system

Abstract

We provide the system. [Solution] Information and communication equipment that collects user voice data and text data, A data processing device that organizes and stores collected data for each individual user, and performs noise reduction and standardization. An AI generation device that uses organized and stored data to model user-specific features by combining natural language processing and machine learning, A response generation device that generates an appropriate response using an AI model generated based on user input, A response provider that provides the generated response to the user, 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 method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 society, the number of elderly people living alone is increasing, and the loneliness and sadness of losing a loved one in particular have become serious problems. Conventional technologies have lacked means to sufficiently relieve such psychological burdens faced by people. Furthermore, it has been difficult to provide an AI with a communication style unique to a user, and there have been limitations in providing individual support. This invention aims to overcome these problems and reduce loneliness and provide a sense of security through an AI that behaves like a human.

Means for Solving the Problems

[0005] This invention utilizes information and communication equipment that collects users' everyday voice and text data in real time. This data is organized and stored by a data processing device, and then subjected to noise reduction and standardization. Subsequently, an AI generation device combining natural language processing and machine learning learns the characteristics of each user based on the collected data and generates an individual AI model. This AI model is utilized by a response generation device that generates appropriate responses in response to user input. Finally, a response provider device provides the generated response to the user, enabling the user to communicate with a sense of security. Furthermore, continuous updates to the AI ​​model ensure that the response is always up-to-date.

[0006] "Information and communication equipment" is a general term for terminal devices used to collect users' everyday voice and text data.

[0007] A "data processing device" is a device that organizes and stores collected audio and text data, and performs noise reduction and standardization on it.

[0008] An "AI generation device" is a device that uses natural language processing and machine learning to learn user-specific characteristics from data formatted by a data processing device and generate an AI model.

[0009] A "response generation device" is a device that uses a generated AI model to produce an appropriate response to user input.

[0010] A "response provider" is a device that has a mechanism to provide the user with a response generated by a response generator. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The system of this invention is implemented using information and communication devices that users use on a daily basis, namely smartphones and smart speakers. The user terminal has the function of recording daily conversations and messages in voice and text and sending that data to a server. The transmitted data is denoised and standardized by a data processing device on the server, and then efficiently stored and managed.

[0033] The server then uses the collected data to generate user-specific AI models using an AI generator. This involves data-driven feature extraction, and natural language processing and machine learning techniques are used to mimic the user's unique communication style and emotions.

[0034] For example, if a user says "The weather's nice today" through their smartphone, the device converts the audio into text and sends it to the server. Based on this data, the server refers to the user's favorite activities and topics from past data and generates a friendly response, such as "You often went to the park on nice days, didn't you? Do you have plans to go today?"

[0035] The response generation device creates an appropriate response to user input, taking into account the user's past actions and conversation history, and presents this response to the user via the response provision device. This system continuously updates its interaction with the user, enabling the AI ​​to behave in a way that allows the user to enjoy a friendly conversation with a deceased family member.

[0036] Therefore, users can utilize the system to receive psychological support, such as alleviating loneliness and providing a sense of security. This invention can be offered as a total communication solution when combined with a communication plan.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user terminal receives voice commands and text messages and records them as digital data. This data has the function of recording the user's daily conversations and messages along with a timestamp.

[0040] Step 2:

[0041] The device periodically or upon a specified trigger sends the collected data to the server. The data is encrypted and transmitted through a secure channel to ensure its safety.

[0042] Step 3:

[0043] The server stores the received data in a database and organizes it, associating it with each user ID. Here, the data is formatted, denoised, and formatted as needed.

[0044] Step 4:

[0045] The server uses the organized data to train a user-specific AI model using an AI generator. This process employs natural language processing algorithms to learn the user's language patterns and characteristics.

[0046] Step 5:

[0047] When a user makes a new voice or text input through the device, the device immediately issues a command to send that data to the server.

[0048] Step 6:

[0049] The server uses an AI model to generate an appropriate response based on the most recent input received. This includes contextual analysis and sentiment recognition.

[0050] Step 7:

[0051] The generated response is sent to the user terminal via the response provider. The terminal either plays the response aloud using speech synthesis technology or displays it as text on the screen.

[0052] Step 8:

[0053] The user receives a response and continues the conversation. This feedback loop allows the system to continuously update user data and evolve the AI ​​model.

[0054] (Example 1)

[0055] 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."

[0056] In modern information and communication devices, generating personalized responses that accurately reflect the individual characteristics of users is challenging. Furthermore, data noise reduction and standardization, as well as dynamic updates of user profiles, are required to improve the accuracy of user input data and enhance response quality. Providing systems that efficiently perform these processes in a cost-effective manner is also a crucial challenge.

[0057] 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.

[0058] In this invention, the server includes means for denoising and standardizing acquired data, AI generation means for generating a generative AI model tailored to individual users by combining natural language processing and machine learning techniques based on the standardized data, and means for dynamically updating the generative AI model to adapt to the user's recent behavior and preferences. This makes it possible to appropriately generate responses that reflect the individual characteristics of the user and provide high-quality responses to user input in real time.

[0059] A "user" is an entity that uses information and communication equipment to generate voice data or text data.

[0060] "Terminal means" refers to a device that has the function of acquiring voice data and text data from the user and transmitting it to a server.

[0061] A "server device" is a device that receives data transmitted from a terminal device and performs noise reduction and standardization processing on it.

[0062] "Noise reduction" is the process of removing unnecessary information from data to make it suitable for analysis.

[0063] "Standardization" refers to organizing data into a consistent format so that it can be processed uniformly.

[0064] An "AI generation method" is a device that uses natural language processing and machine learning technologies to model user-specific characteristics.

[0065] A "generative AI model" is an algorithmic model that mimics a user's communication style and preferences to generate responses.

[0066] A "response generation means" is a device that manages the process of forming an appropriate response to user input based on a generated AI model.

[0067] "Response provision means" refers to a device or function for transmitting the generated response to the user.

[0068] "Dynamic updating" refers to the process of adapting and modifying the generated AI model in real time or periodically based on the user's latest behavior and preferences.

[0069] "Information and communication equipment" refers to electronic devices used to collect user data and provide responses.

[0070] A "communications lease agreement" refers to a fee structure for the use of information and communication equipment, and is a contractual form related to the business model for which users utilize this system.

[0071] The system of this invention consists of the interaction of a terminal, a server, and a user, and provides the user with a personalized communication experience.

[0072] First, the user gives voice commands using a device such as a smartphone or smart speaker. The device then uses speech recognition technology to convert the voice data received from the user into text data. For example, common industry technologies for speech recognition software include Google® Speech-to-Text and other speech recognition libraries.

[0073] Next, the terminal sends the generated text data to the server. The server then performs noise reduction and standardization on the received data. This utilizes speech processing algorithms to ensure the reliability and consistency of the data.

[0074] Furthermore, the server uses natural language processing and machine learning techniques to build a generative AI model with user-specific characteristics. In this process, the server learns important patterns in the data and builds an AI to generate responses that mimic the user's communication style.

[0075] For example, if a user asks, "What should I do today?", the system will refer to their past activity history and generate a response that offers a personalized suggestion, such as, "How about going for a walk today?"

[0076] Examples of prompts in this system include instructions such as, "Generate a response that provides suggestions appropriate to the current situation based on the user's past history."

[0077] Finally, the results generated by the response generation means are presented to the user via the response provision means. This allows the user to enhance the usefulness and relevance of the information received from the system.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user performs voice input using a device such as a smartphone or smart speaker. For example, the user might say, "What's the weather like tomorrow?" This voice data is captured by the device. The input is voice data, and the output is text data. Speech recognition technology is used for this conversion; the speech recognition software on the device converts the voice data into text.

[0081] Step 2:

[0082] The terminal sends the converted text data to the server. Specifically, the text data is transmitted to the server via a secure communication protocol. The input is text data, and the output is raw text data stored on the server.

[0083] Step 3:

[0084] The server performs noise reduction and standardization on the received text data. Specifically, it removes unnecessary symbols and mistranslated words from the text data and prepares it for analysis. The input is raw text data, and the output is processed text data.

[0085] Step 4:

[0086] The server extracts features for a generative AI model based on processed text data. Specifically, it uses natural language processing techniques to extract important keywords and phrases from the data. The input is processed text data, and the output is the feature-extracted data.

[0087] Step 5:

[0088] The server uses the extracted features to build a user-specific generative AI model. Specifically, it uses machine learning algorithms to learn the user's past data patterns and optimize the model. The input is the feature-extracted data, and the output is the generative AI model.

[0089] Step 6:

[0090] The server utilizes a generative AI model to generate responses based on user input. Specifically, it forms responses based on prompts and references past user behavior. The input consists of the user's new input and the generative AI model, while the output is the generated response.

[0091] Step 7:

[0092] The server sends the generated response to the terminal, which then presents it to the user. Specifically, the terminal uses speech synthesis technology to present the generated text to the user as audio. The input is the generated response, and the output is the audio output to the user.

[0093] (Application Example 1)

[0094] 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."

[0095] In today's information and communication environment, users seek greater satisfaction and psychological support by receiving personalized information and content. However, there is a lack of means to provide content appropriately customized based on users' individual hobbies and interests. As a result, users are burdened with the task of selecting information that matches their interests from a vast amount of data.

[0096] 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.

[0097] In this invention, the server includes an information and communication device for collecting user voice and text data, a data processing device for organizing and storing the collected data for each individual user, performing noise reduction and standardization, and an AI generation device that uses the organized and stored data to model user-specific characteristics by combining natural language processing and machine learning. This makes it possible to generate stories based on the user's interests and themes and provide a personalized content experience.

[0098] An "information and communication device" is a device used to collect user voice data and text data.

[0099] A "data processing device" is a device that organizes and stores collected data for each individual user, and performs noise reduction and standardization.

[0100] An "AI generation device" is a device that uses organized and stored data to combine natural language processing and machine learning to model user-specific characteristics.

[0101] A "response generation device" is a device that generates an appropriate response using an AI model that is generated based on input from the user.

[0102] A "response provider" is a device that provides the generated response to the user.

[0103] A "story generation device" is a device that generates stories based on the user's interests and themes.

[0104] A "content distribution device" is a device that delivers generated stories to users.

[0105] The system implementing this invention collects and processes user voice and text data, and generates and provides a user-specific story. The system receives voice input from a smartphone or smart speaker as an information and communication device, and a data processing device organizes the data, performs noise reduction and standardization. The organized data is then used by an AI generation device to create a generative AI model that models the user's unique characteristics using natural language processing and machine learning techniques.

[0106] The system's response generator interprets the current user input based on an AI model and uses a story generator to create a story tailored to the user's interests and themes. This story is then delivered to the user through a content distribution device.

[0107] Specifically, the speech_recognition library is used for voice data conversion and noise reduction, and the GPT-2 model included in the transformers library is used as the natural language generation model for story generation. This enables an interactive experience where users can listen to stories that match their interests based on their voice input.

[0108] For example, if a user says something along the lines of "I want to relax today," a story will be generated based on that history, creating a narrative that is ideal for relaxation. A concrete example of an input prompt to the generation AI model might be text like, "Please create a relaxing story." The story generated based on this prompt will then be delivered as content that resonates with the user's emotional needs.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device recognizes the user's voice and collects the audio data. Here, the device uses a microphone to acquire the voice and converts it into text data using speech recognition software (e.g., the speech_recognition library). The input is audio data, and the output is text data.

