[0046] Next, the server analyzes the received audio data. Using natural language processing techniques, it converts the audio into text and generates the most appropriate response, taking into account the context, emotions, and the user's age and interests. For example, it might generate a response such as, "That bird is a sparrow. Shall we see what a sparrow's call sounds like?"

[0047] The generated text and associated video content are selected from the server and sent to the appropriate display device. This could be a television in the user's home or a parent's mobile device. The device receives this information and provides the child with a more concrete experience by displaying images and sounds of bird songs on the television.

[0048] The user can ask further questions based on the information newly acquired through this process, and the device continues to send this new audio back to the server. The server learns from this feedback data and uses it to improve future interactions. This allows the system to provide more personalized answers over time.

[0049] Thus, this invention creates an environment that naturally responds to children's immediate interests and provides educational information, even while parents are doing household chores. Users can interact through a smart speaker without interrupting their work, thereby deepening parent-child communication and effectively expanding children's curiosity and knowledge.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The device records the user's voice in real time and performs pre-processing such as noise reduction and volume adjustment. This prepares the audio for conversion into digital data.

[0053] Step 2:

[0054] The device sends pre-processed audio data to the server. This data contains the content of the user's speech.

[0055] Step 3:

[0056] The server analyzes the received audio data using natural language processing techniques and converts it into text data. Context, emotions, user age, and interests are also analyzed.

[0057] Step 4:

[0058] The server generates an appropriate response based on the analyzed data. In doing so, it also refers to past conversation history and selects information based on the user's interests.

[0059] Step 5:

[0060] The server sends the generated conversation text and instructions to the user's device to transmit selected related video content to their TV or mobile device.

[0061] Step 6:

[0062] The terminal uses information received from the server to display video and audio on a television or mobile device. This provides users with information visually.

[0063] Step 7:

[0064] When the user asks a new question or gives a response, the device records it again as audio and sends it to the server. This process enables a continuous and natural conversation.

[0065] Step 8:

[0066] The server updates its database based on user feedback and learns for future response generation. This makes subsequent conversations more personalized and effective.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] In modern families, parents are often too busy to communicate effectively with their children. Therefore, there is a need for methods to provide educational dialogue that satisfies children's curiosity even when parents are unable to actively engage with them. While voice-based interfaces are common, they struggle to provide personalized responses based on context and emotions. Consequently, a new system is needed that automatically generates natural and effective dialogue tailored to the user's needs and interests, and combines this with visual information.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes a device capable of recognizing speech in real time and pre-processing it using signal processing technology; a communication means for converting the pre-processed speech into digital data and transmitting it over a network; an information processing means for analyzing the received speech data using a generation AI model and generating appropriate information based on context and emotion; a transmission means for selecting visual content related to the generated information and transmitting it to a display device; and a learning means for collecting user feedback and reflecting it in future information generation. This makes it possible for parents to deepen dialogue with their children without having to do anything themselves and to effectively provide educational information based on the child's interests.

[0072] A "device for real-time voice recognition" refers to a device that has the function of instantly acquiring voice input and converting it into digital data that can be processed instantaneously.

[0073] "Preprocessing using signal processing technology" refers to a series of processing techniques performed to remove noise from audio data and improve the quality of the audio signal.

[0074] "Communication methods for converting to digital data" refers to the technology and procedures for converting audio signals into a digital format and transmitting them to a server over a network.

[0075] "Information processing means that analyze data using a generative AI model" refers to a method that uses AI technology to analyze received digital data, understand the user's intentions and emotions, and generate an appropriate response.

[0076] "Transmission means for selecting relevant visual content" refers to the technology and functions for selecting appropriate video or image data based on generated information and transmitting it to the user's display device.

[0077] A "learning method for collecting user feedback" refers to a learning algorithm that collects responses and actions from users as data and uses that data to improve future responses.

[0078] This invention relates to an interface system that provides natural and effective dialogue, utilizing speech recognition, natural language processing, and generative AI models. The system primarily consists of terminals and servers, each functioning with its own distinct role.

[0079] The terminal acquires voice from the user using a voice input device that recognizes speech in real time. This device instantly converts the user's speech into digital data and performs noise reduction and signal processing. Specifically, smart speakers or similar voice assistant devices are used. The pre-processed voice signal is then sent to a server as digital data.

[0080] The server processes the received digital data using a generation AI model. Using natural language processing techniques, it analyzes the user's intent and emotions, and generates appropriate responses based on the context. This response generation process is based on a pre-trained AI model. The server then selects visual content related to the generated text from a database. The relevant visual information is sent to the user's display device, where it is displayed. Specifically, this can be done on a home television or smartphone.

[0081] Users can ask further questions based on the information presented, and the system can respond to these questions sequentially. The system continuously learns using the data collected through feedback, which makes subsequent interactions more personalized and valuable to the user.

[0082] As an example, consider a scenario where a child asks a smart speaker, "What kind of bird is this?" After voice recognition, the server responds with something like, "That's a sparrow. This is what a sparrow sounds like," and displays an image of a sparrow on the home television. In this way, the user can satisfy the child's curiosity without any effort on their part.

[0083] An example of a prompt for the generative AI model would be: "Analyze the audio 'Tell me about...' and generate a response that provides relevant information and visual content. Aim for a balanced educational response, incorporating visual elements that will capture a child's interest."

[0084] This system allows users to enhance the educational environment within the home and achieve effective parent-child communication even in busy situations.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user speaks to a voice input device such as a smart speaker, inputting questions as voice. For example, the user might ask, "What kind of bird is this?" The device acquires this voice data using its microphone and stores it internally as digital data.

[0088] Step 2:

[0089] The terminal processes the acquired audio data to remove noise. In this step, an audio signal processing algorithm is used to reduce noise from the audio and improve recognition accuracy. The noise-removed audio data becomes the output for the next step.

[0090] Step 3:

[0091] The terminal sends pre-processed audio data to the server over the network. The input here is the audio data that has been de-noised in the previous step, and the output is a data packet to be sent to the server.

[0092] Step 4:

[0093] The server converts received digital audio data into text using natural language processing technology. Specifically, it performs speech recognition using a generative AI model and extracts text information from the audio. The input is audio data, and the output is text data.

[0094] Step 5:

[0095] The server analyzes the converted text using a generative AI model and generates an appropriate response based on the user's intent and context. For example, in response to a user's question, it might generate a text response such as, "That bird is a sparrow. Let's listen to the sound of a sparrow chirping." The input is text data, and the output is the generated response.

[0096] Step 6:

[0097] The server selects visual content related to the generated response from the database and prepares the data to be sent to the user's display device. Specifically, this involves selecting an image of a sparrow. The inputs are the generated response data and data about the user's visual environment, and the output is the data prepared for transmission.

[0098] Step 7:

[0099] The device analyzes data received from the server and displays appropriate visual information on the television or smartphone. Specifically, an image of a sparrow is displayed on the television. The input is data transmitted from the server, and the output is visual information from the user's perspective.

[0100] Step 8:

[0101] Based on the information presented, the user can ask additional questions via voice. This new voice input is then taken into the device, and the process restarts. The user's feedback is used for subsequent learning, improving the accuracy of the generative AI model. The input is the user's new question, and the output is the feedback data.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] In today's world, there is a need for information delivery systems that effectively support children's curiosity and educational needs. However, conventional systems suffer from insufficient accuracy in voice recognition, inadequate content selection, and inadequate utilization of user feedback, hindering natural communication between parents and children. Furthermore, they are unable to adapt to children's dynamically changing interests, making it difficult to provide consistent educational support.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes terminal means capable of recognizing voice in real time and converting it into digital data; information processing means that generate appropriate dialogue using voice data analyzed based on the user's characteristics and interests; communication means that transmit the generated dialogue and related visual content to the user's information communication terminal or video device; and media selection means that select and display relevant information based on behavioral predictions. This makes it possible to effectively respond to children's dynamic interests, promote natural communication between parents and children, and appropriately provide educational information.

[0107] A "terminal device" is a device that has the function of recognizing voice in real time and converting it into digital data.

[0108] "Information processing means" refers to a processor that analyzes voice data based on the user's characteristics and interests and generates appropriate dialogue.

[0109] "Communication means" refers to a system that has the function of transmitting generated dialogue and related visual content to an information and communication terminal or video device.

[0110] An "intelligent learning tool" is an algorithm with a self-learning function that collects user feedback data and incorporates it into the generation of subsequent dialogues.

[0111] "Media selection means" refers to technology that has the ability to select and display relevant information based on behavioral predictions.

[0112] This invention is implemented as a system that provides interactive educational content tailored to the characteristics of the user. The system includes the following main elements:

[0113] First, a "terminal device" is used for the user to perform voice input. Specifically, portable information terminals such as smartphones and tablets are used. These devices recognize speech in real time and convert the speech into digital data using the Google® Speech-to-Text API or similar.

[0114] Subsequently, a server, acting as an "information processing device," receives the converted audio data. This server analyzes the audio data using a large-scale language model. In particular, it utilizes natural language processing technology to generate appropriate dialogue and related media content based on the user's interests and characteristics. OpenAI's (registered trademark) natural language processing model is among those employed.

[0115] The generated content is streamed to the user's mobile device or video device, such as a television, via the cloud infrastructure, which serves as the "means of communication." In particular, AWS (Amazon Web Services) is used to ensure high-speed and stable content delivery.

[0116] As an "intelligent learning tool," user feedback data is stored on the server and reflected in the generation of subsequent dialogues. Using this feedback data, the AI ​​model continuously learns, providing a more personalized educational experience. In this way, the system evolves along with the user's growth.

[0117] For example, if a user asks "Tell me about dinosaurs," the server will automatically generate video content explaining the history and ecology of dinosaurs based on this request and send it to the device. Using the "Generating AI Model, Prompt Text," a prompt like the following is formed.

[0118] Example of a prompt:

[0119] "As educational content for children, please display visual information about 'dinosaurs' based on the voice recognition results."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The user asks a question by voice into a device such as a smartphone or tablet. This voice input is captured by the device. The device then performs noise reduction and converts the voice data into text using the Google Speech-to-Text API. Through this process, the device outputs the voice input as digital text data.

[0123] Step 2:

[0124] The terminal sends the converted text data to the server. The server receives this data and analyzes its content using a natural language processing engine. Specifically, it utilizes OpenAI's language model to understand the context of the text data and generate prompts based on the user's age and interests. In this step, the server outputs the prompt text and related topic information.

[0125] Step 3:

[0126] The server searches for and identifies relevant educational content based on prompts analyzed by a generative AI model. This involves using a database on AWS to select the appropriate visual and audio content. The output of this operation is media content that matches the user's interests.

[0127] Step 4:

[0128] The server transmits the selected content to the user's terminal via a communication method. Here, an encoding process takes place, and the content is delivered in streaming format. The user's terminal receives this content and displays it in real time. This allows the user to visually experience interactive information.

[0129] Step 5:

[0130] Finally, user feedback is collected on the device and sent to the server. The server uses the collected feedback to apply intelligent learning algorithms and optimize the system's response for future use. This process allows the system to continuously improve and provide more personalized educational services.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention provides a system that offers more appropriate and personalized responses in natural communication using the user's voice by utilizing emotion recognition technology. This system is designed to improve the quality of interaction between children and parents.

[0133] In this system, the user first speaks to the smart speaker. For example, suppose a child says, "Today was fun." This voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the audio data to a server via the network, where voice recognition and sentiment analysis are performed simultaneously.

[0134] The server uses an emotion recognition engine to analyze speech and identify the user's emotions. Based on this emotion data, the server generates appropriate conversation content and related video content. For example, if a child is enjoying themselves, it can suggest fun animations or quizzes.

[0135] The generated conversations and content are sent from the server to the user's television or mobile device. The device receives this information and provides a more impactful experience for children by displaying visual images and audio.

[0136] Furthermore, user feedback and new questions are collected again by the device and sent to the server. The server uses a learning algorithm based on this data to reflect in future conversation generation and content suggestions. Sentimental information, in particular, is an important indicator in this learning process, giving the system the flexibility to adapt to the user's situation.

[0137] In addition, this system also manages the user's schedule, streamlining daily life management by providing appropriate reminders and schedule adjustments based on the user's emotional state.

[0138] As described above, the present invention utilizes emotion recognition to provide users with a more personalized experience, thereby improving parent-child communication and educational outcomes.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The device records the user's speech in real time and performs noise reduction and preprocessing of the audio signal. This prepares the audio data for analysis.