[0112] Step 2:

[0113] The server receives text data, and the data processing unit performs noise reduction and standardization. This removes unnecessary information from the text data and arranges it into a unified format. The input is the converted text data, and the output is the noise-reduced and standardized text.

[0114] Step 3:

[0115] The server inputs the formatted text data into the AI ​​generator and uses machine learning to model user-specific characteristics. Here, natural language processing techniques are used to learn the user's past conversation history and preferences to create a generative AI model. The input is standardized text data, and the output is a user-specific generative AI model.

[0116] Step 4:

[0117] The server uses a story generation device based on a generative AI model to generate stories that match the user's interests. The generation prompt uses a "generative AI model, prompt sentence" to generate stories that reflect the latest user interests and themes. The input is a user-specific generative AI model, and the output is the generated story.

[0118] Step 5:

[0119] The server provides the generated story to the user via a content distribution device. The story is delivered to the terminal in audio or text format, allowing the user to access and enjoy it. The input is the generated story, and the output is the content presented to the user.

[0120] 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.

[0121] The system of this invention is implemented on information and communication devices, such as smartphones and smart speakers, for capturing users' daily communications. The user terminal has the function of collecting voice data and text data in real time and sending it to a server periodically.

[0122] The server organizes and stores the transmitted data using a data processing device, performing noise removal and standardization during this process. After this processing, the data is used by an AI generation device to generate an AI model that reflects the characteristics of each user. The AI ​​model utilizes natural language processing and machine learning technologies to deeply learn the patterns and characteristics of the user's conversation, thereby improving the accuracy of personalized responses.

[0123] Furthermore, this system incorporates an emotion engine that analyzes the user's voice tone and word choice to generate emotional information. This emotional information is then used by the response generation device to derive an appropriate response that is in line with the user's emotional state.

[0124] For example, if a user says to a smart speaker, "I'm really tired today," the device sends this voice as data to a server. The server analyzes the tone of the voice and generates emotional information related to the word "tired." If the emotion engine detects "fatigue," an encouraging response such as "You're always working so hard. Please get some rest" is created by the response generator and finally provided to the user as voice by the response provider.

[0125] In this way, the system enables not only simple response generation through interaction with the user, but also interaction that is sensitive to the user's emotions. The emotion engine, by being reflected in the AI ​​model, improves the quality of the dialogue that takes the user's emotions into account. This invention is expected to be implemented on a wider scale when combined with communication plans.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user terminal receives user voice input and text messages in real time and records them as digital data. This data is stored in a format that includes a timestamp of the user's statements and a device ID.

[0129] Step 2:

[0130] The terminal processes the recorded data in batches at regular intervals and sends it to the server. The transmission uses encryption protocols to ensure data confidentiality while enabling efficient transfer.

[0131] Step 3:

[0132] The server stores the received audio and text data in a database and organizes it by user ID. The data is also denoised and standardized to facilitate subsequent processing.

[0133] Step 4:

[0134] The data processing unit inputs the organized data into an emotion engine and analyzes the user's emotions based on voice tone and word choice. The analysis results are output as emotional information such as "happiness," "fatigue," and "anger."

[0135] Step 5:

[0136] The server's AI generator receives output from the emotion engine and updates the AI ​​model to take emotional information into account. This results in a model that reflects the user's unique characteristics.

[0137] Step 6:

[0138] When a user provides new input, the device sends the data at that moment to the server. This input can be in the form of speech or text.

[0139] Step 7:

[0140] The server processes the latest user input using an AI model and generates responses that reflect emotional information. The response generator produces context-adaptive responses, taking into account the user's past history and emotional state.

[0141] Step 8:

[0142] The generated response is delivered to the user terminal by the response provider, and the terminal either plays it back using speech synthesis technology or displays it as text.

[0143] Step 9:

[0144] The user receives responses through the device and can proceed with the next dialogue. The system continuously provides dialogue that reflects the user's emotions by repeatedly engaging in interaction.

[0145] (Example 2)

[0146] 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".

[0147] In today's information and communication environment, understanding user-specific characteristics and providing appropriate responses tailored to their emotions is crucial for achieving high levels of satisfaction that meet individual needs. However, existing systems struggle to accurately analyze user emotions and provide responses that adapt to dynamically changing emotions and preferences, hindering improvements in the user experience. Solving this challenge is essential.

[0148] 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.

[0149] In this invention, the server includes means for collecting user communication content, processing equipment for individually organizing and processing the collected information, removing unwanted noise and applying standardization, generating equipment for generating a model that combines natural language processing and machine learning techniques based on the organized information, and a device that analyzes the user's emotions during communication and generates a selective response adapted to those emotions. This enables highly accurate responses tailored to the user's individual emotions and characteristics, significantly improving the user experience.

[0150] "User communication content" refers to information such as voice and text transmitted by users through information and communication devices.

[0151] A "collection device" refers to a device equipped with hardware and software functions for acquiring and storing user communication content.

[0152] "Processing equipment" refers to devices and solutions used to organize collected information, remove unnecessary elements, and standardize it.

[0153] A "generation device" refers to a device that utilizes organized information to create user-specific models using natural language processing and machine learning techniques.

[0154] "Analyzing emotions" means identifying and classifying the user's emotional state from the content of their communications.

[0155] A "device with the function of generating a selected response" refers to a device that has the ability to create and provide an optimal response based on analyzed information.

[0156] This invention is implemented through a system combining an information and communication device and a server. Specifically, terminals capable of voice and text input are used to capture the user's daily communications. These terminals include smartphones and smart speakers, and they collect the content of the user's communications in real time.

[0157] The terminal sends the collected audio and text data to the server. On the server, the information is first organized using data processing equipment, noise is removed, and then standardization is performed. Data processing libraries such as Python's Pandas and NumPy are used for this process. The organized data is then analyzed by an AI generator to generate a user-specific natural language processing model. This learns the user's unique conversation patterns and contexts, and builds an individual model.

[0158] Furthermore, the server is equipped with an emotion analysis engine that analyzes the user's emotional state from the communication content. Based on the tone and speed of the voice data and the emotional expressions contained in the text, it infers the user's emotions and prepares a response based on those emotions. The response generation device takes this emotional information into consideration and provides the user with an appropriate response.

[0159] For example, if a user says to a smart speaker, "I'm feeling a little down today," the device acquires this voice as data and sends it to the server. The server uses an emotion engine to analyze the emotion of "feeling down," and an AI model generates a response such as, "Has something happened recently? I'm here to listen if there's anything you want to talk about." This response is then provided to the user through the device, enabling a conversation that is sensitive to their emotions.

[0160] An example of a prompt is when the user inputs "Have you had anything good happen recently?" into the system, and the system generates and provides an appropriate response. In this way, the present invention provides a system that enables sophisticated dialogue based on the user's emotional information.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The terminal collects user communication content in real time through a voice input device or text input. This collected data is obtained as a digital voice signal if it is voice data, or as character data if it is text data. In particular, if voice is input, it is converted into character data using speech recognition software. This is the input, and the output is obtained in the form of digital voice or text.

[0164] Step 2:

[0165] The terminal transmits collected voice and text data to the server via the network. During this process, the data is encrypted and transmitted using a secure protocol (such as HTTPS). This is the input, and the state where the data has safely reached the server is the output.

[0166] Step 3:

[0167] The server processes the received data using a data processing unit. The first steps are noise reduction and standardization. For audio data, background noise is removed, and only the clear audio signal is extracted. For text data, character encoding is standardized and unnecessary spaces are removed. This is the input, and the standardized, noise-free data is the output.

[0168] Step 4:

[0169] The server passes the organized data to the AI ​​generator, which then generates an AI model that captures the user's conversational characteristics. Using natural language processing techniques to analyze the data and machine learning (e.g., neural networks), it generates a user-specific communication model. The input data consists of organized text and audio features, and the output is a custom model for each user.

[0170] Step 5:

[0171] The server uses an AI model and an emotion analysis engine to analyze the user's emotional state and generates an appropriate response using a response generator. The analysis engine extracts emotional information based on voice tone and word choice, and this information is reflected in the AI ​​model. Based on this input emotional data, the response generator generates a response in text format. The output is a response sentence appropriate for the user.

[0172] Step 6:

[0173] The terminal receives the generated response and provides it to the user as either voice or text. For voice output, speech synthesis technology is used to convert the text into voice data, which is then played through the speaker. The input is the response data from the server, and the output is a natural-sounding response to the user.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] In modern information and communication technology, accurately analyzing user emotions and ensuring security is a crucial challenge. However, existing systems struggle to detect changes in user emotions or unnatural communication patterns in real time and to quickly warn of potential dangers or scams. To address this challenge, a system is needed that analyzes emotions and automatically detects abnormal communication.

[0177] 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.

[0178] In this invention, the server includes an emotion analysis engine, means for analyzing emotions from voice and text information and detecting abnormal emotions or unnatural communication patterns, security means for warning of potential dangers or fraud and notifying trusted contacts as necessary, and an AI generator for modeling consumer-specific characteristics. This makes it possible to ensure user safety and proactively detect potential dangers.

[0179] "Audio information" refers to data obtained from the voice spoken by the user, and is used to analyze the user's emotions and intentions.

[0180] "Textual information" refers to text data entered or submitted by users, which is then analyzed using natural language processing.

[0181] A "communication device" is a device used to collect voice and text information and transmit it to a server, and includes smartphones and smart speakers.

[0182] "Data processing means" refers to a system that organizes and stores collected information, and performs noise removal and standardization.

[0183] An "AI generation device" is a device that combines natural language understanding and machine learning to model consumer-specific characteristics and generate responses tailored to individual users.

[0184] A "response generation means" is a device that creates an appropriate response using a model generated by an AI generation device based on input from the user.

[0185] "Response provision means" refers to a device or system for providing the generated response to the user.

[0186] An "emotion analysis engine" is a program or device that analyzes a user's emotions from voice and text information and identifies their emotional state.

[0187] "Security measures" refer to systems that detect abnormal emotions or unnatural communication patterns, warn users of potential dangers, and notify trusted contacts.

[0188] This invention is a system that performs emotion analysis and detects abnormal communication patterns using user voice and text information. In its implementation, a smartphone or smart speaker is used as the communication device. These communication devices are responsible for collecting the user's voice and text information and transmitting it to the server.

[0189] The server has data processing capabilities to organize and store the received information, remove noise, and standardize it. Next, an AI generation device is used to model consumer-specific characteristics from the collected information and generate an appropriate response. The response generation means creates a user-optimized response based on this AI model and outputs it to the user via the response provision means.

[0190] The emotion analysis engine analyzes information to identify the user's emotional state. Security measures detect abnormal emotions and unnatural communication patterns, warning the user of potential dangers and notifying trusted contacts when necessary.

[0191] For example, if a user says "I'm anxious" to a smart speaker, the server analyzes the voice information, and the emotion analysis engine identifies "anxiety." Security measures include issuing a warning to the user and contacting registered family members if the server determines that this deviates from normal emotional patterns. A specific example of a prompt would be, "Please create prompts for an AI model to analyze the normal communication of elderly individuals and detect changes in their emotions."

[0192] Thus, in order to concretely implement the invention, a system is needed that analyzes the user's emotional state and takes appropriate action. This aims to ensure user safety and prevent potential dangers.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The terminal collects the user's voice and text information in real time. This information is transmitted to the server via a communication device. The terminal receives the input and converts the voice to text using speech recognition software. The output at this stage is the text information.

[0196] Step 2:

[0197] The server organizes and stores the acquired information using data processing tools, and performs noise removal and standardization. The input is textual information from the terminal, which is processed with data cleansing software to obtain a clean dataset. The output of this step is organized and standardized data.

[0198] Step 3:

[0199] The emotion analysis engine on the server analyzes the user's emotional state using organized, standardized data. Calculations are performed based on an emotion analysis algorithm, with a clean dataset as input. The output is metadata indicating the user's emotional state.

[0200] Step 4:

[0201] The server's AI generator models user-specific characteristics, including emotional state metadata, and uses the generated AI model to produce appropriate responses. Here, consumer-specific future predictions and conditioned reflex models are combined to design a response framework. The output of this step is a customized response message.

[0202] Step 5:

[0203] The response generation means sends a customized response message to the user through the response provision means. The output information from the server is returned to the terminal and provided to the user as voice or text via a smart speaker, smartphone, etc. The feedback to the user is the final output of this step.

[0204] Step 6:

[0205] Security measures detect abnormal patterns and potential dangers from analyzed emotional states. The input is emotional state metadata, and an anomaly detection algorithm makes rapid decisions. The output is a warning notification regarding potential dangers, which is sent to trusted contacts as needed.

[0206] By having each step work in coordination, a system is created that ensures user safety, improves the accuracy of individual responses, and manages potential risks.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] The system of this invention is implemented using information and communication devices that users use on a daily basis, namely smartphones and smart speakers. The user terminal has the function of recording daily conversations and messages in voice and text and sending that data to a server. The transmitted data is denoised and standardized by a data processing device on the server, and then efficiently stored and managed.

[0224] The server then uses the collected data to generate user-specific AI models using an AI generator. This involves data-driven feature extraction, and natural language processing and machine learning techniques are used to mimic the user's unique communication style and emotions.

[0225] For example, if a user says "The weather's nice today" through their smartphone, the device converts the audio into text and sends it to the server. Based on this data, the server refers to the user's favorite activities and topics from past data and generates a friendly response, such as "You often went to the park on nice days, didn't you? Do you have plans to go today?"

[0226] The response generation device creates an appropriate response to user input, taking into account the user's past actions and conversation history, and presents this response to the user via the response provision device. This system continuously updates its interaction with the user, enabling the AI ​​to behave in a way that allows the user to enjoy a friendly conversation with a deceased family member.

[0227] Therefore, users can utilize the system to receive psychological support, such as alleviating loneliness and providing a sense of security. This invention can be offered as a total communication solution when combined with a communication plan.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user terminal receives voice commands and text messages and records them as digital data. This data has the function of recording the user's daily conversations and messages along with a timestamp.

[0231] Step 2:

[0232] The device periodically or upon a specified trigger sends the collected data to the server. The data is encrypted and transmitted through a secure channel to ensure its safety.

[0233] Step 3:

[0234] The server stores the received data in a database and organizes it, associating it with each user ID. Here, the data is formatted, denoised, and formatted as needed.

[0235] Step 4:

[0236] The server uses the organized data to train a user-specific AI model using an AI generator. This process employs natural language processing algorithms to learn the user's language patterns and characteristics.

[0237] Step 5:

[0238] When a user makes a new voice or text input through the device, the device immediately issues a command to send that data to the server.

[0239] Step 6:

[0240] The server uses an AI model to generate an appropriate response based on the most recent input received. This includes contextual analysis and sentiment recognition.

[0241] Step 7:

[0242] The generated response is sent to the user terminal via the response provider. The terminal either plays the response aloud using speech synthesis technology or displays it as text on the screen.

[0243] Step 8:

[0244] The user receives a response and continues the conversation. This feedback loop allows the system to continuously update user data and evolve the AI ​​model.

[0245] (Example 1)

[0246] 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."

[0247] In modern information and communication devices, generating personalized responses that accurately reflect the individual characteristics of users is challenging. Furthermore, data noise reduction and standardization, as well as dynamic updates of user profiles, are required to improve the accuracy of user input data and enhance response quality. Providing systems that efficiently perform these processes in a cost-effective manner is also a crucial challenge.

[0248] 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.

[0249] In this invention, the server includes means for denoising and standardizing acquired data, AI generation means for generating a generative AI model tailored to individual users by combining natural language processing and machine learning techniques based on the standardized data, and means for dynamically updating the generative AI model to adapt to the user's recent behavior and preferences. This makes it possible to appropriately generate responses that reflect the individual characteristics of the user and provide high-quality responses to user input in real time.

[0250] A "user" is an entity that uses information and communication equipment to generate voice data or text data.

[0251] "Terminal means" refers to a device that has the function of acquiring voice data and text data from the user and transmitting it to a server.

[0252] A "server device" is a device that receives data transmitted from a terminal device and performs noise reduction and standardization processing on it.

[0253] "Noise reduction" is the process of removing unnecessary information from data to make it suitable for analysis.

[0254] "Standardization" refers to organizing data into a consistent format so that it can be processed uniformly.

[0255] An "AI generation method" is a device that uses natural language processing and machine learning technologies to model user-specific characteristics.

[0256] A "generative AI model" is an algorithmic model that mimics a user's communication style and preferences to generate responses.

[0257] A "response generation means" is a device that manages the process of forming an appropriate response to user input based on a generated AI model.

[0258] "Response provision means" refers to a device or function for transmitting the generated response to the user.

[0259] "Dynamic updating" refers to the process of adapting and modifying the generated AI model in real time or periodically based on the user's latest behavior and preferences.

[0260] "Information and communication equipment" refers to electronic devices used to collect user data and provide responses.

[0261] A "communications lease agreement" refers to a fee structure for the use of information and communication equipment, and is a contractual form related to the business model for which users utilize this system.

[0262] The system of this invention consists of the interaction of a terminal, a server, and a user, and provides the user with a personalized communication experience.

[0263] First, the user gives voice commands using a device such as a smartphone or smart speaker. The device then uses speech recognition technology to convert the voice data received from the user into text data. For example, common industry technologies for speech recognition software include Google Speech-to-Text and other speech recognition libraries.

[0264] Next, the terminal sends the generated text data to the server. The server then performs noise reduction and standardization on the received data. This utilizes speech processing algorithms to ensure the reliability and consistency of the data.

[0265] Furthermore, the server uses natural language processing and machine learning techniques to build a generative AI model with user-specific characteristics. In this process, the server learns important patterns in the data and builds an AI to generate responses that mimic the user's communication style.

[0266] For example, if a user asks, "What should I do today?", the system will refer to their past activity history and generate a response that offers a personalized suggestion, such as, "How about going for a walk today?"

[0267] Examples of prompts in this system include instructions such as, "Generate a response that provides suggestions appropriate to the current situation based on the user's past history."

[0268] Finally, the results generated by the response generation means are presented to the user via the response provision means. This allows the user to enhance the usefulness and relevance of the information they receive from the system.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The user performs voice input using a device such as a smartphone or smart speaker. For example, the user might say, "What's the weather like tomorrow?" This voice data is captured by the device. The input is voice data, and the output is text data. Speech recognition technology is used for this conversion; the speech recognition software on the device converts the voice data into text.

[0272] Step 2:

[0273] The terminal sends the converted text data to the server. Specifically, the text data is transmitted to the server via a secure communication protocol. The input is text data, and the output is raw text data stored on the server.

[0274] Step 3:

[0275] The server performs noise reduction and standardization on the received text data. Specifically, it removes unnecessary symbols and mistranslated words from the text data and prepares it for analysis. The input is raw text data, and the output is processed text data.

[0276] Step 4:

[0277] The server extracts features for a generative AI model based on processed text data. Specifically, it uses natural language processing techniques to extract important keywords and phrases from the data. The input is processed text data, and the output is the feature-extracted data.

[0278] Step 5:

[0279] The server constructs a generative AI model specialized for the user using the extracted features. As a specific operation, it uses a machine learning algorithm to learn the user's past data patterns and optimize the model. The input is the feature-extracted data, and the output is the generative AI model.

[0280] Step 6:

[0281] The server utilizes the generative AI model to generate a response based on the user's input. As a specific operation, it forms a response based on the prompt text and referring to the past behavior history. The input is the user's new input and the generative AI model, and the output is the generated response.

[0282] Step 7:

[0283] The server transmits the generated response to the terminal, and the terminal presents it to the user. As a specific operation, the terminal presents the generated text to the user as voice using voice synthesis technology. The input is the generated response, and the output is the voice output to the user.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In the modern information and communication environment, users seek higher satisfaction and psychological support by receiving individualized information and content. However, there is a lack of means to provide appropriately customized content based on the individual hobbies and interests of users. For this reason, users are burdened with the task of selecting content that suits their interests from a large amount of information.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0288] In this invention, the server includes an information and communication device for collecting user voice and text data, a data processing device for organizing and storing the collected data for each individual user, performing noise reduction and standardization, and an AI generation device that uses the organized and stored data to model user-specific characteristics by combining natural language processing and machine learning. This makes it possible to generate stories based on the user's interests and themes and provide a personalized content experience.

[0289] An "information and communication device" is a device used to collect user voice data and text data.

[0290] A "data processing device" is a device that organizes and stores collected data for each individual user, and performs noise reduction and standardization.

[0291] An "AI generation device" is a device that uses organized and stored data to combine natural language processing and machine learning to model user-specific characteristics.

[0292] A "response generation device" is a device that generates an appropriate response using an AI model that is generated based on input from the user.

[0293] A "response provider" is a device that provides the generated response to the user.

[0294] A "story generation device" is a device that generates stories based on the user's interests and themes.

[0295] A "content distribution device" is a device that delivers generated stories to users.

[0296] The system implementing this invention collects and processes user voice and text data, and generates and provides a user-specific story. The system receives voice input from a smartphone or smart speaker as an information and communication device, and a data processing device organizes the data, performs noise reduction and standardization. The organized data is then used by an AI generation device to create a generative AI model that models the user's unique characteristics using natural language processing and machine learning techniques.

[0297] The system's response generator interprets the current user input based on an AI model and uses a story generator to create a story tailored to the user's interests and themes. This story is then delivered to the user through a content distribution device.

[0298] Specifically, the speech_recognition library is used for voice data conversion and noise reduction, and the GPT-2 model included in the transformers library is used as the natural language generation model for story generation. This enables an interactive experience where users can listen to stories that match their interests based on their voice input.

[0299] For example, if a user says something along the lines of "I want to relax today," a story will be generated based on that history, creating a narrative that is ideal for relaxation. A concrete example of an input prompt to the generation AI model might be text like, "Please create a relaxing story." The story generated based on this prompt will then be delivered as content that resonates with the user's emotional needs.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The terminal recognizes the user's voice and collects the voice data. Here, the terminal obtains the voice using a microphone and converts the voice into text data with voice recognition software (e.g., speech_recognition library). The input is voice data, and the output is text data.

[0303] Step 2:

[0304] The server receives the text data and performs noise removal and normalization with a data processing device. As a result, unnecessary information in the text data is removed and it is arranged in a unified format. The input is the converted text data, and the output is the text with noise removed and normalized.

[0305] Step 3:

[0306] The server inputs the arranged text data into an AI generation device and models user-specific features using machine learning. Here, natural language processing technology is used to learn the user's past conversation history and preferences to create a generated AI model. The input is the normalized text data, and the output is a user-specific generated AI model.

[0307] Step 4:

[0308] Based on the generated AI model, the server uses a story generation device to generate a story that suits the user's interests. The generation prompt uses "generated AI model, prompt sentence" to generate a story according to the latest user interests and themes. The input is a user-specific generated AI model, and the output is the generated story.