[0142] Step 2:

[0143] The terminal sends pre-processed audio data to the server. The server prepares the received audio data for analysis.

[0144] Step 3:

[0145] The server uses a speech recognition engine to convert speech data into text. Furthermore, it uses an emotion recognition engine to analyze the user's emotions and obtain emotion data.

[0146] Step 4:

[0147] The server generates appropriate conversation content based on analyzed text and sentiment data. It selects responses based on the user's age, interests, and emotions.

[0148] Step 5:

[0149] The server retrieves relevant video content from a database and selects content that matches the user's emotions. The selected information is then transmitted to the terminal and the user's display device.

[0150] Step 6:

[0151] The terminal prepares and displays the conversation and video information received from the server on a television or mobile device. This provides information to the user through both sight and sound.

[0152] Step 7:

[0153] The device records any new comments or questions submitted by the user and sends them to the next server.

[0154] Step 8:

[0155] The server collects user feedback and sentiment data, and uses machine learning algorithms to improve the model for future conversations and content suggestions.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] In modern society, where opportunities for communication are decreasing, there is a growing need to promote effective dialogue, particularly between parents and children, and to provide flexible responses tailored to each user's emotions. Furthermore, accurately understanding users' emotional states and providing lifestyle suggestions and information based on those understandings is desired to improve the efficiency and quality of their lives. However, current technology is insufficient for recognizing emotions and generating appropriate responses based on them, necessitating more advanced systems.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes processing means for acquiring audio in real time, performing noise reduction and preprocessing, and then converting it into a digital signal; generation means for analyzing the user's emotional state and dynamically generating corresponding dialogue content and visual information; and response means for enhancing the user's emotion-based response using a generation AI model. This makes it possible to accurately recognize the user's emotions and provide personalized, interactive responses in real time based on those emotions.

[0161] A "processing device" is a device that acquires the user's voice and converts it into a digital signal while removing noise.

[0162] A "generation device" is a device that analyzes the user's emotional state and dynamically generates appropriate dialogue content and visual information based on that information.

[0163] A "response mechanism" is a device that uses a generative AI model to provide responses tailored to the user's emotions and plays a role in enhancing those responses.

[0164] A "visual display means" is a device that has the function of transmitting generated dialogue and visual information to the user's display device and playing it back.

[0165] An "information learning device" is a device that collects responses and feedback from users and incorporates that information to improve the performance of the system.

[0166] A "schedule management device" is a device that adjusts information such as schedules according to the user's emotions, thereby streamlining daily management.

[0167] An "information presentation device" is a device that has the function of selecting and displaying relevant visual information based on the user's request.

[0168] This invention is a system that utilizes emotion recognition technology to provide users with personalized conversational experiences. The system primarily aims to improve user interaction and functions by combining real-time processing, emotion analysis, and response generation.

[0169] First, the user provides voice input via a smart speaker. This voice is acquired by the device and subjected to noise reduction and pre-processing. The device relies on hardware for this processing, for example, using a high-performance DSP (Digital Signal Processing) chip to perform it efficiently. The processed voice data is then sent to a server.

[0170] The server uses speech recognition software to convert speech data into text and an emotion recognition engine to analyze the user's emotions. The technologies used include, for example, machine learning algorithms and neural networks. Based on the analysis results, the server utilizes a generative AI model to dynamically generate appropriate responses and visual information.

[0171] The generated content is sent from the server to the user's display device. For example, if the emotion is determined to be "enjoyment," the device will display a fun animation on the user's TV or mobile device. This allows the user to enjoy an interactive experience with the system.

[0172] As a further interaction, user feedback is collected on the device and sent to the server. The server then applies a learning algorithm based on this information and incorporates it to improve the system's response. Through this process, the system can continuously learn and evolve.

[0173] Furthermore, to support users' daily lives, the server provides a schedule management function that can adjust appointments and generate reminders based on their emotions. For example, if it determines that a user is feeling stressed, it can suggest activities to help them relax.

[0174] A concrete example of a prompt could be something like, "Animation ideas to suggest when the user is enjoying themselves." This allows the system to generate responses and content that are best suited to the user's situation and emotions.

[0175] The above describes specific embodiments for carrying out the present invention.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] Users speak to the smart speaker, providing their emotions and thoughts to the system as voice input. The input voice is captured by the device as raw sound data.

[0179] Step 2:

[0180] The terminal performs noise reduction and audio signal preprocessing on the acquired raw sound data. Specifically, it uses digital signal processing technology to perform noise filtering and generate clear audio data. This results in the output of high-quality audio data to improve speech recognition accuracy.

[0181] Step 3:

[0182] The terminal sends pre-processed audio data to the server via a secure network. The server receives the audio data and prepares for the next processing step.

[0183] Step 4:

[0184] The server uses speech recognition software to convert speech data into text data. During this process, the speech signal is broken down into phonemes through a feature extraction process and output as text. In parallel, an emotion recognition engine is used to analyze the user's emotional state from the text data. The output of this analysis is emotion data.

[0185] Step 5:

[0186] The server utilizes a generative AI model, using emotion data and text data from the user as prompts to dynamically generate appropriate dialogue and visual content. For example, if the user is in a "fun" state, an AI model for generating fun animations is activated and the corresponding content data is output.

[0187] Step 6:

[0188] The server sends the generated conversation content and other materials to the user's device. The destination is the user's television or mobile device, and the server also performs format conversion according to the characteristics of the connected device.

[0189] Step 7:

[0190] The device displays or plays received content for the user. This includes functions such as displaying video on the screen and playing audio through the speaker. Through this, the user can experience rich interaction with the system.

[0191] Step 8:

[0192] Users can provide additional feedback and questions about the content they are given. This input becomes the data needed to create new conversational flows.

[0193] Step 9:

[0194] The device collects user feedback and sends it to the server. The server analyzes this data using a learning algorithm to improve the accuracy of responses in future dialogue generation processes. User sentiment information and feedback drive the system's evolution through repeated learning.

[0195] Step 10:

[0196] The server utilizes emotional information to manage the user's schedule and provide lifestyle suggestions optimized for their emotional state. For example, when relaxation is needed, it suggests content with relaxing effects. This makes it possible to improve the quality of the user's daily life.

[0197] (Application Example 2)

[0198] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0199] In today's world, enriching parent-child communication and educational interaction requires individualized support tailored to each child's emotions. However, existing technologies lack the means to recognize a child's emotions in real time and provide appropriate content and dialogue accordingly. As a result, support that fits children's interests and emotions is not provided, leading to insufficient learning effectiveness and emotional satisfaction.

[0200] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0201] In this invention, the server includes an information terminal means capable of recognizing voice in real time and converting it into digital information, an information processing means that generates appropriate dialogue using voice information analyzed based on the user's emotional state, and a communication means that transmits the generated dialogue and related visual content to the user's mobile terminal or video display device. This makes it possible to provide appropriate dialogue and content that responds to a child's emotions.

[0202] An "information terminal device" is a device that recognizes voice in real time and converts it into digital information.

[0203] "Information processing means" refers to a device or system that generates appropriate dialogue using voice information analyzed based on the user's emotional state.

[0204] "Communication means" refers to a device or system for transmitting the generated dialogue and associated visual content to a user's mobile terminal or video display device.

[0205] A "learning processing means" is a device or system for collecting user emotional information and reflecting it in the generation of subsequent dialogues.

[0206] "Information presentation means" refers to a device or system for acquiring and presenting content appropriately according to the user's emotions.

[0207] "Media presentation means" refers to a device or system for selecting and presenting relevant content based on the user's emotion recognition results.

[0208] The system implementing this invention is built around an "information terminal means" that recognizes speech in real time and converts it into digital information. Specifically, a mobile information terminal such as a smartphone or tablet is used. When the terminal acquires speech data, it performs real-time noise reduction preprocessing and then transmits the speech data to a server via the network.

[0209] The server uses "information processing means" to analyze the user's emotional state from the audio data. For emotion recognition, advanced AI models are used, such as Google Cloud's Natural Language API. This API is used to analyze emotions and generate appropriate dialogue. Furthermore, based on the results, relevant visual content is appropriately selected via "communication means" and transmitted to the user's mobile device or video display device.

[0210] During the learning process, the server uses a "learning processing method" to continuously collect feedback from users and improve the system's accuracy by reflecting this feedback in subsequent interactions and content generation.

[0211] For example, if a child user speaks to the information terminal and the system recognizes that they are having fun, the server activates an "emotion-responsive content curator" and suggests fun animations or quizzes. This allows parents to confidently provide their children with appropriate, educational, and emotionally resonant support.

[0212] Furthermore, in order to provide diverse content based on the user's emotions through the "information presentation means," a generative AI model is used to utilize prompt phrases such as, "Please tell me how to recognize emotions from a child's voice and suggest the most suitable learning content based on that," thereby enabling the provision of optimal content.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The device records the child's voice in real time. The input is raw audio data, which is pre-processed to remove noise. The output is clear audio data with the noise removed.

[0216] Step 2:

[0217] The terminal sends pre-processed audio data to the server via the network. The input is clear audio data, which is sent directly to the server as output. The specific operation for network communication utilizes an internet connection.

[0218] Step 3:

[0219] The server analyzes the transmitted audio data and uses an emotion recognition engine to identify the user's emotional state. The input is audio data, and the output is the analyzed emotion information. Here, emotion analysis is performed using Google Cloud's Natural Language API, among others.

[0220] Step 4:

[0221] The server generates appropriate dialogue and visual content based on emotional information. The input is emotional information, and an AI model is used to select relevant content. The output includes the generated dialogue text and content identifiers.

[0222] Step 5:

[0223] The server sends content to the terminal. The input is the generated dialogue text and content identifier, which are then transmitted to the terminal as output. Specifically, the data is packetized and transmitted using a communication protocol.

[0224] Step 6:

[0225] The terminal displays or plays received dialogue text and content to the user. Input is data from the server, and output is visualized dialogue and visual content. Specific actions include displaying this content on the application screen.

[0226] Step 7:

[0227] The user reacts to the system's presentation, and the device collects these reactions as data. Input consists of user actions and feedback, while output is feedback data used to generate future dialogues. Taps on the UI and voice input are recorded for this data collection.

[0228] Step 8:

[0229] The server stores the collected feedback data and uses it as learning material for future interactions and content generation. The input is feedback data, and the output is an updated learning model or stored data. To apply a generative AI model to this learning process, prompts such as "Please tell me how to recognize emotions from a child's voice and suggest the most appropriate learning content based on that" are used.

[0230] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0231] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0232] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0236] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0237] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0238] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0239] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0240] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0241] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0242] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0243] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0244] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0245] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0246] This invention provides an interface system that enables natural and fluent communication between a user and a computer system. It supports communication between children and parents using voice and provides appropriate information tailored to the child's interests. Specific embodiments of this system are described below.

[0247] First, the user speaks to the smart speaker. For example, consider a child asking, "What kind of bird is this?" The voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the processed audio data to a server over the network.

[0248] Next, the server analyzes the received audio data. Using natural language processing techniques, it converts the audio into text and generates the most appropriate response, taking into account the context, emotions, and the user's age and interests. For example, it might generate a response such as, "That bird is a sparrow. Shall we see what a sparrow's call sounds like?"

[0249] The generated text and associated video content are selected from the server and sent to the appropriate display device. This could be a television in the user's home or a parent's mobile device. The device receives this information and provides the child with a more concrete experience by displaying images and sounds of bird songs on the television.

[0250] The user can ask further questions based on the information newly acquired through this process, and the device continues to send this new audio back to the server. The server learns from this feedback data and uses it to improve future interactions. This allows the system to provide more personalized answers over time.

[0251] Thus, this invention creates an environment that naturally responds to children's immediate interests and provides educational information, even while parents are doing household chores. Users can interact through a smart speaker without interrupting their work, thereby deepening parent-child communication and effectively expanding children's curiosity and knowledge.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The device records the user's voice in real time and performs pre-processing such as noise reduction and volume adjustment. This prepares the audio for conversion into digital data.

[0255] Step 2:

[0256] The device sends pre-processed audio data to the server. This data contains the content of the user's speech.

[0257] Step 3:

[0258] The server analyzes the received audio data using natural language processing techniques and converts it into text data. Context, emotions, user age, and interests are also analyzed.