[0309] Step 5:

[0310] The server provides the generated story to the user via a content distribution device. The story is delivered to the terminal in audio or text format, allowing the user to access and enjoy it. The input is the generated story, and the output is the content presented to the user.

[0311] 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.

[0312] The system of this invention is implemented on information and communication devices, such as smartphones and smart speakers, for capturing users' daily communications. The user terminal has the function of collecting voice data and text data in real time and sending it to a server periodically.

[0313] The server organizes and stores the transmitted data using a data processing device, performing noise removal and standardization during this process. After this processing, the data is used by an AI generation device to generate an AI model that reflects the characteristics of each user. The AI ​​model utilizes natural language processing and machine learning technologies to deeply learn the patterns and characteristics of the user's conversation, thereby improving the accuracy of personalized responses.

[0314] Furthermore, this system incorporates an emotion engine that analyzes the user's voice tone and word choice to generate emotional information. This emotional information is then used by the response generation device to derive an appropriate response that is in line with the user's emotional state.

[0315] For example, if a user says to a smart speaker, "I'm really tired today," the device sends this voice as data to a server. The server analyzes the tone of the voice and generates emotional information related to the word "tired." If the emotion engine detects "fatigue," an encouraging response such as "You're always working so hard. Please get some rest" is created by the response generator and finally provided to the user as voice by the response provider.

[0316] In this way, the system enables not only simple response generation through interaction with the user, but also interaction that is sensitive to the user's emotions. The emotion engine, by being reflected in the AI ​​model, improves the quality of the dialogue that takes the user's emotions into account. This invention is expected to be implemented on a wider scale when combined with communication plans.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The user terminal receives user voice input and text messages in real time and records them as digital data. This data is stored in a format that includes a timestamp of the user's statements and a device ID.

[0320] Step 2:

[0321] The terminal processes the recorded data in batches at regular intervals and sends it to the server. The transmission uses encryption protocols to ensure data confidentiality while enabling efficient transfer.

[0322] Step 3:

[0323] The server stores the received audio and text data in a database and organizes it by user ID. The data is also denoised and standardized to facilitate subsequent processing.

[0324] Step 4:

[0325] The data processing unit inputs the organized data into an emotion engine and analyzes the user's emotions based on voice tone and word choice. The analysis results are output as emotional information such as "happiness," "fatigue," and "anger."

[0326] Step 5:

[0327] The server's AI generator receives output from the emotion engine and updates the AI ​​model to take emotional information into account. This results in a model that reflects the user's unique characteristics.

[0328] Step 6:

[0329] When a user provides new input, the device sends the data at that moment to the server. This input can be in the form of speech or text.

[0330] Step 7:

[0331] The server processes the latest user input using an AI model and generates responses that reflect emotional information. The response generator produces context-adaptive responses, taking into account the user's past history and emotional state.

[0332] Step 8:

[0333] The generated response is delivered to the user terminal by the response provider, and the terminal either plays it back using speech synthesis technology or displays it as text.

[0334] Step 9:

[0335] The user receives responses through the device and can proceed with the next dialogue. The system continuously provides dialogue that reflects the user's emotions by repeatedly engaging in interaction.

[0336] (Example 2)

[0337] 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".

[0338] In today's information and communication environment, understanding user-specific characteristics and providing appropriate responses tailored to their emotions is crucial for achieving high levels of satisfaction that meet individual needs. However, existing systems struggle to accurately analyze user emotions and provide responses that adapt to dynamically changing emotions and preferences, hindering improvements in the user experience. Solving this challenge is essential.

[0339] 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.

[0340] In this invention, the server includes means for collecting user communication content, processing equipment for individually organizing and processing the collected information, removing unwanted noise and applying standardization, generating equipment for generating a model that combines natural language processing and machine learning techniques based on the organized information, and a device that analyzes the user's emotions during communication and generates a selective response adapted to those emotions. This enables highly accurate responses tailored to the user's individual emotions and characteristics, significantly improving the user experience.

[0341] "User communication content" refers to information such as voice and text transmitted by users through information and communication devices.

[0342] A "collection device" refers to a device equipped with hardware and software functions for acquiring and storing user communication content.

[0343] "Processing equipment" refers to devices and solutions used to organize collected information, remove unnecessary elements, and standardize it.

[0344] A "generation device" refers to a device that utilizes organized information to create user-specific models using natural language processing and machine learning techniques.

[0345] "Analyzing emotions" means identifying and classifying the user's emotional state from the content of their communications.

[0346] A "device with the function of generating a selected response" refers to a device that has the ability to create and provide an optimal response based on analyzed information.

[0347] This invention is implemented through a system combining an information and communication device and a server. Specifically, terminals capable of voice and text input are used to capture the user's daily communications. These terminals include smartphones and smart speakers, and they collect the content of the user's communications in real time.

[0348] The terminal sends the collected audio and text data to the server. On the server, the information is first organized using data processing equipment, noise is removed, and then standardization is performed. Data processing libraries such as Python's Pandas and NumPy are used for this process. The organized data is then analyzed by an AI generator to generate a user-specific natural language processing model. This learns the user's unique conversation patterns and contexts, and builds an individual model.

[0349] Furthermore, the server is equipped with an emotion analysis engine that analyzes the user's emotional state from the communication content. Based on the tone and speed of the voice data and the emotional expressions contained in the text, it infers the user's emotions and prepares a response based on those emotions. The response generation device takes this emotional information into consideration and provides the user with an appropriate response.

[0350] For example, if a user says to a smart speaker, "I'm feeling a little down today," the device acquires this voice as data and sends it to the server. The server uses an emotion engine to analyze the emotion of "feeling down," and an AI model generates a response such as, "Has something happened recently? I'm here to listen if there's anything you want to talk about." This response is then provided to the user through the device, enabling a conversation that is sensitive to their emotions.

[0351] An example of a prompt is when the user inputs "Have you had anything good happen recently?" into the system, and the system generates and provides an appropriate response. In this way, the present invention provides a system that enables sophisticated dialogue based on the user's emotional information.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The terminal collects user communication content in real time through a voice input device or text input. This collected data is obtained as a digital voice signal if it is voice data, or as character data if it is text data. In particular, if voice is input, it is converted into character data using speech recognition software. This is the input, and the output is obtained in the form of digital voice or text.

[0355] Step 2:

[0356] The terminal transmits collected voice and text data to the server via the network. During this process, the data is encrypted and transmitted using a secure protocol (such as HTTPS). This is the input, and the state where the data has safely reached the server is the output.

[0357] Step 3:

[0358] The server processes the received data using a data processing unit. The first steps are noise reduction and standardization. For audio data, background noise is removed, and only the clear audio signal is extracted. For text data, character encoding is standardized and unnecessary spaces are removed. This is the input, and the standardized, noise-free data is the output.

[0359] Step 4:

[0360] The server passes the organized data to the AI ​​generator, which then generates an AI model that captures the user's conversational characteristics. Using natural language processing techniques to analyze the data and machine learning (e.g., neural networks), it generates a user-specific communication model. The input data consists of organized text and audio features, and the output is a custom model for each user.

[0361] Step 5:

[0362] The server uses an AI model and an emotion analysis engine to analyze the user's emotional state and generates an appropriate response using a response generator. The analysis engine extracts emotional information based on voice tone and word choice, and this information is reflected in the AI ​​model. Based on this input emotional data, the response generator generates a response in text format. The output is a response sentence appropriate for the user.

[0363] Step 6:

[0364] The terminal receives the generated response and provides it to the user as either voice or text. In the case of voice output, speech synthesis technology is used to convert the text into voice data, which is then played through the speaker. The input is the response data from the server, and the output is a natural-sounding response to the user.

[0365] (Application Example 2)

[0366] 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."

[0367] In modern information and communication technology, accurately analyzing user emotions and ensuring security is a crucial challenge. However, existing systems struggle to detect changes in user emotions or unnatural communication patterns in real time and to quickly warn of potential dangers or scams. To address this challenge, a system is needed that analyzes emotions and automatically detects abnormal communication.

[0368] 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.

[0369] In this invention, the server includes an emotion analysis engine, means for analyzing emotions from voice and text information and detecting abnormal emotions or unnatural communication patterns, security means for warning of potential dangers or fraud and notifying trusted contacts as necessary, and an AI generator for modeling consumer-specific characteristics. This makes it possible to ensure user safety and proactively detect potential dangers.

[0370] "Audio information" refers to data obtained from the voice spoken by the user, and is used to analyze the user's emotions and intentions.

[0371] "Textual information" refers to text data entered or submitted by users, which is then analyzed using natural language processing.

[0372] A "communication device" is a device used to collect voice and text information and transmit it to a server, and includes smartphones and smart speakers.

[0373] "Data processing means" refers to a system that organizes and stores collected information, and performs noise removal and standardization.

[0374] An "AI generation device" is a device that combines natural language understanding and machine learning to model consumer-specific characteristics and generate responses tailored to individual users.

[0375] A "response generation means" is a device that creates an appropriate response using a model generated by an AI generation device based on input from the user.

[0376] "Response provision means" refers to a device or system for providing the generated response to the user.

[0377] An "emotion analysis engine" is a program or device that analyzes a user's emotions from voice and text information and identifies their emotional state.

[0378] A "security measure" is a system that detects abnormal emotions or unnatural communication patterns, warns users of potential dangers, and notifies trusted contacts.

[0379] This invention is a system that performs emotion analysis and detects abnormal communication patterns using user voice and text information. In its implementation, a smartphone or smart speaker is used as the communication device. These communication devices are responsible for collecting the user's voice and text information and transmitting it to the server.

[0380] The server has data processing capabilities to organize and store the received information, remove noise, and standardize it. Next, an AI generation device is used to model consumer-specific characteristics from the collected information and generate an appropriate response. The response generation means creates a user-optimized response based on this AI model and outputs it to the user via the response provision means.

[0381] The emotion analysis engine analyzes information to identify the user's emotional state. Security measures detect abnormal emotions and unnatural communication patterns, warning the user of potential dangers and notifying trusted contacts when necessary.

[0382] For example, if a user says "I'm anxious" to a smart speaker, the server analyzes the voice information, and the emotion analysis engine identifies "anxiety." Security measures include issuing a warning to the user and contacting registered family members if the server determines that this deviates from normal emotional patterns. A specific example of a prompt would be, "Please create prompts for an AI model to analyze the normal communication of elderly individuals and detect changes in their emotions."

[0383] Thus, in order to concretely implement the invention, a system is needed that analyzes the user's emotional state and takes appropriate action. This aims to ensure user safety and prevent potential dangers.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The terminal collects the user's voice and text information in real time. This information is transmitted to the server via a communication device. The terminal receives the input and converts the voice to text using speech recognition software. The output at this stage is the text information.

[0387] Step 2:

[0388] The server organizes and stores the acquired information using data processing tools, and performs noise removal and standardization. The input is textual information from the terminal, which is processed with data cleansing software to obtain a clean dataset. The output of this step is organized and standardized data.

[0389] Step 3:

[0390] The sentiment analysis engine on the server analyzes the user's emotional state using organized, standardized data. Calculations are performed based on sentiment analysis algorithms, with a clean dataset as input. The output is metadata indicating the user's emotional state.

[0391] Step 4:

[0392] The server's AI generator models user-specific characteristics, including emotional state metadata, and uses the generated AI model to produce appropriate responses. Here, consumer-specific future predictions and conditioned reflex models are combined to design a response framework. The output of this step is a customized response message.

[0393] Step 5:

[0394] The response generation means sends a customized response message to the user through the response provision means. The output information from the server is returned to the terminal and provided to the user as voice or text via a smart speaker, smartphone, etc. The feedback to the user is the final output of this step.

[0395] Step 6:

[0396] The security measures detect abnormal patterns and potential dangers from analyzed emotional states. The input is emotional state metadata, and an anomaly detection algorithm makes a rapid judgment. The output is a warning notification about potential dangers, which is sent to trusted contacts as needed.

[0397] By having each step work in coordination, a system is created that ensures user safety, improves the accuracy of individual responses, and manages potential risks.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] The system of this invention is implemented using information and communication devices that users use on a daily basis, namely smartphones and smart speakers. The user terminal has the function of recording daily conversations and messages in voice and text and sending that data to a server. The transmitted data is denoised and standardized by a data processing device on the server, and then efficiently stored and managed.

[0415] The server then uses the collected data to generate user-specific AI models using an AI generator. This involves data-driven feature extraction, and natural language processing and machine learning techniques are used to mimic the user's unique communication style and emotions.

[0416] For example, if a user says "The weather's nice today" through their smartphone, the device converts the audio into text and sends it to the server. Based on this data, the server refers to the user's favorite activities and topics from past data and generates a friendly response, such as "You often went to the park on nice days, didn't you? Do you have plans to go today?"

[0417] The response generation device creates an appropriate response to user input, taking into account the user's past actions and conversation history, and presents this response to the user via the response provision device. This system continuously updates its interaction with the user, enabling the AI ​​to behave in a way that allows the user to enjoy a friendly conversation with a deceased family member.

[0418] Therefore, users can utilize the system to receive psychological support, such as alleviating loneliness and providing a sense of security. This invention can be offered as a total communication solution when combined with a communication plan.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The user terminal receives voice commands and text messages and records them as digital data. This data has the function of recording the user's daily conversations and messages along with a timestamp.

[0422] Step 2:

[0423] The device periodically or upon a specified trigger sends the collected data to the server. The data is encrypted and transmitted through a secure channel to ensure its safety.

[0424] Step 3:

[0425] The server stores the received data in a database and organizes it, associating it with each user ID. Here, the data is formatted, denoised, and formatted as needed.

[0426] Step 4:

[0427] The server uses the organized data to train a user-specific AI model using an AI generator. This process employs natural language processing algorithms to learn the user's language patterns and characteristics.

[0428] Step 5:

[0429] When a user makes a new voice or text input through the device, the device immediately issues a command to send that data to the server.

[0430] Step 6:

[0431] The server uses an AI model to generate an appropriate response based on the most recent input received. This includes contextual analysis and sentiment recognition.

[0432] Step 7:

[0433] The generated response is sent to the user terminal via the response provider. The terminal either plays the response aloud using speech synthesis technology or displays it as text on the screen.

[0434] Step 8:

[0435] The user receives a response and continues the conversation. This feedback loop allows the system to continuously update user data and evolve the AI ​​model.

[0436] (Example 1)

[0437] 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."

[0438] In modern information and communication devices, generating personalized responses that accurately reflect the individual characteristics of users is challenging. Furthermore, data noise reduction and standardization, as well as dynamic updates of user profiles, are required to improve the accuracy of user input data and enhance response quality. Providing systems that efficiently perform these processes in a cost-effective manner is also a crucial challenge.

[0439] 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.

[0440] In this invention, the server includes means for denoising and standardizing acquired data, AI generation means for generating a generative AI model tailored to individual users by combining natural language processing and machine learning techniques based on the standardized data, and means for dynamically updating the generative AI model to adapt to the user's recent behavior and preferences. This makes it possible to appropriately generate responses that reflect the individual characteristics of the user and provide high-quality responses to user input in real time.

[0441] A "user" is an entity that uses information and communication equipment to generate voice data or text data.

[0442] "Terminal means" refers to a device that has the function of acquiring voice data and text data from the user and transmitting it to a server.

[0443] A "server device" is a device that receives data transmitted from a terminal device and performs noise reduction and standardization processing on it.

[0444] "Noise reduction" is the process of removing unnecessary information from data to make it suitable for analysis.

[0445] "Standardization" refers to organizing data into a consistent format so that it can be processed uniformly.

[0446] An "AI generation method" is a device that uses natural language processing and machine learning technologies to model user-specific characteristics.

[0447] A "generative AI model" is an algorithmic model that mimics a user's communication style and preferences to generate responses.

[0448] A "response generation means" is a device that manages the process of forming an appropriate response to user input based on a generated AI model.

[0449] "Response provision means" refers to a device or function for transmitting the generated response to the user.

[0450] "Dynamic updating" refers to the process of adapting and modifying the generated AI model in real time or periodically based on the user's latest behavior and preferences.

[0451] "Information and communication equipment" refers to electronic devices used to collect user data and provide responses.

[0452] A "communications lease agreement" refers to a fee structure for the use of information and communication equipment, and is a contractual form related to the business model for which users utilize this system.

[0453] The system of this invention consists of the interaction of a terminal, a server, and a user, and provides the user with a personalized communication experience.

[0454] First, the user gives voice commands using a device such as a smartphone or smart speaker. The device then uses speech recognition technology to convert the voice data received from the user into text data. For example, common industry technologies for speech recognition software include Google Speech-to-Text and other speech recognition libraries.

[0455] Next, the terminal sends the generated text data to the server. The server then performs noise reduction and standardization on the received data. This utilizes speech processing algorithms to ensure the reliability and consistency of the data.

[0456] Furthermore, the server uses natural language processing and machine learning techniques to build a generative AI model with user-specific characteristics. In this process, the server learns important patterns in the data and builds an AI to generate responses that mimic the user's communication style.

[0457] For example, if a user asks, "What should I do today?", the system will refer to their past activity history and generate a response that offers a personalized suggestion, such as, "How about going for a walk today?"

[0458] Examples of prompts in this system include instructions such as, "Generate a response that provides suggestions appropriate to the current situation based on the user's past history."

[0459] Finally, the results generated by the response generation means are presented to the user via the response provision means. This allows the user to enhance the usefulness and relevance of the information they receive from the system.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] The user performs voice input using a device such as a smartphone or smart speaker. For example, the user might say, "What's the weather like tomorrow?" This voice data is captured by the device. The input is voice data, and the output is text data. Speech recognition technology is used for this conversion; the speech recognition software on the device converts the voice data into text.

[0463] Step 2:

[0464] The terminal sends the converted text data to the server. Specifically, the text data is transmitted to the server via a secure communication protocol. The input is text data, and the output is raw text data stored on the server.

[0465] Step 3:

[0466] The server performs noise reduction and standardization on the received text data. Specifically, it removes unnecessary symbols and mistranslated words from the text data and prepares it for analysis. The input is raw text data, and the output is processed text data.

[0467] Step 4:

[0468] The server extracts features for a generative AI model based on processed text data. Specifically, it uses natural language processing techniques to extract important keywords and phrases from the data. The input is processed text data, and the output is the feature-extracted data.

[0469] Step 5:

[0470] The server uses the extracted features to build a user-specific generative AI model. Specifically, it uses machine learning algorithms to learn the user's past data patterns and optimize the model. The input is the feature-extracted data, and the output is the generative AI model.

[0471] Step 6:

[0472] The server utilizes a generative AI model to generate responses based on user input. Specifically, it forms responses based on prompts and references past user behavior. The input consists of the user's new input and the generative AI model, while the output is the generated response.

[0473] Step 7:

[0474] The server sends the generated response to the terminal, which then presents it to the user. Specifically, the terminal uses speech synthesis technology to present the generated text to the user as audio. The input is the generated response, and the output is the audio output to the user.

[0475] (Application Example 1)

[0476] 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."

[0477] In today's information and communication environment, users seek greater satisfaction and psychological support by receiving personalized information and content. However, there is a lack of means to provide content appropriately customized based on users' individual hobbies and interests. As a result, users are burdened with the task of selecting information that matches their interests from a vast amount of data.

[0478] 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.

[0479] In this invention, the server includes an information and communication device for collecting user voice and text data, a data processing device for organizing and storing the collected data for each individual user, performing noise reduction and standardization, and an AI generation device that uses the organized and stored data to model user-specific characteristics by combining natural language processing and machine learning. This makes it possible to generate stories based on the user's interests and themes and provide a personalized content experience.

[0480] An "information and communication device" is a device used to collect user voice data and text data.

[0481] A "data processing device" is a device that organizes and stores collected data for each individual user, and performs noise reduction and standardization.

[0482] An "AI generation device" is a device that uses organized and stored data to combine natural language processing and machine learning to model user-specific characteristics.

[0483] A "response generation device" is a device that generates an appropriate response using an AI model that is generated based on input from the user.

[0484] A "response provider" is a device that provides the generated response to the user.

[0485] A "story generation device" is a device that generates stories based on the user's interests and themes.

[0486] A "content distribution device" is a device that delivers generated stories to users.

[0487] The system implementing this invention collects and processes user voice and text data, and generates and provides a user-specific story. The system receives voice input from a smartphone or smart speaker as an information and communication device, and a data processing device organizes the data, performs noise reduction and standardization. The organized data is then used by an AI generation device to create a generative AI model that models the user's unique characteristics using natural language processing and machine learning techniques.

[0488] The system's response generator interprets the current user input based on an AI model and uses a story generator to create a story tailored to the user's interests and themes. This story is then delivered to the user through a content distribution device.

[0489] Specifically, the speech_recognition library is used for voice data conversion and noise reduction, and the GPT-2 model included in the transformers library is used as the natural language generation model for story generation. This enables an interactive experience where users can listen to stories that match their interests based on their voice input.

[0490] For example, if a user says something along the lines of "I want to relax today," a story will be generated based on that history, creating a narrative that is ideal for relaxation. A concrete example of an input prompt to the generation AI model might be text like, "Please create a relaxing story." The story generated based on this prompt will then be delivered as content that resonates with the user's emotional needs.

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The device recognizes the user's voice and collects the audio data. Here, the device uses a microphone to acquire the voice and converts it into text data using speech recognition software (e.g., the speech_recognition library). The input is audio data, and the output is text data.

[0494] Step 2:

[0495] The server receives text data, and the data processing unit performs noise reduction and standardization. This removes unnecessary information from the text data and arranges it into a unified format. The input is the converted text data, and the output is the noise-reduced and standardized text.

[0496] Step 3:

[0497] The server inputs the formatted text data into the AI ​​generator and uses machine learning to model user-specific characteristics. Here, natural language processing techniques are used to learn the user's past conversation history and preferences to create a generative AI model. The input is standardized text data, and the output is a user-specific generative AI model.

[0498] Step 4:

[0499] The server uses a story generation device based on a generative AI model to generate stories that match the user's interests. The generation prompt uses a "generative AI model, prompt sentence" to generate stories that reflect the latest user interests and themes. The input is a user-specific generative AI model, and the output is the generated story.

[0500] Step 5:

[0501] The server provides the generated story to the user via a content distribution device. The story is delivered to the terminal in audio or text format, allowing the user to access and enjoy it. The input is the generated story, and the output is the content presented to the user.

[0502] 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.

[0503] The system of this invention is implemented on information and communication devices, such as smartphones and smart speakers, for capturing users' daily communications. The user terminal has the function of collecting voice data and text data in real time and sending it to a server periodically.