[0259] Step 4:

[0260] The server generates an appropriate response based on the analyzed data. In doing so, it also refers to past conversation history and selects information based on the user's interests.

[0261] Step 5:

[0262] The server sends the generated conversation text and instructions to the user's device to transmit selected related video content to their TV or mobile device.

[0263] Step 6:

[0264] The terminal uses information received from the server to display video and audio on a television or mobile device. This provides users with information visually.

[0265] Step 7:

[0266] When the user asks a new question or gives a response, the device records it again as audio and sends it to the server. This process enables a continuous and natural conversation.

[0267] Step 8:

[0268] The server updates its database based on user feedback and learns for future response generation. This makes subsequent conversations more personalized and effective.

[0269] (Example 1)

[0270] Next, we will describe Example 1. 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."

[0271] In modern families, parents are often too busy to communicate effectively with their children. Therefore, there is a need for methods to provide educational dialogue that satisfies children's curiosity even when parents are unable to actively engage with them. While voice-based interfaces are common, they struggle to provide personalized responses based on context and emotions. Consequently, a new system is needed that automatically generates natural and effective dialogue tailored to the user's needs and interests, and combines this with visual information.

[0272] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0273] In this invention, the server includes a device capable of recognizing speech in real time and pre-processing it using signal processing technology; a communication means for converting the pre-processed speech into digital data and transmitting it over a network; an information processing means for analyzing the received speech data using a generation AI model and generating appropriate information based on context and emotion; a transmission means for selecting visual content related to the generated information and transmitting it to a display device; and a learning means for collecting user feedback and reflecting it in future information generation. This makes it possible for parents to deepen dialogue with their children without having to do anything themselves and to effectively provide educational information based on the child's interests.

[0274] A "device for real-time voice recognition" refers to a device that has the function of instantly acquiring voice input and converting it into digital data that can be processed instantaneously.

[0275] "Preprocessing using signal processing technology" refers to a series of processing techniques performed to remove noise from audio data and improve the quality of the audio signal.

[0276] "Communication methods for converting to digital data" refers to the technology and procedures for converting audio signals into a digital format and transmitting them to a server over a network.

[0277] "Information processing means that analyze data using a generative AI model" refers to a method that uses AI technology to analyze received digital data, understand the user's intentions and emotions, and generate an appropriate response.

[0278] "Transmission means for selecting relevant visual content" refers to the technology and functions for selecting appropriate video or image data based on generated information and transmitting it to the user's display device.

[0279] A "learning method for collecting user feedback" refers to a learning algorithm that collects responses and actions from users as data and uses that data to improve future responses.

[0280] This invention relates to an interface system that provides natural and effective dialogue, utilizing speech recognition, natural language processing, and generative AI models. The system primarily consists of terminals and servers, each functioning with its own distinct role.

[0281] The terminal acquires voice from the user using a voice input device that recognizes speech in real time. This device instantly converts the user's speech into digital data and performs noise reduction and signal processing. Specifically, smart speakers or similar voice assistant devices are used. The pre-processed voice signal is then sent to a server as digital data.

[0282] The server processes the received digital data using a generative AI model. By using natural language processing technology, it analyzes the user's intentions and emotions and generates appropriate responses according to the context. This response generation process is based on a pre-trained AI model. The server further selects visual content related to the generated text from a database. The relevant visual information is sent to the user's display device and is displayed. Specifically, a TV or smartphone within the home is applicable.

[0283] The user can ask further questions based on the presented information, and the system can continuously respond to this. The system continuously learns using the data collected through feedback, thereby making the next interaction more personalized and valuable to the user.

[0284] As an example, consider a scenario where a child asks a smart speaker, "What is this bird?" After voice recognition, the server displays an image of a sparrow on the home TV along with a response such as "That bird is a sparrow. Sparrows make this kind of chirping sound." In this way, the user can satisfy the child's curiosity without much effort.

[0285] As an example of a prompt sentence for the generative AI model, it is input in the form of "Please analyze the voice 'Tell me about something?' and generate a response to provide relevant information and visual content. Try to give a balanced educational answer and combine visual elements that can attract a child's interest."

[0286] With this system, the user can enhance the educational environment within the home and achieve effective parent-child communication even in a busy situation.

[0287] The flow of the specific process in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The user speaks to a voice input device such as a smart speaker, inputting questions as voice. For example, the user might ask, "What kind of bird is this?" The device acquires this voice data using its microphone and stores it internally as digital data.

[0290] Step 2:

[0291] The terminal processes the acquired audio data to remove noise. In this step, an audio signal processing algorithm is used to reduce noise from the audio and improve recognition accuracy. The noise-removed audio data becomes the output for the next step.

[0292] Step 3:

[0293] The terminal sends pre-processed audio data to the server over the network. The input here is the audio data that has been de-noised in the previous step, and the output is a data packet to be sent to the server.

[0294] Step 4:

[0295] The server converts received digital audio data into text using natural language processing technology. Specifically, it performs speech recognition using a generative AI model and extracts text information from the audio. The input is audio data, and the output is text data.

[0296] Step 5:

[0297] The server analyzes the converted text using a generative AI model and generates an appropriate response based on the user's intent and context. For example, in response to a user's question, it might generate a text response such as, "That bird is a sparrow. Let's listen to the sound of a sparrow chirping." The input is text data, and the output is the generated response.

[0298] Step 6:

[0299] The server selects visual content related to the generated response from the database and prepares the data to be sent to the user's display device. Specifically, this involves selecting an image of a sparrow. The inputs are the generated response data and data about the user's visual environment, and the output is the data prepared for transmission.

[0300] Step 7:

[0301] The device analyzes data received from the server and displays appropriate visual information on the television or smartphone. Specifically, an image of a sparrow is displayed on the television. The input is data transmitted from the server, and the output is visual information from the user's perspective.

[0302] Step 8:

[0303] Based on the information presented, the user can ask additional questions via voice. This new voice input is then taken into the device, and the process restarts. The user's feedback is used for subsequent learning, improving the accuracy of the generative AI model. The input is the user's new question, and the output is the feedback data.

[0304] (Application Example 1)

[0305] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0306] In today's world, there is a need for information delivery systems that effectively support children's curiosity and educational needs. However, conventional systems suffer from insufficient accuracy in voice recognition, inadequate content selection, and inadequate utilization of user feedback, hindering natural communication between parents and children. Furthermore, they are unable to adapt to children's dynamically changing interests, making it difficult to provide consistent educational support.

[0307] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means respectively.

[0308] In this invention, the server includes terminal means capable of recognizing voice in real time and converting it into digital data, information processing means for generating appropriate conversations using voice data analyzed based on user characteristics and interests, communication means for transmitting the generated conversations and related visual contents to the user's information communication terminal or video device, and media selection means for selecting and displaying related information based on behavior prediction. Thereby, it is possible to effectively respond to the dynamic interests of children, promote natural communication between parents and children, and appropriately provide educational information.

[0309] The "terminal means" is a device having a function of recognizing voice in real time and converting it into digital data.

[0310] The "information processing means" is a processor having a function of analyzing voice data based on user characteristics and interests and generating appropriate conversations.

[0311] The "communication means" is a system having a function of transmitting the generated conversations and related visual contents to an information communication terminal or a video device.

[0312] The "intelligent learning means" is an algorithm having a self-learning function of collecting feedback data from the user and reflecting it in the generation of conversations after the next time.

[0313] The "media selection means" is a technology having the ability to select and display related information based on behavior prediction.

[0314] The present invention is implemented as a system for providing interactive educational content adapted to user characteristics. The system includes the following main elements.

[0315] First, a "terminal device" is used for the user to perform voice input. Specifically, portable information terminals such as smartphones and tablets are used. These devices recognize speech in real time and convert the speech into digital data using the Google Speech-to-Text API or similar.

[0316] Subsequently, a server, acting as an "information processing tool," receives the converted audio data. This server analyzes the audio data using a large-scale language model. In particular, it utilizes natural language processing techniques to generate appropriate dialogue and related media content based on the user's interests and characteristics. OpenAI's natural language processing model, among others, is employed.

[0317] The generated content is streamed to the user's mobile device or video device, such as a television, via the cloud infrastructure, which serves as the "means of communication." In particular, AWS (Amazon Web Services) is used to ensure high-speed and stable content delivery.

[0318] As an "intelligent learning tool," user feedback data is stored on the server and reflected in the generation of subsequent dialogues. Using this feedback data, the AI ​​model continuously learns, providing a more personalized educational experience. In this way, the system evolves along with the user's growth.

[0319] For example, if a user asks "Tell me about dinosaurs," the server will automatically generate video content explaining the history and ecology of dinosaurs based on this request and send it to the device. Using the "Generating AI Model, Prompt Text," a prompt like the following is formed.

[0320] Example of a prompt:

[0321] "As educational content for children, please display visual information about 'dinosaurs' based on the voice recognition results."

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The user asks a question by voice into a device such as a smartphone or tablet. This voice input is captured by the device. The device then performs noise reduction and converts the voice data into text using the Google Speech-to-Text API. Through this process, the device outputs the voice input as digital text data.

[0325] Step 2:

[0326] The terminal sends the converted text data to the server. The server receives this data and analyzes its content using a natural language processing engine. Specifically, it utilizes OpenAI's language model to understand the context of the text data and generate prompts based on the user's age and interests. In this step, the server outputs the prompt text and related topic information.

[0327] Step 3:

[0328] The server searches for and identifies relevant educational content based on prompts analyzed by a generative AI model. This involves using a database on AWS to select the appropriate visual and audio content. The output of this operation is media content that matches the user's interests.

[0329] Step 4:

[0330] The server transmits the selected content to the user's terminal via a communication method. Here, an encoding process takes place, and the content is delivered in streaming format. The user's terminal receives this content and displays it in real time. This allows the user to visually experience interactive information.

[0331] Step 5:

[0332] Finally, user feedback is collected on the device and sent to the server. The server uses the collected feedback to apply intelligent learning algorithms and optimize the system's response for future use. This process allows the system to continuously improve and provide more personalized educational services.

[0333] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0334] This invention provides a system that offers more appropriate and personalized responses in natural communication using the user's voice by utilizing emotion recognition technology. This system is designed to improve the quality of interaction between children and parents.

[0335] In this system, the user first speaks to the smart speaker. For example, suppose a child says, "Today was fun." This voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the audio data to a server via the network, where voice recognition and sentiment analysis are performed simultaneously.

[0336] The server uses an emotion recognition engine to analyze speech and identify the user's emotions. Based on this emotion data, the server generates appropriate conversation content and related video content. For example, if a child is enjoying themselves, it can suggest fun animations or quizzes.

[0337] The generated conversations and content are sent from the server to the user's television or mobile device. The device receives this information and provides a more impactful experience for children by displaying visual images and audio.

[0338] Furthermore, user feedback and new questions are collected again by the device and sent to the server. The server uses a learning algorithm based on this data to reflect in future conversation generation and content suggestions. Sentimental information, in particular, is an important indicator in this learning process, giving the system the flexibility to adapt to the user's situation.

[0339] In addition, this system also manages the user's schedule, streamlining daily life management by providing appropriate reminders and schedule adjustments based on the user's emotional state.

[0340] As described above, the present invention utilizes emotion recognition to provide users with a more personalized experience, thereby improving parent-child communication and educational outcomes.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The device records the user's speech in real time and performs noise reduction and preprocessing of the audio signal. This prepares the audio data for analysis.

[0344] Step 2:

[0345] The terminal sends pre-processed audio data to the server. The server prepares the received audio data for analysis.

[0346] Step 3:

[0347] The server uses a speech recognition engine to convert speech data into text. Furthermore, it uses an emotion recognition engine to analyze the user's emotions and obtain emotion data.

[0348] Step 4:

[0349] The server generates appropriate conversation content based on analyzed text and sentiment data. It selects responses based on the user's age, interests, and emotions.

[0350] Step 5:

[0351] The server retrieves relevant video content from a database and selects content that matches the user's emotions. The selected information is then transmitted to the terminal and the user's display device.

[0352] Step 6:

[0353] The terminal prepares and displays the conversation and video information received from the server on a television or mobile device. This provides information to the user through both sight and sound.

[0354] Step 7:

[0355] The device records any new comments or questions submitted by the user and sends them to the next server.

[0356] Step 8:

[0357] The server collects user feedback and sentiment data, and uses machine learning algorithms to improve the model for future conversations and content suggestions.

[0358] (Example 2)

[0359] Next, we will describe Example 2. 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".

[0360] In modern society, where opportunities for communication are decreasing, there is a growing need to promote effective dialogue, particularly between parents and children, and to provide flexible responses tailored to each user's emotions. Furthermore, accurately understanding users' emotional states and providing lifestyle suggestions and information based on those understandings is desired to improve the efficiency and quality of their lives. However, current technology is insufficient for recognizing emotions and generating appropriate responses based on them, necessitating more advanced systems.

[0361] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0362] In this invention, the server includes processing means for acquiring audio in real time, performing noise reduction and preprocessing, and then converting it into a digital signal; generation means for analyzing the user's emotional state and dynamically generating corresponding dialogue content and visual information; and response means for enhancing the user's emotion-based response using a generation AI model. This makes it possible to accurately recognize the user's emotions and provide personalized, interactive responses in real time based on those emotions.

[0363] A "processing device" is a device that acquires the user's voice and converts it into a digital signal while removing noise.

[0364] A "generation device" is a device that analyzes the user's emotional state and dynamically generates appropriate dialogue content and visual information based on that information.

[0365] A "response mechanism" is a device that uses a generative AI model to provide responses tailored to the user's emotions and plays a role in enhancing those responses.

[0366] A "visual display means" is a device that has the function of transmitting generated dialogue and visual information to the user's display device and playing it back.

[0367] An "information learning device" is a device that collects responses and feedback from users and incorporates that information to improve the performance of the system.

[0368] A "schedule management device" is a device that adjusts information such as schedules according to the user's emotions, thereby streamlining daily management.

[0369] An "information presentation device" is a device that has the function of selecting and displaying relevant visual information based on the user's request.

[0370] This invention is a system that utilizes emotion recognition technology to provide users with personalized conversational experiences. The system primarily aims to improve user interaction and functions by combining real-time processing, emotion analysis, and response generation.

[0371] First, the user provides voice input via a smart speaker. This voice is acquired by the device and subjected to noise reduction and pre-processing. The device relies on hardware for this processing, for example, using a high-performance DSP (Digital Signal Processing) chip to perform it efficiently. The processed voice data is then sent to a server.

[0372] The server uses speech recognition software to convert speech data into text and an emotion recognition engine to analyze the user's emotions. The technologies used include, for example, machine learning algorithms and neural networks. Based on the analysis results, the server utilizes a generative AI model to dynamically generate appropriate responses and visual information.

[0373] The generated content is sent from the server to the user's display device. For example, if the emotion is determined to be "enjoyment," the device will display a fun animation on the user's TV or mobile device. This allows the user to enjoy an interactive experience with the system.

[0374] As a further interaction, user feedback is collected on the device and sent to the server. The server then applies a learning algorithm based on this information and incorporates it to improve the system's response. Through this process, the system can continuously learn and evolve.

[0375] Furthermore, to support users' daily lives, the server provides a schedule management function that can adjust appointments and generate reminders based on their emotions. For example, if it determines that a user is feeling stressed, it can suggest activities to help them relax.

[0376] A concrete example of a prompt could be something like, "Animation ideas to suggest when the user is enjoying themselves." This allows the system to generate responses and content that are best suited to the user's situation and emotions.

[0377] The above describes specific embodiments for carrying out the present invention.

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] Users speak to the smart speaker, providing their emotions and thoughts to the system as voice input. The input voice is captured by the device as raw sound data.

[0381] Step 2:

[0382] The terminal performs noise reduction and audio signal preprocessing on the acquired raw sound data. Specifically, it uses digital signal processing technology to perform noise filtering and generate clear audio data. This results in the output of high-quality audio data to improve speech recognition accuracy.

[0383] Step 3:

[0384] The terminal sends pre-processed audio data to the server via a secure network. The server receives the audio data and prepares for the next processing step.

[0385] Step 4:

[0386] The server uses speech recognition software to convert speech data into text data. During this process, the speech signal is broken down into phonemes through a feature extraction process and output as text. In parallel, an emotion recognition engine is used to analyze the user's emotional state from the text data. The output of this analysis is emotion data.

[0387] Step 5:

[0388] The server utilizes a generative AI model, using emotion data and text data from the user as prompts to dynamically generate appropriate dialogue and visual content. For example, if the user is in a "fun" state, an AI model for generating fun animations is activated and the corresponding content data is output.

[0389] Step 6:

[0390] The server sends the generated conversation content and other materials to the user's device. The destination is the user's television or mobile device, and the server also performs format conversion according to the characteristics of the connected device.

[0391] Step 7:

[0392] The device displays or plays received content for the user. This includes functions such as displaying video on the screen and playing audio through the speaker. Through this, the user can experience rich interaction with the system.

[0393] Step 8:

[0394] Users can provide additional feedback and questions about the content they are given. This input becomes the data needed to create new conversational flows.

[0395] Step 9:

[0396] The device collects user feedback and sends it to the server. The server analyzes this data using a learning algorithm to improve the accuracy of responses in future dialogue generation processes. User sentiment information and feedback drive the system's evolution through repeated learning.

[0397] Step 10:

[0398] The server utilizes emotional information to manage the user's schedule and provide lifestyle suggestions optimized for their emotional state. For example, when relaxation is needed, it suggests content with relaxing effects. This makes it possible to improve the quality of the user's daily life.

[0399] (Application Example 2)

[0400] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0401] In today's world, enriching parent-child communication and educational interaction requires individualized support tailored to each child's emotions. However, existing technologies lack the means to recognize a child's emotions in real time and provide appropriate content and dialogue accordingly. As a result, support that fits children's interests and emotions is not provided, leading to insufficient learning effectiveness and emotional satisfaction.

[0402] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0403] In this invention, the server includes an information terminal means capable of recognizing voice in real time and converting it into digital information, an information processing means that generates appropriate dialogue using voice information analyzed based on the user's emotional state, and a communication means that transmits the generated dialogue and related visual content to the user's mobile terminal or video display device. This makes it possible to provide appropriate dialogue and content that responds to a child's emotions.

[0404] An "information terminal device" is a device that recognizes voice in real time and converts it into digital information.

[0405] "Information processing means" refers to a device or system that generates appropriate dialogue using voice information analyzed based on the user's emotional state.

[0406] "Communication means" refers to a device or system for transmitting the generated dialogue and associated visual content to a user's mobile terminal or video display device.

[0407] A "learning processing means" is a device or system for collecting user emotional information and reflecting it in the generation of subsequent dialogues.

[0408] "Information presentation means" refers to a device or system for acquiring and presenting content appropriately according to the user's emotions.

[0409] "Media presentation means" refers to a device or system for selecting and presenting relevant content based on the user's emotion recognition results.

[0410] The system implementing this invention is built around an "information terminal means" that recognizes speech in real time and converts it into digital information. Specifically, a mobile information terminal such as a smartphone or tablet is used. When the terminal acquires speech data, it performs real-time noise reduction preprocessing and then transmits the speech data to a server via the network.

[0411] The server uses "information processing means" to analyze the user's emotional state from the audio data. For emotion recognition, advanced AI models are used, such as Google Cloud's Natural Language API. This API is used to analyze emotions and generate appropriate dialogue. Furthermore, based on the results, relevant visual content is appropriately selected via "communication means" and transmitted to the user's mobile device or video display device.

[0412] During the learning process, the server uses a "learning processing method" to continuously collect feedback from users and improve the system's accuracy by reflecting this feedback in subsequent interactions and content generation.

[0413] For example, if a child user speaks to the information terminal and the system recognizes that they are having fun, the server activates an "emotion-responsive content curator" and suggests fun animations or quizzes. This allows parents to confidently provide their children with appropriate, educational, and emotionally resonant support.

[0414] Furthermore, in order to provide diverse content based on the user's emotions through the "information presentation means," a generative AI model is used to utilize prompt phrases such as, "Please tell me how to recognize emotions from a child's voice and suggest the most suitable learning content based on that," thereby enabling the provision of optimal content.

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The device records the child's voice in real time. The input is raw audio data, which is pre-processed to remove noise. The output is clear audio data with the noise removed.

[0418] Step 2:

[0419] The terminal sends pre-processed audio data to the server via the network. The input is clear audio data, which is sent directly to the server as output. The specific operation for network communication utilizes an internet connection.

[0420] Step 3:

[0421] The server analyzes the transmitted audio data and uses an emotion recognition engine to identify the user's emotional state. The input is audio data, and the output is the analyzed emotion information. Here, emotion analysis is performed using Google Cloud's Natural Language API, among others.

[0422] Step 4:

[0423] The server generates appropriate dialogue and visual content based on emotional information. The input is emotional information, and an AI model is used to select relevant content. The output includes the generated dialogue text and content identifiers.

[0424] Step 5:

[0425] The server sends content to the terminal. The input is the generated dialogue text and content identifier, which are then transmitted to the terminal as output. Specifically, the data is packetized and transmitted using a communication protocol.

[0426] Step 6:

[0427] The terminal displays or plays received dialogue text and content to the user. Input is data from the server, and output is visualized dialogue and visual content. Specific actions include displaying this content on the application screen.

[0428] Step 7:

[0429] The user reacts to the system's presentation, and the device collects these reactions as data. Input consists of user actions and feedback, while output is feedback data used to generate future dialogues. Taps on the UI and voice input are recorded for this data collection.

[0430] Step 8:

[0431] The server stores the collected feedback data and uses it as learning material for future interactions and content generation. The input is feedback data, and the output is an updated learning model or stored data. To apply a generative AI model to this learning process, prompts such as "Please tell me how to recognize emotions from a child's voice and suggest the most appropriate learning content based on that" are used.

[0432] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0438] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0440] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0442] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0443] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0444] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0445] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0447] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0448] This invention provides an interface system that enables natural and fluent communication between a user and a computer system. It supports communication between children and parents using voice and provides appropriate information tailored to the child's interests. Specific embodiments of this system are described below.

[0449] First, the user speaks to the smart speaker. For example, consider a child asking, "What kind of bird is this?" The voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the processed audio data to a server over the network.

[0450] Next, the server analyzes the received audio data. Using natural language processing techniques, it converts the audio into text and generates the most appropriate response, taking into account the context, emotions, and the user's age and interests. For example, it might generate a response such as, "That bird is a sparrow. Shall we see what a sparrow's call sounds like?"

[0451] The generated text and associated video content are selected from the server and sent to the appropriate display device. This could be a television in the user's home or a parent's mobile device. The device receives this information and provides the child with a more concrete experience by displaying images and sounds of bird songs on the television.

[0452] The user can ask further questions based on the information newly acquired through this process, and the device continues to send this new audio back to the server. The server learns from this feedback data and uses it to improve future interactions. This allows the system to provide more personalized answers over time.

[0453] Thus, this invention creates an environment that naturally responds to children's immediate interests and provides educational information, even while parents are doing household chores. Users can interact through a smart speaker without interrupting their work, thereby deepening parent-child communication and effectively expanding children's curiosity and knowledge.

[0454] The following describes the processing flow.

[0455] Step 1:

[0456] The device records the user's voice in real time and performs pre-processing such as noise reduction and volume adjustment. This prepares the audio for conversion into digital data.

[0457] Step 2:

[0458] The device sends pre-processed audio data to the server. This data contains the content of the user's speech.

[0459] Step 3:

[0460] The server analyzes the received audio data using natural language processing techniques and converts it into text data. Context, emotions, user age, and interests are also analyzed.

[0461] Step 4:

[0462] The server generates an appropriate response based on the analyzed data. In doing so, it also refers to past conversation history and selects information based on the user's interests.

[0463] Step 5:

[0464] The server sends the generated conversation text and instructions to the user's device to transmit selected related video content to their TV or mobile device.

[0465] Step 6:

[0466] The terminal uses information received from the server to display video and audio on a television or mobile device. This provides users with information visually.

[0467] Step 7:

[0468] When the user asks a new question or gives a response, the device records it again as audio and sends it to the server. This process enables a continuous and natural conversation.

[0469] Step 8:

[0470] The server updates its database based on user feedback and learns for future response generation. This makes subsequent conversations more personalized and effective.

[0471] (Example 1)

[0472] Next, we will describe Example 1. 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."