[0504] The server organizes and stores the transmitted data using a data processing device, performing noise removal and standardization during this process. After this processing, the data is used by an AI generation device to generate an AI model that reflects the characteristics of each user. The AI ​​model utilizes natural language processing and machine learning technologies to deeply learn the patterns and characteristics of the user's conversation, thereby improving the accuracy of personalized responses.

[0505] Furthermore, this system incorporates an emotion engine that analyzes the user's voice tone and word choice to generate emotional information. This emotional information is then used by the response generation device to derive an appropriate response that is in line with the user's emotional state.

[0506] For example, if a user says to a smart speaker, "I'm really tired today," the device sends this voice as data to a server. The server analyzes the tone of the voice and generates emotional information related to the word "tired." If the emotion engine detects "fatigue," an encouraging response such as "You're always working so hard. Please get some rest" is created by the response generator and finally provided to the user as voice by the response provider.

[0507] In this way, the system enables not only simple response generation through interaction with the user, but also interaction that is sensitive to the user's emotions. The emotion engine, by being reflected in the AI ​​model, improves the quality of the dialogue that takes the user's emotions into account. This invention is expected to be implemented on a wider scale when combined with communication plans.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The user terminal receives user voice input and text messages in real time and records them as digital data. This data is stored in a format that includes a timestamp of the user's statements and a device ID.

[0511] Step 2:

[0512] The terminal processes the recorded data in batches at regular intervals and sends it to the server. The transmission uses encryption protocols to ensure data confidentiality while enabling efficient transfer.

[0513] Step 3:

[0514] The server stores the received audio and text data in a database and organizes it by user ID. The data is also denoised and standardized to facilitate subsequent processing.

[0515] Step 4:

[0516] The data processing unit inputs the organized data into an emotion engine and analyzes the user's emotions based on voice tone and word choice. The analysis results are output as emotional information such as "happiness," "fatigue," and "anger."

[0517] Step 5:

[0518] The server's AI generator receives output from the emotion engine and updates the AI ​​model to take emotional information into account. This results in a model that reflects the user's unique characteristics.

[0519] Step 6:

[0520] When a user provides new input, the device sends the data at that moment to the server. This input can be in the form of speech or text.

[0521] Step 7:

[0522] The server processes the latest user input using an AI model and generates responses that reflect emotional information. The response generator produces context-adaptive responses, taking into account the user's past history and emotional state.

[0523] Step 8:

[0524] The generated response is delivered to the user terminal by the response provider, and the terminal either plays it back using speech synthesis technology or displays it as text.

[0525] Step 9:

[0526] The user receives responses through the device and can proceed with the next dialogue. The system continuously provides dialogue that reflects the user's emotions by repeatedly engaging in interaction.

[0527] (Example 2)

[0528] 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."

[0529] In today's information and communication environment, understanding user-specific characteristics and providing appropriate responses tailored to their emotions is crucial for achieving high levels of satisfaction that meet individual needs. However, existing systems struggle to accurately analyze user emotions and provide responses that adapt to dynamically changing emotions and preferences, hindering improvements in the user experience. Solving this challenge is essential.

[0530] 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.

[0531] In this invention, the server includes means for collecting user communication content, processing equipment for individually organizing and processing the collected information, removing unwanted noise and applying standardization, generating equipment for generating a model that combines natural language processing and machine learning techniques based on the organized information, and a device that analyzes the user's emotions during communication and generates a selective response adapted to those emotions. This enables highly accurate responses tailored to the user's individual emotions and characteristics, significantly improving the user experience.

[0532] "User communication content" refers to information such as voice and text transmitted by users through information and communication devices.

[0533] A "collection device" refers to a device equipped with hardware and software functions for acquiring and storing user communication content.

[0534] "Processing equipment" refers to devices and solutions used to organize collected information, remove unnecessary elements, and standardize it.

[0535] A "generation device" refers to a device that utilizes organized information to create user-specific models using natural language processing and machine learning techniques.

[0536] "Analyzing emotions" means identifying and classifying the user's emotional state from the content of their communications.

[0537] A "device with the function of generating a selected response" refers to a device that has the ability to create and provide an optimal response based on analyzed information.

[0538] This invention is implemented through a system combining an information and communication device and a server. Specifically, terminals capable of voice and text input are used to capture the user's daily communications. These terminals include smartphones and smart speakers, and they collect the content of the user's communications in real time.

[0539] The terminal sends the collected audio and text data to the server. On the server, the information is first organized using data processing equipment, noise is removed, and then standardization is performed. Data processing libraries such as Python's Pandas and NumPy are used for this process. The organized data is then analyzed by an AI generator to generate a user-specific natural language processing model. This learns the user's unique conversation patterns and contexts, and builds an individual model.

[0540] Furthermore, the server is equipped with an emotion analysis engine that analyzes the user's emotional state from the communication content. Based on the tone and speed of the voice data and the emotional expressions contained in the text, it infers the user's emotions and prepares a response based on those emotions. The response generation device takes this emotional information into consideration and provides the user with an appropriate response.

[0541] For example, if a user says to a smart speaker, "I'm feeling a little down today," the device acquires this voice as data and sends it to the server. The server uses an emotion engine to analyze the emotion of "feeling down," and an AI model generates a response such as, "Has something happened recently? I'm here to listen if there's anything you want to talk about." This response is then provided to the user through the device, enabling a conversation that is sensitive to their emotions.

[0542] An example of a prompt is when the user inputs "Have you had anything good happen recently?" into the system, and the system generates and provides an appropriate response. In this way, the present invention provides a system that enables sophisticated dialogue based on the user's emotional information.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The terminal collects user communication content in real time through a voice input device or text input. This collected data is obtained as a digital voice signal if it is voice data, or as character data if it is text data. In particular, if voice is input, it is converted into character data using speech recognition software. This is the input, and the output is obtained in the form of digital voice or text.

[0546] Step 2:

[0547] The terminal transmits collected voice and text data to the server via the network. During this process, the data is encrypted and transmitted using a secure protocol (such as HTTPS). This is the input, and the state where the data has safely reached the server is the output.

[0548] Step 3:

[0549] The server processes the received data using a data processing unit. The first steps are noise reduction and standardization. For audio data, background noise is removed, and only the clear audio signal is extracted. For text data, character encoding is standardized and unnecessary spaces are removed. This is the input, and the standardized, noise-free data is the output.

[0550] Step 4:

[0551] The server passes the organized data to the AI ​​generator, which then generates an AI model that captures the user's conversational characteristics. Using natural language processing techniques to analyze the data and machine learning (e.g., neural networks), it generates a user-specific communication model. The input data consists of organized text and audio features, and the output is a custom model for each user.

[0552] Step 5:

[0553] The server uses an AI model and an emotion analysis engine to analyze the user's emotional state and generates an appropriate response using a response generator. The analysis engine extracts emotional information based on voice tone and word choice, and this information is reflected in the AI ​​model. Based on this input emotional data, the response generator generates a response in text format. The output is a response sentence appropriate for the user.

[0554] Step 6:

[0555] The terminal receives the generated response and provides it to the user as either voice or text. In the case of voice output, speech synthesis technology is used to convert the text into voice data, which is then played through the speaker. The input is the response data from the server, and the output is a natural-sounding response to the user.

[0556] (Application Example 2)

[0557] 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."

[0558] In modern information and communication technology, accurately analyzing user emotions and ensuring security is a crucial challenge. However, existing systems struggle to detect changes in user emotions or unnatural communication patterns in real time and to quickly warn of potential dangers or scams. To address this challenge, a system is needed that analyzes emotions and automatically detects abnormal communication.

[0559] 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.

[0560] In this invention, the server includes an emotion analysis engine, means for analyzing emotions from voice and text information and detecting abnormal emotions or unnatural communication patterns, security means for warning of potential dangers or fraud and notifying trusted contacts as necessary, and an AI generator for modeling consumer-specific characteristics. This makes it possible to ensure user safety and proactively detect potential dangers.

[0561] "Audio information" refers to data obtained from the voice spoken by the user, and is used to analyze the user's emotions and intentions.

[0562] "Textual information" refers to text data entered or submitted by users, which is then analyzed using natural language processing.

[0563] A "communication device" is a device used to collect voice and text information and transmit it to a server, and includes smartphones and smart speakers.

[0564] "Data processing means" refers to a system that organizes and stores collected information, and performs noise removal and standardization.

[0565] An "AI generation device" is a device that combines natural language understanding and machine learning to model consumer-specific characteristics and generate responses tailored to individual users.

[0566] A "response generation means" is a device that creates an appropriate response using a model generated by an AI generation device based on input from the user.

[0567] "Response provision means" refers to a device or system for providing the generated response to the user.

[0568] An "emotion analysis engine" is a program or device that analyzes a user's emotions from voice and text information and identifies their emotional state.

[0569] A "security measure" is a system that detects abnormal emotions or unnatural communication patterns, warns users of potential dangers, and notifies trusted contacts.

[0570] This invention is a system that performs emotion analysis and detects abnormal communication patterns using user voice and text information. In its implementation, a smartphone or smart speaker is used as the communication device. These communication devices are responsible for collecting the user's voice and text information and transmitting it to the server.

[0571] The server has data processing capabilities to organize and store the received information, remove noise, and standardize it. Next, an AI generation device is used to model consumer-specific characteristics from the collected information and generate an appropriate response. The response generation means creates a user-optimized response based on this AI model and outputs it to the user via the response provision means.

[0572] The emotion analysis engine analyzes information to identify the user's emotional state. Security measures detect abnormal emotions and unnatural communication patterns, warning the user of potential dangers and notifying trusted contacts when necessary.

[0573] For example, if a user says "I'm anxious" to a smart speaker, the server analyzes the voice information, and the emotion analysis engine identifies "anxiety." Security measures include issuing a warning to the user and contacting registered family members if the server determines that this deviates from normal emotional patterns. A specific example of a prompt would be, "Please create prompts for an AI model to analyze the normal communication of elderly individuals and detect changes in their emotions."

[0574] Thus, in order to concretely implement the invention, a system is needed that analyzes the user's emotional state and takes appropriate action. This aims to ensure user safety and prevent potential dangers.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The terminal collects the user's voice and text information in real time. This information is transmitted to the server via a communication device. The terminal receives the input and converts the voice to text using speech recognition software. The output at this stage is the text information.

[0578] Step 2:

[0579] The server organizes and stores the acquired information using data processing tools, and performs noise removal and standardization. The input is textual information from the terminal, which is processed with data cleansing software to obtain a clean dataset. The output of this step is organized and standardized data.

[0580] Step 3:

[0581] The sentiment analysis engine on the server analyzes the user's emotional state using organized, standardized data. Calculations are performed based on sentiment analysis algorithms, with a clean dataset as input. The output is metadata indicating the user's emotional state.

[0582] Step 4:

[0583] The server's AI generator models user-specific characteristics, including emotional state metadata, and uses the generated AI model to produce appropriate responses. Here, consumer-specific future predictions and conditioned reflex models are combined to design a response framework. The output of this step is a customized response message.

[0584] Step 5:

[0585] The response generation means sends a customized response message to the user through the response provision means. The output information from the server is returned to the terminal and provided to the user as voice or text via a smart speaker, smartphone, etc. The feedback to the user is the final output of this step.

[0586] Step 6:

[0587] The security measures detect abnormal patterns and potential dangers from analyzed emotional states. The input is emotional state metadata, and an anomaly detection algorithm makes a rapid judgment. The output is a warning notification about potential dangers, which is sent to trusted contacts as needed.