[0473] In modern families, parents are often too busy to communicate effectively with their children. Therefore, there is a need for methods to provide educational dialogue that satisfies children's curiosity even when parents are unable to actively engage with them. While voice-based interfaces are common, they struggle to provide personalized responses based on context and emotions. Consequently, a new system is needed that automatically generates natural and effective dialogue tailored to the user's needs and interests, and combines this with visual information.

[0474] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0475] In this invention, the server includes a device capable of recognizing speech in real time and pre-processing it using signal processing technology; a communication means for converting the pre-processed speech into digital data and transmitting it over a network; an information processing means for analyzing the received speech data using a generation AI model and generating appropriate information based on context and emotion; a transmission means for selecting visual content related to the generated information and transmitting it to a display device; and a learning means for collecting user feedback and reflecting it in future information generation. This makes it possible for parents to deepen dialogue with their children without having to do anything themselves and to effectively provide educational information based on the child's interests.

[0476] A "device for real-time voice recognition" refers to a device that has the function of instantly acquiring voice input and converting it into digital data that can be processed instantaneously.

[0477] "Preprocessing using signal processing technology" refers to a series of processing techniques performed to remove noise from audio data and improve the quality of the audio signal.

[0478] "Communication methods for converting to digital data" refers to the technology and procedures for converting audio signals into a digital format and transmitting them to a server over a network.

[0479] "Information processing means that analyze data using a generative AI model" refers to a method that uses AI technology to analyze received digital data, understand the user's intentions and emotions, and generate an appropriate response.

[0480] "Transmission means for selecting relevant visual content" refers to the technology and functions for selecting appropriate video or image data based on generated information and transmitting it to the user's display device.

[0481] A "learning method for collecting user feedback" refers to a learning algorithm that collects responses and actions from users as data and uses that data to improve future responses.

[0482] This invention relates to an interface system that provides natural and effective dialogue, utilizing speech recognition, natural language processing, and generative AI models. The system primarily consists of terminals and servers, each functioning with its own distinct role.

[0483] The terminal acquires voice from the user using a voice input device that recognizes speech in real time. This device instantly converts the user's speech into digital data and performs noise reduction and signal processing. Specifically, smart speakers or similar voice assistant devices are used. The pre-processed voice signal is then sent to a server as digital data.

[0484] The server processes the received digital data using a generation AI model. Using natural language processing techniques, it analyzes the user's intent and emotions, and generates appropriate responses based on the context. This response generation process is based on a pre-trained AI model. The server then selects visual content related to the generated text from a database. The relevant visual information is sent to the user's display device, where it is displayed. Specifically, this can be done on a home television or smartphone.

[0485] Users can ask further questions based on the information presented, and the system can respond to these questions sequentially. The system continuously learns using the data collected through feedback, which makes subsequent interactions more personalized and valuable to the user.

[0486] As an example, consider a scenario where a child asks a smart speaker, "What kind of bird is this?" After voice recognition, the server responds with something like, "That's a sparrow. This is what a sparrow sounds like," and displays an image of a sparrow on the home television. In this way, the user can satisfy the child's curiosity without any effort on their part.

[0487] An example of a prompt for the generative AI model would be: "Analyze the audio 'Tell me about...' and generate a response that provides relevant information and visual content. Aim for a balanced educational response, incorporating visual elements that will capture a child's interest."

[0488] This system allows users to enhance the educational environment within the home and achieve effective parent-child communication even in busy situations.

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The user speaks to a voice input device such as a smart speaker, inputting questions as voice. For example, the user might ask, "What kind of bird is this?" The device acquires this voice data using its microphone and stores it internally as digital data.

[0492] Step 2:

[0493] The terminal processes the acquired audio data to remove noise. In this step, an audio signal processing algorithm is used to reduce noise from the audio and improve recognition accuracy. The noise-removed audio data becomes the output for the next step.

[0494] Step 3:

[0495] The terminal sends pre-processed audio data to the server over the network. The input here is the audio data that has been de-noised in the previous step, and the output is a data packet to be sent to the server.

[0496] Step 4:

[0497] The server converts received digital audio data into text using natural language processing technology. Specifically, it performs speech recognition using a generative AI model and extracts text information from the audio. The input is audio data, and the output is text data.

[0498] Step 5:

[0499] The server analyzes the converted text using a generative AI model and generates an appropriate response based on the user's intent and context. For example, in response to a user's question, it might generate a text response such as, "That bird is a sparrow. Let's listen to the sound of a sparrow chirping." The input is text data, and the output is the generated response.

[0500] Step 6:

[0501] The server selects visual content related to the generated response from the database and prepares the data to be sent to the user's display device. Specifically, this involves selecting an image of a sparrow. The inputs are the generated response data and data about the user's visual environment, and the output is the data prepared for transmission.

[0502] Step 7:

[0503] The device analyzes data received from the server and displays appropriate visual information on the television or smartphone. Specifically, an image of a sparrow is displayed on the television. The input is data transmitted from the server, and the output is visual information from the user's perspective.

[0504] Step 8:

[0505] Based on the information presented, the user can ask additional questions via voice. This new voice input is then taken into the device, and the process restarts. The user's feedback is used for subsequent learning, improving the accuracy of the generative AI model. The input is the user's new question, and the output is the feedback data.

[0506] (Application Example 1)

[0507] Next, we will explain Application Example 1. In the following explanation, 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."

[0508] In today's world, there is a need for information delivery systems that effectively support children's curiosity and educational needs. However, conventional systems suffer from insufficient accuracy in voice recognition, inadequate content selection, and inadequate utilization of user feedback, hindering natural communication between parents and children. Furthermore, they are unable to adapt to children's dynamically changing interests, making it difficult to provide consistent educational support.

[0509] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0510] In this invention, the server includes terminal means capable of recognizing voice in real time and converting it into digital data; information processing means that generate appropriate dialogue using voice data analyzed based on the user's characteristics and interests; communication means that transmit the generated dialogue and related visual content to the user's information communication terminal or video device; and media selection means that select and display relevant information based on behavioral predictions. This makes it possible to effectively respond to children's dynamic interests, promote natural communication between parents and children, and appropriately provide educational information.

[0511] A "terminal device" is a device that has the function of recognizing voice in real time and converting it into digital data.

[0512] "Information processing means" refers to a processor that analyzes voice data based on the user's characteristics and interests and generates appropriate dialogue.

[0513] "Communication means" refers to a system that has the function of transmitting generated dialogue and related visual content to an information and communication terminal or video device.

[0514] An "intelligent learning tool" is an algorithm with a self-learning function that collects user feedback data and incorporates it into the generation of subsequent dialogues.

[0515] "Media selection means" refers to technology that has the ability to select and display relevant information based on behavioral predictions.

[0516] This invention is implemented as a system that provides interactive educational content tailored to the characteristics of the user. The system includes the following main elements:

[0517] First, a "terminal device" is used for the user to perform voice input. Specifically, portable information terminals such as smartphones and tablets are used. These devices recognize speech in real time and convert the speech into digital data using the Google Speech-to-Text API or similar.

[0518] Subsequently, a server, acting as an "information processing tool," receives the converted audio data. This server analyzes the audio data using a large-scale language model. In particular, it utilizes natural language processing techniques to generate appropriate dialogue and related media content based on the user's interests and characteristics. OpenAI's natural language processing model, among others, is employed.

[0519] The generated content is streamed to the user's mobile device or video device, such as a television, via the cloud infrastructure, which serves as the "means of communication." In particular, AWS (Amazon Web Services) is used to ensure high-speed and stable content delivery.

[0520] As an "intelligent learning tool," user feedback data is stored on the server and reflected in the generation of subsequent dialogues. Using this feedback data, the AI ​​model continuously learns, providing a more personalized educational experience. In this way, the system evolves along with the user's growth.

[0521] For example, if a user asks "Tell me about dinosaurs," the server will automatically generate video content explaining the history and ecology of dinosaurs based on this request and send it to the device. Using the "Generating AI Model, Prompt Text," a prompt like the following is formed.

[0522] Example of a prompt:

[0523] "As educational content for children, please display visual information about 'dinosaurs' based on the voice recognition results."

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The user asks a question by voice into a device such as a smartphone or tablet. This voice input is captured by the device. The device then performs noise reduction and converts the voice data into text using the Google Speech-to-Text API. Through this process, the device outputs the voice input as digital text data.

[0527] Step 2:

[0528] The terminal sends the converted text data to the server. The server receives this data and analyzes its content using a natural language processing engine. Specifically, it utilizes OpenAI's language model to understand the context of the text data and generate prompts based on the user's age and interests. In this step, the server outputs the prompt text and related topic information.

[0529] Step 3:

[0530] The server searches for and identifies relevant educational content based on prompts analyzed by a generative AI model. This involves using a database on AWS to select the appropriate visual and audio content. The output of this operation is media content that matches the user's interests.

[0531] Step 4:

[0532] The server transmits the selected content to the user's terminal via a communication method. Here, an encoding process takes place, and the content is delivered in streaming format. The user's terminal receives this content and displays it in real time. This allows the user to visually experience interactive information.

[0533] Step 5:

[0534] Finally, user feedback is collected on the device and sent to the server. The server uses the collected feedback to apply intelligent learning algorithms and optimize the system's response for future use. This process allows the system to continuously improve and provide more personalized educational services.

[0535] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0536] This invention provides a system that offers more appropriate and personalized responses in natural communication using the user's voice by utilizing emotion recognition technology. This system is designed to improve the quality of interaction between children and parents.

[0537] In this system, the user first speaks to the smart speaker. For example, suppose a child says, "Today was fun." This voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the audio data to a server via the network, where voice recognition and sentiment analysis are performed simultaneously.

[0538] The server uses an emotion recognition engine to analyze speech and identify the user's emotions. Based on this emotion data, the server generates appropriate conversation content and related video content. For example, if a child is enjoying themselves, it can suggest fun animations or quizzes.

[0539] The generated conversations and content are sent from the server to the user's television or mobile device. The device receives this information and provides a more impactful experience for children by displaying visual images and audio.

[0540] Furthermore, user feedback and new questions are collected again by the device and sent to the server. The server uses a learning algorithm based on this data to reflect in future conversation generation and content suggestions. Sentimental information, in particular, is an important indicator in this learning process, giving the system the flexibility to adapt to the user's situation.

[0541] In addition, this system also manages the user's schedule, streamlining daily life management by providing appropriate reminders and schedule adjustments based on the user's emotional state.

[0542] As described above, the present invention utilizes emotion recognition to provide users with a more personalized experience, thereby improving parent-child communication and educational outcomes.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] The device records the user's speech in real time and performs noise reduction and preprocessing of the audio signal. This prepares the audio data for analysis.

[0546] Step 2:

[0547] The terminal sends pre-processed audio data to the server. The server prepares the received audio data for analysis.

[0548] Step 3:

[0549] The server uses a speech recognition engine to convert speech data into text. Furthermore, it uses an emotion recognition engine to analyze the user's emotions and obtain emotion data.

[0550] Step 4:

[0551] The server generates appropriate conversation content based on analyzed text and sentiment data. It selects responses based on the user's age, interests, and emotions.

[0552] Step 5:

[0553] The server retrieves relevant video content from a database and selects content that matches the user's emotions. The selected information is then transmitted to the terminal and the user's display device.

[0554] Step 6:

[0555] The terminal prepares and displays the conversation and video information received from the server on a television or mobile device. This provides information to the user through both sight and sound.

[0556] Step 7:

[0557] The device records any new comments or questions submitted by the user and sends them to the next server.

[0558] Step 8:

[0559] The server collects user feedback and sentiment data, and uses machine learning algorithms to improve the model for future conversations and content suggestions.

[0560] (Example 2)

[0561] Next, we will describe Example 2. 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."

[0562] In modern society, where opportunities for communication are decreasing, there is a growing need to promote effective dialogue, particularly between parents and children, and to provide flexible responses tailored to each user's emotions. Furthermore, accurately understanding users' emotional states and providing lifestyle suggestions and information based on those understandings is desired to improve the efficiency and quality of their lives. However, current technology is insufficient for recognizing emotions and generating appropriate responses based on them, necessitating more advanced systems.

[0563] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0564] In this invention, the server includes processing means for acquiring audio in real time, performing noise reduction and preprocessing, and then converting it into a digital signal; generation means for analyzing the user's emotional state and dynamically generating corresponding dialogue content and visual information; and response means for enhancing the user's emotion-based response using a generation AI model. This makes it possible to accurately recognize the user's emotions and provide personalized, interactive responses in real time based on those emotions.

[0565] A "processing device" is a device that acquires the user's voice and converts it into a digital signal while removing noise.

[0566] A "generation device" is a device that analyzes the user's emotional state and dynamically generates appropriate dialogue content and visual information based on that information.

[0567] A "response mechanism" is a device that uses a generative AI model to provide responses tailored to the user's emotions and plays a role in enhancing those responses.

[0568] A "visual display means" is a device that has the function of transmitting generated dialogue and visual information to the user's display device and playing it back.

[0569] An "information learning device" is a device that collects responses and feedback from users and incorporates that information to improve the performance of the system.

[0570] A "schedule management device" is a device that adjusts information such as schedules according to the user's emotions, thereby streamlining daily management.

[0571] An "information presentation device" is a device that has the function of selecting and displaying relevant visual information based on the user's request.

[0572] This invention is a system that utilizes emotion recognition technology to provide users with personalized conversational experiences. The system primarily aims to improve user interaction and functions by combining real-time processing, emotion analysis, and response generation.

[0573] First, the user provides voice input via a smart speaker. This voice is acquired by the device and subjected to noise reduction and pre-processing. The device relies on hardware for this processing, for example, using a high-performance DSP (Digital Signal Processing) chip to perform it efficiently. The processed voice data is then sent to a server.

[0574] The server uses speech recognition software to convert speech data into text and an emotion recognition engine to analyze the user's emotions. The technologies used include, for example, machine learning algorithms and neural networks. Based on the analysis results, the server utilizes a generative AI model to dynamically generate appropriate responses and visual information.

[0575] The generated content is sent from the server to the user's display device. For example, if the emotion is determined to be "enjoyment," the device will display a fun animation on the user's TV or mobile device. This allows the user to enjoy an interactive experience with the system.

[0576] As a further interaction, user feedback is collected on the device and sent to the server. The server then applies a learning algorithm based on this information and incorporates it to improve the system's response. Through this process, the system can continuously learn and evolve.

[0577] Furthermore, to support users' daily lives, the server provides a schedule management function that can adjust appointments and generate reminders based on their emotions. For example, if it determines that a user is feeling stressed, it can suggest activities to help them relax.

[0578] A concrete example of a prompt could be something like, "Animation ideas to suggest when the user is enjoying themselves." This allows the system to generate responses and content that are best suited to the user's situation and emotions.

[0579] The above describes specific embodiments for carrying out the present invention.

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] Users speak to the smart speaker, providing their emotions and thoughts to the system as voice input. The input voice is captured by the device as raw sound data.

[0583] Step 2:

[0584] The terminal performs noise reduction and audio signal preprocessing on the acquired raw sound data. Specifically, it uses digital signal processing technology to perform noise filtering and generate clear audio data. This results in the output of high-quality audio data to improve speech recognition accuracy.

[0585] Step 3:

[0586] The terminal sends pre-processed audio data to the server via a secure network. The server receives the audio data and prepares for the next processing step.

[0587] Step 4:

[0588] The server uses speech recognition software to convert speech data into text data. During this process, the speech signal is broken down into phonemes through a feature extraction process and output as text. In parallel, an emotion recognition engine is used to analyze the user's emotional state from the text data. The output of this analysis is emotion data.

[0589] Step 5:

[0590] The server utilizes a generative AI model, using emotion data and text data from the user as prompts to dynamically generate appropriate dialogue and visual content. For example, if the user is in a "fun" state, an AI model for generating fun animations is activated and the corresponding content data is output.

[0591] Step 6:

[0592] The server sends the generated conversation content and other materials to the user's device. The destination is the user's television or mobile device, and the server also performs format conversion according to the characteristics of the connected device.

[0593] Step 7:

[0594] The device displays or plays received content for the user. This includes functions such as displaying video on the screen and playing audio through the speaker. Through this, the user can experience rich interaction with the system.

[0595] Step 8:

[0596] Users can provide additional feedback and questions about the content they are given. This input becomes the data needed to create new conversational flows.

[0597] Step 9:

[0598] The device collects user feedback and sends it to the server. The server analyzes this data using a learning algorithm to improve the accuracy of responses in future dialogue generation processes. User sentiment information and feedback drive the system's evolution through repeated learning.

[0599] Step 10:

[0600] The server utilizes emotional information to manage the user's schedule and provide lifestyle suggestions optimized for their emotional state. For example, when relaxation is needed, it suggests content with relaxing effects. This makes it possible to improve the quality of the user's daily life.

[0601] (Application Example 2)

[0602] Next, we will explain application example 2. In the following explanation, 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."

[0603] In today's world, enriching parent-child communication and educational interaction requires individualized support tailored to each child's emotions. However, existing technologies lack the means to recognize a child's emotions in real time and provide appropriate content and dialogue accordingly. As a result, support that fits children's interests and emotions is not provided, leading to insufficient learning effectiveness and emotional satisfaction.

[0604] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0605] In this invention, the server includes an information terminal means capable of recognizing voice in real time and converting it into digital information, an information processing means that generates appropriate dialogue using voice information analyzed based on the user's emotional state, and a communication means that transmits the generated dialogue and related visual content to the user's mobile terminal or video display device. This makes it possible to provide appropriate dialogue and content that responds to a child's emotions.

[0606] An "information terminal device" is a device that recognizes voice in real time and converts it into digital information.

[0607] "Information processing means" refers to a device or system that generates appropriate dialogue using voice information analyzed based on the user's emotional state.

[0608] "Communication means" refers to a device or system for transmitting the generated dialogue and associated visual content to a user's mobile terminal or video display device.

[0609] A "learning processing means" is a device or system for collecting user emotional information and reflecting it in the generation of subsequent dialogues.

[0610] "Information presentation means" refers to a device or system for acquiring and presenting content appropriately according to the user's emotions.

[0611] "Media presentation means" refers to a device or system for selecting and presenting relevant content based on the user's emotion recognition results.

[0612] The system implementing this invention is built around an "information terminal means" that recognizes speech in real time and converts it into digital information. Specifically, a mobile information terminal such as a smartphone or tablet is used. When the terminal acquires speech data, it performs real-time noise reduction preprocessing and then transmits the speech data to a server via the network.

[0613] The server uses "information processing means" to analyze the user's emotional state from the audio data. For emotion recognition, advanced AI models are used, such as Google Cloud's Natural Language API. This API is used to analyze emotions and generate appropriate dialogue. Furthermore, based on the results, relevant visual content is appropriately selected via "communication means" and transmitted to the user's mobile device or video display device.

[0614] During the learning process, the server uses a "learning processing method" to continuously collect feedback from users and improve the system's accuracy by reflecting this feedback in subsequent interactions and content generation.

[0615] For example, if a child user speaks to the information terminal and the system recognizes that they are having fun, the server activates an "emotion-responsive content curator" and suggests fun animations or quizzes. This allows parents to confidently provide their children with appropriate, educational, and emotionally resonant support.

[0616] Furthermore, in order to provide diverse content based on the user's emotions through the "information presentation means," a generative AI model is used to utilize prompt phrases such as, "Please tell me how to recognize emotions from a child's voice and suggest the most suitable learning content based on that," thereby enabling the provision of optimal content.

[0617] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0618] Step 1:

[0619] The device records the child's voice in real time. The input is raw audio data, which is pre-processed to remove noise. The output is clear audio data with the noise removed.

[0620] Step 2:

[0621] The terminal sends pre-processed audio data to the server via the network. The input is clear audio data, which is sent directly to the server as output. The specific operation for network communication utilizes an internet connection.

[0622] Step 3:

[0623] The server analyzes the transmitted audio data and uses an emotion recognition engine to identify the user's emotional state. The input is audio data, and the output is the analyzed emotion information. Here, emotion analysis is performed using Google Cloud's Natural Language API, among others.

[0624] Step 4:

[0625] The server generates appropriate dialogue and visual content based on emotional information. The input is emotional information, and an AI model is used to select relevant content. The output includes the generated dialogue text and content identifiers.

[0626] Step 5:

[0627] The server sends content to the terminal. The input is the generated dialogue text and content identifier, which are then transmitted to the terminal as output. Specifically, the data is packetized and transmitted using a communication protocol.

[0628] Step 6:

[0629] The terminal displays or plays received dialogue text and content to the user. Input is data from the server, and output is visualized dialogue and visual content. Specific actions include displaying this content on the application screen.

[0630] Step 7:

[0631] The user reacts to the system's presentation, and the device collects these reactions as data. Input consists of user actions and feedback, while output is feedback data used to generate future dialogues. Taps on the UI and voice input are recorded for this data collection.

[0632] Step 8:

[0633] The server stores the collected feedback data and uses it as learning material for future interactions and content generation. The input is feedback data, and the output is an updated learning model or stored data. To apply a generative AI model to this learning process, prompts such as "Please tell me how to recognize emotions from a child's voice and suggest the most appropriate learning content based on that" are used.

[0634] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0635] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0636] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0637] [Fourth Embodiment]

[0638] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0639] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0640] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0641] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0642] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0643] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0644] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0645] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0646] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0647] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0648] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0649] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0650] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0651] This invention provides an interface system that enables natural and fluent communication between a user and a computer system. It supports communication between children and parents using voice and provides appropriate information tailored to the child's interests. Specific embodiments of this system are described below.

[0652] First, the user speaks to the smart speaker. For example, consider a child asking, "What kind of bird is this?" The voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the processed audio data to a server over the network.

[0653] Next, the server analyzes the received audio data. Using natural language processing techniques, it converts the audio into text and generates the most appropriate response, taking into account the context, emotions, and the user's age and interests. For example, it might generate a response such as, "That bird is a sparrow. Shall we see what a sparrow's call sounds like?"

[0654] The generated text and associated video content are selected from the server and sent to the appropriate display device. This could be a television in the user's home or a parent's mobile device. The device receives this information and provides the child with a more concrete experience by displaying images and sounds of bird songs on the television.

[0655] The user can ask further questions based on the information newly acquired through this process, and the device continues to send this new audio back to the server. The server learns from this feedback data and uses it to improve future interactions. This allows the system to provide more personalized answers over time.

[0656] Thus, this invention creates an environment that naturally responds to children's immediate interests and provides educational information, even while parents are doing household chores. Users can interact through a smart speaker without interrupting their work, thereby deepening parent-child communication and effectively expanding children's curiosity and knowledge.

[0657] The following describes the processing flow.

[0658] Step 1:

[0659] The device records the user's voice in real time and performs pre-processing such as noise reduction and volume adjustment. This prepares the audio for conversion into digital data.

[0660] Step 2:

[0661] The device sends pre-processed audio data to the server. This data contains the content of the user's speech.

[0662] Step 3:

[0663] The server analyzes the received audio data using natural language processing techniques and converts it into text data. Context, emotions, user age, and interests are also analyzed.

[0664] Step 4:

[0665] The server generates an appropriate response based on the analyzed data. In doing so, it also refers to past conversation history and selects information based on the user's interests.

[0666] Step 5:

[0667] The server sends the generated conversation text and instructions to the user's device to transmit selected related video content to their TV or mobile device.

[0668] Step 6:

[0669] The terminal uses information received from the server to display video and audio on a television or mobile device. This provides users with information visually.

[0670] Step 7:

[0671] When the user asks a new question or gives a response, the device records it again as audio and sends it to the server. This process enables a continuous and natural conversation.

[0672] Step 8:

[0673] The server updates its database based on user feedback and learns for future response generation. This makes subsequent conversations more personalized and effective.

[0674] (Example 1)

[0675] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0676] In modern families, parents are often too busy to communicate effectively with their children. Therefore, there is a need for methods to provide educational dialogue that satisfies children's curiosity even when parents are unable to actively engage with them. While voice-based interfaces are common, they struggle to provide personalized responses based on context and emotions. Consequently, a new system is needed that automatically generates natural and effective dialogue tailored to the user's needs and interests, and combines this with visual information.

[0677] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0678] In this invention, the server includes a device capable of recognizing speech in real time and pre-processing it using signal processing technology; a communication means for converting the pre-processed speech into digital data and transmitting it over a network; an information processing means for analyzing the received speech data using a generation AI model and generating appropriate information based on context and emotion; a transmission means for selecting visual content related to the generated information and transmitting it to a display device; and a learning means for collecting user feedback and reflecting it in future information generation. This makes it possible for parents to deepen dialogue with their children without having to do anything themselves and to effectively provide educational information based on the child's interests.