[0588] By having each step work in coordination, a system is created that ensures user safety, improves the accuracy of individual responses, and manages potential risks.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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".

[0606] The system of this invention is implemented using information and communication devices that users use on a daily basis, namely smartphones and smart speakers. The user terminal has the function of recording daily conversations and messages in voice and text and sending that data to a server. The transmitted data is denoised and standardized by a data processing device on the server, and then efficiently stored and managed.

[0607] The server then uses the collected data to generate user-specific AI models using an AI generator. This involves data-driven feature extraction, and natural language processing and machine learning techniques are used to mimic the user's unique communication style and emotions.

[0608] For example, if a user says "The weather's nice today" through their smartphone, the device converts the audio into text and sends it to the server. Based on this data, the server refers to the user's favorite activities and topics from past data and generates a friendly response, such as "You often went to the park on nice days, didn't you? Do you have plans to go today?"

[0609] The response generation device creates an appropriate response to user input, taking into account the user's past actions and conversation history, and presents this response to the user via the response provision device. This system continuously updates its interaction with the user, enabling the AI ​​to behave in a way that allows the user to enjoy a friendly conversation with a deceased family member.

[0610] Therefore, users can utilize the system to receive psychological support, such as alleviating loneliness and providing a sense of security. This invention can be offered as a total communication solution when combined with a communication plan.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The user terminal receives voice commands and text messages and records them as digital data. This data has the function of recording the user's daily conversations and messages along with a timestamp.

[0614] Step 2:

[0615] The device periodically or upon a specified trigger sends the collected data to the server. The data is encrypted and transmitted through a secure channel to ensure its safety.

[0616] Step 3:

[0617] The server stores the received data in a database and organizes it, associating it with each user ID. Here, the data is formatted, denoised, and formatted as needed.

[0618] Step 4:

[0619] The server uses the organized data to train a user-specific AI model using an AI generator. This process employs natural language processing algorithms to learn the user's language patterns and characteristics.

[0620] Step 5:

[0621] When a user makes a new voice or text input through the device, the device immediately issues a command to send that data to the server.

[0622] Step 6:

[0623] The server uses an AI model to generate an appropriate response based on the most recent input received. This includes contextual analysis and sentiment recognition.

[0624] Step 7:

[0625] The generated response is sent to the user terminal via the response provider. The terminal either plays the response aloud using speech synthesis technology or displays it as text on the screen.

[0626] Step 8:

[0627] The user receives a response and continues the conversation. This feedback loop allows the system to continuously update user data and evolve the AI ​​model.

[0628] (Example 1)

[0629] 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".

[0630] In modern information and communication devices, generating personalized responses that accurately reflect the individual characteristics of users is challenging. Furthermore, data noise reduction and standardization, as well as dynamic updates of user profiles, are required to improve the accuracy of user input data and enhance response quality. Providing systems that efficiently perform these processes in a cost-effective manner is also a crucial challenge.

[0631] 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.

[0632] In this invention, the server includes means for denoising and standardizing acquired data, AI generation means for generating a generative AI model tailored to individual users by combining natural language processing and machine learning techniques based on the standardized data, and means for dynamically updating the generative AI model to adapt to the user's recent behavior and preferences. This makes it possible to appropriately generate responses that reflect the individual characteristics of the user and provide high-quality responses to user input in real time.

[0633] A "user" is an entity that uses information and communication equipment to generate voice data or text data.

[0634] "Terminal means" refers to a device that has the function of acquiring voice data and text data from the user and transmitting it to a server.

[0635] A "server device" is a device that receives data transmitted from a terminal device and performs noise reduction and standardization processing on it.

[0636] "Noise reduction" is the process of removing unnecessary information from data to make it suitable for analysis.

[0637] "Standardization" refers to organizing data into a consistent format so that it can be processed uniformly.

[0638] An "AI generation method" is a device that uses natural language processing and machine learning technologies to model user-specific characteristics.

[0639] A "generative AI model" is an algorithmic model that mimics a user's communication style and preferences to generate responses.

[0640] A "response generation means" is a device that manages the process of forming an appropriate response to user input based on a generated AI model.

[0641] "Response provision means" refers to a device or function for transmitting the generated response to the user.

[0642] "Dynamic updating" refers to the process of adapting and modifying the generated AI model in real time or periodically based on the user's latest behavior and preferences.

[0643] "Information and communication equipment" refers to electronic devices used to collect user data and provide responses.

[0644] A "communications lease agreement" refers to a fee structure for the use of information and communication equipment, and is a contractual form related to the business model for which users utilize this system.

[0645] The system of this invention consists of the interaction of a terminal, a server, and a user, and provides the user with a personalized communication experience.

[0646] First, the user gives voice commands using a device such as a smartphone or smart speaker. The device then uses speech recognition technology to convert the voice data received from the user into text data. For example, common industry technologies for speech recognition software include Google Speech-to-Text and other speech recognition libraries.

[0647] Next, the terminal sends the generated text data to the server. The server then performs noise reduction and standardization on the received data. This utilizes speech processing algorithms to ensure the reliability and consistency of the data.

[0648] Furthermore, the server uses natural language processing and machine learning techniques to build a generative AI model with user-specific characteristics. In this process, the server learns important patterns in the data and builds an AI to generate responses that mimic the user's communication style.

[0649] For example, if a user asks, "What should I do today?", the system will refer to their past activity history and generate a response that offers a personalized suggestion, such as, "How about going for a walk today?"

[0650] Examples of prompts in this system include instructions such as, "Generate a response that provides suggestions appropriate to the current situation based on the user's past history."

[0651] Finally, the results generated by the response generation means are presented to the user via the response provision means. This allows the user to enhance the usefulness and relevance of the information they receive from the system.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The user performs voice input using a device such as a smartphone or smart speaker. For example, the user might say, "What's the weather like tomorrow?" This voice data is captured by the device. The input is voice data, and the output is text data. Speech recognition technology is used for this conversion; the speech recognition software on the device converts the voice data into text.

[0655] Step 2:

[0656] The terminal sends the converted text data to the server. Specifically, the text data is transmitted to the server via a secure communication protocol. The input is text data, and the output is raw text data stored on the server.

[0657] Step 3:

[0658] The server performs noise reduction and standardization on the received text data. Specifically, it removes unnecessary symbols and mistranslated words from the text data and prepares it for analysis. The input is raw text data, and the output is processed text data.

[0659] Step 4:

[0660] The server extracts features for a generative AI model based on processed text data. Specifically, it uses natural language processing techniques to extract important keywords and phrases from the data. The input is processed text data, and the output is the feature-extracted data.

[0661] Step 5:

[0662] The server uses the extracted features to build a user-specific generative AI model. Specifically, it uses machine learning algorithms to learn the user's past data patterns and optimize the model. The input is the feature-extracted data, and the output is the generative AI model.

[0663] Step 6:

[0664] The server utilizes a generative AI model to generate responses based on user input. Specifically, it forms responses based on prompts and references past user behavior. The input consists of the user's new input and the generative AI model, while the output is the generated response.

[0665] Step 7:

[0666] The server sends the generated response to the terminal, which then presents it to the user. Specifically, the terminal uses speech synthesis technology to present the generated text to the user as audio. The input is the generated response, and the output is the audio output to the user.

[0667] (Application Example 1)

[0668] 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".

[0669] In today's information and communication environment, users seek greater satisfaction and psychological support by receiving personalized information and content. However, there is a lack of means to provide content appropriately customized based on users' individual hobbies and interests. As a result, users are burdened with the task of selecting information that matches their interests from a vast amount of data.

[0670] 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.

[0671] In this invention, the server includes an information and communication device for collecting user voice and text data, a data processing device for organizing and storing the collected data for each individual user, performing noise reduction and standardization, and an AI generation device that uses the organized and stored data to model user-specific characteristics by combining natural language processing and machine learning. This makes it possible to generate stories based on the user's interests and themes and provide a personalized content experience.

[0672] An "information and communication device" is a device used to collect user voice data and text data.

[0673] A "data processing device" is a device that organizes and stores collected data for each individual user, and performs noise reduction and standardization.

[0674] An "AI generation device" is a device that uses organized and stored data to combine natural language processing and machine learning to model user-specific characteristics.

[0675] A "response generation device" is a device that generates an appropriate response using an AI model that is generated based on input from the user.

[0676] A "response provider" is a device that provides the generated response to the user.

[0677] A "story generation device" is a device that generates stories based on the user's interests and themes.

[0678] A "content distribution device" is a device that delivers generated stories to users.

[0679] The system implementing this invention collects and processes user voice and text data, and generates and provides a user-specific story. The system receives voice input from a smartphone or smart speaker as an information and communication device, and a data processing device organizes the data, performs noise reduction and standardization. The organized data is then used by an AI generation device to create a generative AI model that models the user's unique characteristics using natural language processing and machine learning techniques.

[0680] The system's response generator interprets the current user input based on an AI model and uses a story generator to create a story tailored to the user's interests and themes. This story is then delivered to the user through a content distribution device.

[0681] Specifically, the speech_recognition library is used for voice data conversion and noise reduction, and the GPT-2 model included in the transformers library is used as the natural language generation model for story generation. This enables an interactive experience where users can listen to stories that match their interests based on their voice input.

[0682] For example, if a user says something along the lines of "I want to relax today," a story will be generated based on that history, creating a narrative that is ideal for relaxation. A concrete example of an input prompt to the generation AI model might be text like, "Please create a relaxing story." The story generated based on this prompt will then be delivered as content that resonates with the user's emotional needs.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The device recognizes the user's voice and collects the audio data. Here, the device uses a microphone to acquire the voice and converts it into text data using speech recognition software (e.g., the speech_recognition library). The input is audio data, and the output is text data.

[0686] Step 2:

[0687] The server receives text data, and the data processing unit performs noise reduction and standardization. This removes unnecessary information from the text data and arranges it into a unified format. The input is the converted text data, and the output is the noise-reduced and standardized text.

[0688] Step 3:

[0689] The server inputs the formatted text data into the AI ​​generator and uses machine learning to model user-specific characteristics. Here, natural language processing techniques are used to learn the user's past conversation history and preferences to create a generative AI model. The input is standardized text data, and the output is a user-specific generative AI model.

[0690] Step 4:

[0691] The server uses a story generation device based on a generative AI model to generate stories that match the user's interests. The generation prompt uses a "generative AI model, prompt sentence" to generate stories that reflect the latest user interests and themes. The input is a user-specific generative AI model, and the output is the generated story.

[0692] Step 5:

[0693] The server provides the generated story to the user via a content distribution device. The story is delivered to the terminal in audio or text format, allowing the user to access and enjoy it. The input is the generated story, and the output is the content presented to the user.

[0694] 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.

[0695] The system of this invention is implemented on information and communication devices, such as smartphones and smart speakers, for capturing users' daily communications. The user terminal has the function of collecting voice data and text data in real time and sending it to a server periodically.

[0696] The server organizes and stores the transmitted data using a data processing device, performing noise removal and standardization during this process. After this processing, the data is used by an AI generation device to generate an AI model that reflects the characteristics of each user. The AI ​​model utilizes natural language processing and machine learning technologies to deeply learn the patterns and characteristics of the user's conversation, thereby improving the accuracy of personalized responses.

[0697] Furthermore, this system incorporates an emotion engine that analyzes the user's voice tone and word choice to generate emotional information. This emotional information is then used by the response generation device to derive an appropriate response that is in line with the user's emotional state.