[0679] A "device for real-time voice recognition" refers to a device that has the function of instantly acquiring voice input and converting it into digital data that can be processed instantaneously.

[0680] "Preprocessing using signal processing technology" refers to a series of processing techniques performed to remove noise from audio data and improve the quality of the audio signal.

[0681] "Communication methods for converting to digital data" refers to the technology and procedures for converting audio signals into a digital format and transmitting them to a server over a network.

[0682] "Information processing means that analyze data using a generative AI model" refers to a method that uses AI technology to analyze received digital data, understand the user's intentions and emotions, and generate an appropriate response.

[0683] "Transmission means for selecting relevant visual content" refers to the technology and functions for selecting appropriate video or image data based on generated information and transmitting it to the user's display device.

[0684] A "learning method for collecting user feedback" refers to a learning algorithm that collects responses and actions from users as data and uses that data to improve future responses.

[0685] This invention relates to an interface system that provides natural and effective dialogue, utilizing speech recognition, natural language processing, and generative AI models. The system primarily consists of terminals and servers, each functioning with its own distinct role.

[0686] The terminal acquires voice from the user using a voice input device that recognizes speech in real time. This device instantly converts the user's speech into digital data and performs noise reduction and signal processing. Specifically, smart speakers or similar voice assistant devices are used. The pre-processed voice signal is then sent to a server as digital data.

[0687] The server processes the received digital data using a generation AI model. Using natural language processing techniques, it analyzes the user's intent and emotions, and generates appropriate responses based on the context. This response generation process is based on a pre-trained AI model. The server then selects visual content related to the generated text from a database. The relevant visual information is sent to the user's display device, where it is displayed. Specifically, this can be done on a home television or smartphone.

[0688] Users can ask further questions based on the information presented, and the system can respond to these questions sequentially. The system continuously learns using the data collected through feedback, which makes subsequent interactions more personalized and valuable to the user.

[0689] As an example, consider a scenario where a child asks a smart speaker, "What kind of bird is this?" After voice recognition, the server responds with something like, "That's a sparrow. This is what a sparrow sounds like," and displays an image of a sparrow on the home television. In this way, the user can satisfy the child's curiosity without any effort on their part.

[0690] An example of a prompt for the generative AI model would be: "Analyze the audio 'Tell me about...' and generate a response that provides relevant information and visual content. Aim for a balanced educational response, incorporating visual elements that will capture a child's interest."

[0691] This system allows users to enhance the educational environment within the home and achieve effective parent-child communication even in busy situations.

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The user speaks to a voice input device such as a smart speaker, inputting questions as voice. For example, the user might ask, "What kind of bird is this?" The device acquires this voice data using its microphone and stores it internally as digital data.

[0695] Step 2:

[0696] The terminal processes the acquired audio data to remove noise. In this step, an audio signal processing algorithm is used to reduce noise from the audio and improve recognition accuracy. The noise-removed audio data becomes the output for the next step.

[0697] Step 3:

[0698] The terminal sends pre-processed audio data to the server over the network. The input here is the audio data that has been de-noised in the previous step, and the output is a data packet to be sent to the server.

[0699] Step 4:

[0700] The server converts received digital audio data into text using natural language processing technology. Specifically, it performs speech recognition using a generative AI model and extracts text information from the audio. The input is audio data, and the output is text data.

[0701] Step 5:

[0702] The server analyzes the converted text using a generative AI model and generates an appropriate response based on the user's intent and context. For example, in response to a user's question, it might generate a text response such as, "That bird is a sparrow. Let's listen to the sound of a sparrow chirping." The input is text data, and the output is the generated response.

[0703] Step 6:

[0704] The server selects visual content related to the generated response from the database and prepares the data to be sent to the user's display device. Specifically, this involves selecting an image of a sparrow. The inputs are the generated response data and data about the user's visual environment, and the output is the data prepared for transmission.

[0705] Step 7:

[0706] The device analyzes data received from the server and displays appropriate visual information on the television or smartphone. Specifically, an image of a sparrow is displayed on the television. The input is data transmitted from the server, and the output is visual information from the user's perspective.

[0707] Step 8:

[0708] Based on the information presented, the user can ask additional questions via voice. This new voice input is then taken into the device, and the process restarts. The user's feedback is used for subsequent learning, improving the accuracy of the generative AI model. The input is the user's new question, and the output is the feedback data.

[0709] (Application Example 1)

[0710] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] In today's world, there is a need for information delivery systems that effectively support children's curiosity and educational needs. However, conventional systems suffer from insufficient accuracy in voice recognition, inadequate content selection, and inadequate utilization of user feedback, hindering natural communication between parents and children. Furthermore, they are unable to adapt to children's dynamically changing interests, making it difficult to provide consistent educational support.

[0712] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0713] In this invention, the server includes terminal means capable of recognizing voice in real time and converting it into digital data; information processing means that generate appropriate dialogue using voice data analyzed based on the user's characteristics and interests; communication means that transmit the generated dialogue and related visual content to the user's information communication terminal or video device; and media selection means that select and display relevant information based on behavioral predictions. This makes it possible to effectively respond to children's dynamic interests, promote natural communication between parents and children, and appropriately provide educational information.

[0714] A "terminal device" is a device that has the function of recognizing voice in real time and converting it into digital data.

[0715] "Information processing means" refers to a processor that analyzes voice data based on the user's characteristics and interests and generates appropriate dialogue.

[0716] "Communication means" refers to a system that has the function of transmitting generated dialogue and related visual content to an information and communication terminal or video device.

[0717] An "intelligent learning tool" is an algorithm with a self-learning function that collects user feedback data and incorporates it into the generation of subsequent dialogues.

[0718] "Media selection means" refers to technology that has the ability to select and display relevant information based on behavioral predictions.

[0719] This invention is implemented as a system that provides interactive educational content tailored to the characteristics of the user. The system includes the following main elements:

[0720] First, a "terminal device" is used for the user to perform voice input. Specifically, portable information terminals such as smartphones and tablets are used. These devices recognize speech in real time and convert the speech into digital data using the Google Speech-to-Text API or similar.

[0721] Subsequently, a server, acting as an "information processing tool," receives the converted audio data. This server analyzes the audio data using a large-scale language model. In particular, it utilizes natural language processing techniques to generate appropriate dialogue and related media content based on the user's interests and characteristics. OpenAI's natural language processing model, among others, is employed.

[0722] The generated content is streamed to the user's mobile device or video device, such as a television, via the cloud infrastructure, which serves as the "means of communication." In particular, AWS (Amazon Web Services) is used to ensure high-speed and stable content delivery.

[0723] As an "intelligent learning tool," user feedback data is stored on the server and reflected in the generation of subsequent dialogues. Using this feedback data, the AI ​​model continuously learns, providing a more personalized educational experience. In this way, the system evolves along with the user's growth.

[0724] For example, if a user asks "Tell me about dinosaurs," the server will automatically generate video content explaining the history and ecology of dinosaurs based on this request and send it to the device. Using the "Generating AI Model, Prompt Text," a prompt like the following is formed.

[0725] Example of a prompt:

[0726] "As educational content for children, please display visual information about 'dinosaurs' based on the voice recognition results."

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] The user asks a question by voice into a device such as a smartphone or tablet. This voice input is captured by the device. The device then performs noise reduction and converts the voice data into text using the Google Speech-to-Text API. Through this process, the device outputs the voice input as digital text data.

[0730] Step 2:

[0731] The terminal sends the converted text data to the server. The server receives this data and analyzes its content using a natural language processing engine. Specifically, it utilizes OpenAI's language model to understand the context of the text data and generate prompts based on the user's age and interests. In this step, the server outputs the prompt text and related topic information.

[0732] Step 3:

[0733] The server searches for and identifies relevant educational content based on prompts analyzed by a generative AI model. This involves using a database on AWS to select the appropriate visual and audio content. The output of this operation is media content that matches the user's interests.

[0734] Step 4:

[0735] The server transmits the selected content to the user's terminal via a communication method. Here, an encoding process takes place, and the content is delivered in streaming format. The user's terminal receives this content and displays it in real time. This allows the user to visually experience interactive information.

[0736] Step 5:

[0737] Finally, user feedback is collected on the device and sent to the server. The server uses the collected feedback to apply intelligent learning algorithms and optimize the system's response for future use. This process allows the system to continuously improve and provide more personalized educational services.

[0738] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0739] This invention provides a system that offers more appropriate and personalized responses in natural communication using the user's voice by utilizing emotion recognition technology. This system is designed to improve the quality of interaction between children and parents.

[0740] In this system, the user first speaks to the smart speaker. For example, suppose a child says, "Today was fun." This voice is recorded in real time by the device, and noise reduction and pre-processing of the audio signal are performed. The device then sends the audio data to a server via the network, where voice recognition and sentiment analysis are performed simultaneously.

[0741] The server uses an emotion recognition engine to analyze speech and identify the user's emotions. Based on this emotion data, the server generates appropriate conversation content and related video content. For example, if a child is enjoying themselves, it can suggest fun animations or quizzes.

[0742] The generated conversations and content are sent from the server to the user's television or mobile device. The device receives this information and provides a more impactful experience for children by displaying visual images and audio.

[0743] Furthermore, user feedback and new questions are collected again by the device and sent to the server. The server uses a learning algorithm based on this data to reflect in future conversation generation and content suggestions. Sentimental information, in particular, is an important indicator in this learning process, giving the system the flexibility to adapt to the user's situation.

[0744] In addition, this system also manages the user's schedule, streamlining daily life management by providing appropriate reminders and schedule adjustments based on the user's emotional state.

[0745] As described above, the present invention utilizes emotion recognition to provide users with a more personalized experience, thereby improving parent-child communication and educational outcomes.

[0746] The following describes the processing flow.

[0747] Step 1:

[0748] The device records the user's speech in real time and performs noise reduction and preprocessing of the audio signal. This prepares the audio data for analysis.

[0749] Step 2:

[0750] The terminal sends pre-processed audio data to the server. The server prepares the received audio data for analysis.

[0751] Step 3:

[0752] The server uses a speech recognition engine to convert speech data into text. Furthermore, it uses an emotion recognition engine to analyze the user's emotions and obtain emotion data.

[0753] Step 4:

[0754] The server generates appropriate conversation content based on analyzed text and sentiment data. It selects responses based on the user's age, interests, and emotions.

[0755] Step 5:

[0756] The server retrieves relevant video content from a database and selects content that matches the user's emotions. The selected information is then transmitted to the terminal and the user's display device.

[0757] Step 6:

[0758] The terminal prepares and displays the conversation and video information received from the server on a television or mobile device. This provides information to the user through both sight and sound.

[0759] Step 7:

[0760] The device records any new comments or questions submitted by the user and sends them to the next server.

[0761] Step 8:

[0762] The server collects user feedback and sentiment data, and uses machine learning algorithms to improve the model for future conversations and content suggestions.

[0763] (Example 2)

[0764] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] In modern society, where opportunities for communication are decreasing, there is a growing need to promote effective dialogue, particularly between parents and children, and to provide flexible responses tailored to each user's emotions. Furthermore, accurately understanding users' emotional states and providing lifestyle suggestions and information based on those understandings is desired to improve the efficiency and quality of their lives. However, current technology is insufficient for recognizing emotions and generating appropriate responses based on them, necessitating more advanced systems.

[0766] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0767] In this invention, the server includes processing means for acquiring audio in real time, performing noise reduction and preprocessing, and then converting it into a digital signal; generation means for analyzing the user's emotional state and dynamically generating corresponding dialogue content and visual information; and response means for enhancing the user's emotion-based response using a generation AI model. This makes it possible to accurately recognize the user's emotions and provide personalized, interactive responses in real time based on those emotions.

[0768] A "processing device" is a device that acquires the user's voice and converts it into a digital signal while removing noise.

[0769] A "generation device" is a device that analyzes the user's emotional state and dynamically generates appropriate dialogue content and visual information based on that information.

[0770] A "response mechanism" is a device that uses a generative AI model to provide responses tailored to the user's emotions and plays a role in enhancing those responses.

[0771] A "visual display means" is a device that has the function of transmitting generated dialogue and visual information to the user's display device and playing it back.

[0772] An "information learning device" is a device that collects responses and feedback from users and incorporates that information to improve the performance of the system.

[0773] A "schedule management device" is a device that adjusts information such as schedules according to the user's emotions, thereby streamlining daily management.

[0774] An "information presentation device" is a device that has the function of selecting and displaying relevant visual information based on the user's request.

[0775] This invention is a system that utilizes emotion recognition technology to provide users with personalized conversational experiences. The system primarily aims to improve user interaction and functions by combining real-time processing, emotion analysis, and response generation.

[0776] First, the user provides voice input via a smart speaker. This voice is acquired by the device and subjected to noise reduction and pre-processing. The device relies on hardware for this processing, for example, using a high-performance DSP (Digital Signal Processing) chip to perform it efficiently. The processed voice data is then sent to a server.

[0777] The server uses speech recognition software to convert speech data into text and an emotion recognition engine to analyze the user's emotions. The technologies used include, for example, machine learning algorithms and neural networks. Based on the analysis results, the server utilizes a generative AI model to dynamically generate appropriate responses and visual information.

[0778] The generated content is sent from the server to the user's display device. For example, if the emotion is determined to be "enjoyment," the device will display a fun animation on the user's TV or mobile device. This allows the user to enjoy an interactive experience with the system.

[0779] As a further interaction, user feedback is collected on the device and sent to the server. The server then applies a learning algorithm based on this information and incorporates it to improve the system's response. Through this process, the system can continuously learn and evolve.

[0780] Furthermore, to support users' daily lives, the server provides a schedule management function that can adjust appointments and generate reminders based on their emotions. For example, if it determines that a user is feeling stressed, it can suggest activities to help them relax.

[0781] A concrete example of a prompt could be something like, "Animation ideas to suggest when the user is enjoying themselves." This allows the system to generate responses and content that are best suited to the user's situation and emotions.

[0782] The above describes specific embodiments for carrying out the present invention.

[0783] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0784] Step 1:

[0785] Users speak to the smart speaker, providing their emotions and thoughts to the system as voice input. The input voice is captured by the device as raw sound data.

[0786] Step 2:

[0787] The terminal performs noise reduction and audio signal preprocessing on the acquired raw sound data. Specifically, it uses digital signal processing technology to perform noise filtering and generate clear audio data. This results in the output of high-quality audio data to improve speech recognition accuracy.

[0788] Step 3:

[0789] The terminal sends pre-processed audio data to the server via a secure network. The server receives the audio data and prepares for the next processing step.

[0790] Step 4:

[0791] The server uses speech recognition software to convert speech data into text data. During this process, the speech signal is broken down into phonemes through a feature extraction process and output as text. In parallel, an emotion recognition engine is used to analyze the user's emotional state from the text data. The output of this analysis is emotion data.

[0792] Step 5:

[0793] The server utilizes a generative AI model, using emotion data and text data from the user as prompts to dynamically generate appropriate dialogue and visual content. For example, if the user is in a "fun" state, an AI model for generating fun animations is activated and the corresponding content data is output.

[0794] Step 6:

[0795] The server sends the generated conversation content and other materials to the user's device. The destination is the user's television or mobile device, and the server also performs format conversion according to the characteristics of the connected device.

[0796] Step 7:

[0797] The device displays or plays received content for the user. This includes functions such as displaying video on the screen and playing audio through the speaker. Through this, the user can experience rich interaction with the system.

[0798] Step 8:

[0799] Users can provide additional feedback and questions about the content they are given. This input becomes the data needed to create new conversational flows.

[0800] Step 9:

[0801] The device collects user feedback and sends it to the server. The server analyzes this data using a learning algorithm to improve the accuracy of responses in future dialogue generation processes. User sentiment information and feedback drive the system's evolution through repeated learning.

[0802] Step 10:

[0803] The server utilizes emotional information to manage the user's schedule and provide lifestyle suggestions optimized for their emotional state. For example, when relaxation is needed, it suggests content with relaxing effects. This makes it possible to improve the quality of the user's daily life.

[0804] (Application Example 2)

[0805] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0806] In today's world, enriching parent-child communication and educational interaction requires individualized support tailored to each child's emotions. However, existing technologies lack the means to recognize a child's emotions in real time and provide appropriate content and dialogue accordingly. As a result, support that fits children's interests and emotions is not provided, leading to insufficient learning effectiveness and emotional satisfaction.

[0807] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0808] In this invention, the server includes an information terminal means capable of recognizing voice in real time and converting it into digital information, an information processing means that generates appropriate dialogue using voice information analyzed based on the user's emotional state, and a communication means that transmits the generated dialogue and related visual content to the user's mobile terminal or video display device. This makes it possible to provide appropriate dialogue and content that responds to a child's emotions.

[0809] An "information terminal device" is a device that recognizes voice in real time and converts it into digital information.

[0810] "Information processing means" refers to a device or system that generates appropriate dialogue using voice information analyzed based on the user's emotional state.

[0811] "Communication means" refers to a device or system for transmitting the generated dialogue and associated visual content to a user's mobile terminal or video display device.

[0812] A "learning processing means" is a device or system for collecting user emotional information and reflecting it in the generation of subsequent dialogues.

[0813] "Information presentation means" refers to a device or system for acquiring and presenting content appropriately according to the user's emotions.

[0814] "Media presentation means" refers to a device or system for selecting and presenting relevant content based on the user's emotion recognition results.

[0815] The system implementing this invention is built around an "information terminal means" that recognizes speech in real time and converts it into digital information. Specifically, a mobile information terminal such as a smartphone or tablet is used. When the terminal acquires speech data, it performs real-time noise reduction preprocessing and then transmits the speech data to a server via the network.

[0816] The server uses "information processing means" to analyze the user's emotional state from the audio data. For emotion recognition, advanced AI models are used, such as Google Cloud's Natural Language API. This API is used to analyze emotions and generate appropriate dialogue. Furthermore, based on the results, relevant visual content is appropriately selected via "communication means" and transmitted to the user's mobile device or video display device.

[0817] During the learning process, the server uses a "learning processing method" to continuously collect feedback from users and improve the system's accuracy by reflecting this feedback in subsequent interactions and content generation.

[0818] For example, if a child user speaks to the information terminal and the system recognizes that they are having fun, the server activates an "emotion-responsive content curator" and suggests fun animations or quizzes. This allows parents to confidently provide their children with appropriate, educational, and emotionally resonant support.

[0819] Furthermore, in order to provide diverse content based on the user's emotions through the "information presentation means," a generative AI model is used to utilize prompt phrases such as, "Please tell me how to recognize emotions from a child's voice and suggest the most suitable learning content based on that," thereby enabling the provision of optimal content.

[0820] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0821] Step 1:

[0822] The device records the child's voice in real time. The input is raw audio data, which is pre-processed to remove noise. The output is clear audio data with the noise removed.

[0823] Step 2:

[0824] The terminal sends pre-processed audio data to the server via the network. The input is clear audio data, which is sent directly to the server as output. The specific operation for network communication utilizes an internet connection.

[0825] Step 3:

[0826] The server analyzes the transmitted audio data and uses an emotion recognition engine to identify the user's emotional state. The input is audio data, and the output is the analyzed emotion information. Here, emotion analysis is performed using Google Cloud's Natural Language API, among others.

[0827] Step 4:

[0828] The server generates appropriate dialogue and visual content based on emotional information. The input is emotional information, and an AI model is used to select relevant content. The output includes the generated dialogue text and content identifiers.

[0829] Step 5:

[0830] The server sends content to the terminal. The input is the generated dialogue text and content identifier, which are then transmitted to the terminal as output. Specifically, the data is packetized and transmitted using a communication protocol.

[0831] Step 6:

[0832] The terminal displays or plays received dialogue text and content to the user. Input is data from the server, and output is visualized dialogue and visual content. Specific actions include displaying this content on the application screen.

[0833] Step 7:

[0834] The user reacts to the system's presentation, and the device collects these reactions as data. Input consists of user actions and feedback, while output is feedback data used to generate future dialogues. Taps on the UI and voice input are recorded for this data collection.

[0835] Step 8:

[0836] The server stores the collected feedback data and uses it as learning material for future interactions and content generation. The input is feedback data, and the output is an updated learning model or stored data. To apply a generative AI model to this learning process, prompts such as "Please tell me how to recognize emotions from a child's voice and suggest the most appropriate learning content based on that" are used.

[0837] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0838] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0839] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0840] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0841] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0842] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0843] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0844] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0845] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0846] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0847] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0848] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0849] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0850] 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.

[0851] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0852] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0853] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0854] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0855] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0856] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0857] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0858] The following is further disclosed regarding the embodiments described above.

[0859] (Claim 1)

[0860] A terminal device capable of recognizing voice in real time and converting it into digital data,

[0861] A server means that generates appropriate conversations using voice data analyzed based on the user's age and interests,

[0862] A transmission means for transmitting the generated conversation and related video content to the user's mobile device or television,

[0863] A learning method that collects user feedback and incorporates it into future conversation generation,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising a schedule management means for managing a user's schedule and generating reminders as appropriate.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising media display means capable of selecting and displaying related video content according to user requests.

[0869] "Example 1"

[0870] (Claim 1)

[0871] A device capable of recognizing speech in real time and pre-processing it using signal processing technology,

[0872] A communication means that converts pre-processed audio into digital data and transmits it over a network,

[0873] An information processing means that analyzes received audio data using a generation AI model and generates appropriate information based on context and emotion,

[0874] A transmission means for selecting generated information and related visual content and transmitting it to a display device,

[0875] A learning method that collects user feedback and incorporates it into future information generation,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, further comprising management means for tracking user activity and generating timely notifications.

[0879] (Claim 3)

[0880] The system according to claim 1, further comprising a display method capable of selecting and displaying relevant visual content according to the user's request.

[0881] "Application Example 1"

[0882] (Claim 1)

[0883] A terminal device capable of recognizing voice in real time and converting it into digital data,

[0884] An information processing means that generates appropriate dialogue using voice data analyzed based on user characteristics and interests,

[0885] A communication means for transmitting the generated dialogue and related visual content to the user's information and communication terminal or video device,

[0886] An intelligent learning method that collects user feedback data and reflects it in the generation of subsequent dialogues,

[0887] A media selection means that selects and displays relevant information based on behavioral predictions,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising a time management means for managing time in accordance with user activity and generating notifications as appropriate.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising video display means capable of selecting and displaying interactive educational content according to the user's interests.

[0893] "Example 2 of combining an emotion engine"

[0894] (Claim 1)

[0895] A processing means for acquiring audio in real time, performing noise reduction and preprocessing, and then converting it into a digital signal,

[0896] A generation means that analyzes the user's emotional state and dynamically generates corresponding dialogue content and visual information,

[0897] A response method that enhances user emotion-based responses using a generative AI model,

[0898] A visual display means that transmits and plays back the generated dialogue and visual information to the user's display device,

[0899] An information learning method that collects user responses and incorporates them to improve system performance,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, further comprising a schedule management means for generating schedule information adjusted according to the user's emotions and for streamlining daily management.

[0903] (Claim 3)

[0904] The system according to claim 1, further comprising information presentation means capable of dynamically selecting and displaying relevant visual information based on user requests.

[0905] "Application example 2 when combining with an emotional engine"

[0906] (Claim 1)

[0907] An information terminal capable of recognizing voice in real time and converting it into digital information,

[0908] An information processing means that generates appropriate dialogue using voice information analyzed based on the user's emotional state,

[0909] A communication means for transmitting the generated dialogue and related visual content to the user's mobile device or video display device,

[0910] A learning processing method that collects user emotion information and reflects it in generating subsequent dialogues,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, further comprising an information presentation means for appropriately acquiring and presenting content in accordance with the user's emotions.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising media presentation means capable of selecting and presenting relevant content based on the user's emotion recognition results. [Explanation of symbols]

[0916] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal device capable of recognizing voice in real time and converting it into digital data, A server means that generates appropriate conversations using voice data analyzed based on the user's age and interests, A transmission means for transmitting the generated conversation and related video content to the user's mobile device or television, A learning method that collects user feedback and incorporates it into future conversation generation, A system that includes this.

2. The system according to claim 1, further comprising a schedule management means for managing the user's schedule and generating reminders as appropriate.

3. The system according to claim 1, further comprising media display means capable of selecting and displaying related video content according to the user's request.

Images