[0698] For example, if a user says to a smart speaker, "I'm really tired today," the device sends this voice as data to a server. The server analyzes the tone of the voice and generates emotional information related to the word "tired." If the emotion engine detects "fatigue," an encouraging response such as "You're always working so hard. Please get some rest" is created by the response generator and finally provided to the user as voice by the response provider.

[0699] In this way, the system enables not only simple response generation through interaction with the user, but also interaction that is sensitive to the user's emotions. The emotion engine, by being reflected in the AI ​​model, improves the quality of the dialogue that takes the user's emotions into account. This invention is expected to be implemented on a wider scale when combined with communication plans.

[0700] The following describes the processing flow.

[0701] Step 1:

[0702] The user terminal receives user voice input and text messages in real time and records them as digital data. This data is stored in a format that includes a timestamp of the user's statements and a device ID.

[0703] Step 2:

[0704] The terminal processes the recorded data in batches at regular intervals and sends it to the server. The transmission uses encryption protocols to ensure data confidentiality while enabling efficient transfer.

[0705] Step 3:

[0706] The server stores the received audio and text data in a database and organizes it by user ID. The data is also denoised and standardized to facilitate subsequent processing.

[0707] Step 4:

[0708] The data processing unit inputs the organized data into an emotion engine and analyzes the user's emotions based on voice tone and word choice. The analysis results are output as emotional information such as "happiness," "fatigue," and "anger."

[0709] Step 5:

[0710] The server's AI generator receives output from the emotion engine and updates the AI ​​model to take emotional information into account. This results in a model that reflects the user's unique characteristics.

[0711] Step 6:

[0712] When a user provides new input, the device sends the data at that moment to the server. This input can be in the form of speech or text.

[0713] Step 7:

[0714] The server processes the latest user input using an AI model and generates responses that reflect emotional information. The response generator produces context-adaptive responses, taking into account the user's past history and emotional state.

[0715] Step 8:

[0716] The generated response is delivered to the user terminal by the response provider, and the terminal either plays it back using speech synthesis technology or displays it as text.

[0717] Step 9:

[0718] The user receives responses through the device and can proceed with the next dialogue. The system continuously provides dialogue that reflects the user's emotions by repeatedly engaging in interaction.

[0719] (Example 2)

[0720] 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".

[0721] In today's information and communication environment, understanding user-specific characteristics and providing appropriate responses tailored to their emotions is crucial for achieving high levels of satisfaction that meet individual needs. However, existing systems struggle to accurately analyze user emotions and provide responses that adapt to dynamically changing emotions and preferences, hindering improvements in the user experience. Solving this challenge is essential.

[0722] 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.

[0723] In this invention, the server includes means for collecting user communication content, processing equipment for individually organizing and processing the collected information, removing unwanted noise and applying standardization, generating equipment for generating a model that combines natural language processing and machine learning techniques based on the organized information, and a device that analyzes the user's emotions during communication and generates a selective response adapted to those emotions. This enables highly accurate responses tailored to the user's individual emotions and characteristics, significantly improving the user experience.

[0724] "User communication content" refers to information such as voice and text transmitted by users through information and communication devices.

[0725] A "collection device" refers to a device equipped with hardware and software functions for acquiring and storing user communication content.

[0726] "Processing equipment" refers to devices and solutions used to organize collected information, remove unnecessary elements, and standardize it.

[0727] A "generation device" refers to a device that utilizes organized information to create user-specific models using natural language processing and machine learning techniques.

[0728] "Analyzing emotions" means identifying and classifying the user's emotional state from the content of their communications.

[0729] A "device with the function of generating a selected response" refers to a device that has the ability to create and provide an optimal response based on analyzed information.

[0730] This invention is implemented through a system combining an information and communication device and a server. Specifically, terminals capable of voice and text input are used to capture the user's daily communications. These terminals include smartphones and smart speakers, and they collect the content of the user's communications in real time.

[0731] The terminal sends the collected audio and text data to the server. On the server, the information is first organized using data processing equipment, noise is removed, and then standardization is performed. Data processing libraries such as Python's Pandas and NumPy are used for this process. The organized data is then analyzed by an AI generator to generate a user-specific natural language processing model. This learns the user's unique conversation patterns and contexts, and builds an individual model.

[0732] Furthermore, the server is equipped with an emotion analysis engine that analyzes the user's emotional state from the communication content. Based on the tone and speed of the voice data and the emotional expressions contained in the text, it infers the user's emotions and prepares a response based on those emotions. The response generation device takes this emotional information into consideration and provides the user with an appropriate response.

[0733] For example, if a user says to a smart speaker, "I'm feeling a little down today," the device acquires this voice as data and sends it to the server. The server uses an emotion engine to analyze the emotion of "feeling down," and an AI model generates a response such as, "Has something happened recently? I'm here to listen if there's anything you want to talk about." This response is then provided to the user through the device, enabling a conversation that is sensitive to their emotions.

[0734] An example of a prompt is when the user inputs "Have you had anything good happen recently?" into the system, and the system generates and provides an appropriate response. In this way, the present invention provides a system that enables sophisticated dialogue based on the user's emotional information.

[0735] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0736] Step 1:

[0737] The terminal collects user communication content in real time through a voice input device or text input. This collected data is obtained as a digital voice signal if it is voice data, or as character data if it is text data. In particular, if voice is input, it is converted into character data using speech recognition software. This is the input, and the output is obtained in the form of digital voice or text.

[0738] Step 2:

[0739] The terminal transmits collected voice and text data to the server via the network. During this process, the data is encrypted and transmitted using a secure protocol (such as HTTPS). This is the input, and the state where the data has safely reached the server is the output.

[0740] Step 3:

[0741] The server processes the received data using a data processing unit. The first steps are noise reduction and standardization. For audio data, background noise is removed, and only the clear audio signal is extracted. For text data, character encoding is standardized and unnecessary spaces are removed. This is the input, and the standardized, noise-free data is the output.

[0742] Step 4:

[0743] The server passes the organized data to the AI ​​generator, which then generates an AI model that captures the user's conversational characteristics. Using natural language processing techniques to analyze the data and machine learning (e.g., neural networks), it generates a user-specific communication model. The input data consists of organized text and audio features, and the output is a custom model for each user.

[0744] Step 5:

[0745] The server uses an AI model and an emotion analysis engine to analyze the user's emotional state and generates an appropriate response using a response generator. The analysis engine extracts emotional information based on voice tone and word choice, and this information is reflected in the AI ​​model. Based on this input emotional data, the response generator generates a response in text format. The output is a response sentence appropriate for the user.

[0746] Step 6:

[0747] The terminal receives the generated response and provides it to the user as either voice or text. In the case of voice output, speech synthesis technology is used to convert the text into voice data, which is then played through the speaker. The input is the response data from the server, and the output is a natural-sounding response to the user.

[0748] (Application Example 2)

[0749] 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".

[0750] In modern information and communication technology, accurately analyzing user emotions and ensuring security is a crucial challenge. However, existing systems struggle to detect changes in user emotions or unnatural communication patterns in real time and to quickly warn of potential dangers or scams. To address this challenge, a system is needed that analyzes emotions and automatically detects abnormal communication.

[0751] 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.

[0752] In this invention, the server includes an emotion analysis engine, means for analyzing emotions from voice and text information and detecting abnormal emotions or unnatural communication patterns, security means for warning of potential dangers or fraud and notifying trusted contacts as necessary, and an AI generator for modeling consumer-specific characteristics. This makes it possible to ensure user safety and proactively detect potential dangers.

[0753] "Audio information" refers to data obtained from the voice spoken by the user, and is used to analyze the user's emotions and intentions.

[0754] "Textual information" refers to text data entered or submitted by users, which is then analyzed using natural language processing.

[0755] A "communication device" is a device used to collect voice and text information and transmit it to a server, and includes smartphones and smart speakers.

[0756] "Data processing means" refers to a system that organizes and stores collected information, and performs noise removal and standardization.

[0757] An "AI generation device" is a device that combines natural language understanding and machine learning to model consumer-specific characteristics and generate responses tailored to individual users.

[0758] A "response generation means" is a device that creates an appropriate response using a model generated by an AI generation device based on input from the user.

[0759] "Response provision means" refers to a device or system for providing the generated response to the user.

[0760] An "emotion analysis engine" is a program or device that analyzes a user's emotions from voice and text information and identifies their emotional state.

[0761] A "security measure" is a system that detects abnormal emotions or unnatural communication patterns, warns users of potential dangers, and notifies trusted contacts.

[0762] This invention is a system that performs emotion analysis and detects abnormal communication patterns using user voice and text information. In its implementation, a smartphone or smart speaker is used as the communication device. These communication devices are responsible for collecting the user's voice and text information and transmitting it to the server.

[0763] The server has data processing capabilities to organize and store the received information, remove noise, and standardize it. Next, an AI generation device is used to model consumer-specific characteristics from the collected information and generate an appropriate response. The response generation means creates a user-optimized response based on this AI model and outputs it to the user via the response provision means.

[0764] The emotion analysis engine analyzes information to identify the user's emotional state. Security measures detect abnormal emotions and unnatural communication patterns, warning the user of potential dangers and notifying trusted contacts when necessary.

[0765] For example, if a user says "I'm anxious" to a smart speaker, the server analyzes the voice information, and the emotion analysis engine identifies "anxiety." Security measures include issuing a warning to the user and contacting registered family members if the server determines that this deviates from normal emotional patterns. A specific example of a prompt would be, "Please create prompts for an AI model to analyze the normal communication of elderly individuals and detect changes in their emotions."

[0766] Thus, in order to concretely implement the invention, a system is needed that analyzes the user's emotional state and takes appropriate action. This aims to ensure user safety and prevent potential dangers.

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] The terminal collects the user's voice and text information in real time. This information is transmitted to the server via a communication device. The terminal receives the input and converts the voice to text using speech recognition software. The output at this stage is the text information.

[0770] Step 2:

[0771] The server organizes and stores the acquired information using data processing tools, and performs noise removal and standardization. The input is textual information from the terminal, which is processed with data cleansing software to obtain a clean dataset. The output of this step is organized and standardized data.

[0772] Step 3:

[0773] The sentiment analysis engine on the server analyzes the user's emotional state using organized, standardized data. Calculations are performed based on sentiment analysis algorithms, with a clean dataset as input. The output is metadata indicating the user's emotional state.

[0774] Step 4:

[0775] The server's AI generator models user-specific characteristics, including emotional state metadata, and uses the generated AI model to produce appropriate responses. Here, consumer-specific future predictions and conditioned reflex models are combined to design a response framework. The output of this step is a customized response message.

[0776] Step 5:

[0777] The response generation means sends a customized response message to the user through the response provision means. The output information from the server is returned to the terminal and provided to the user as voice or text via a smart speaker, smartphone, etc. The feedback to the user is the final output of this step.

[0778] Step 6:

[0779] The security measures detect abnormal patterns and potential dangers from analyzed emotional states. The input is emotional state metadata, and an anomaly detection algorithm makes a rapid judgment. The output is a warning notification about potential dangers, which is sent to trusted contacts as needed.

[0780] By having each step work in coordination, a system is created that ensures user safety, improves the accuracy of individual responses, and manages potential risks.

[0781] 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.

[0782] 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.

[0783] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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."

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] The following is further disclosed regarding the embodiments described above.

[0803] (Claim 1)

[0804] Information and communication equipment that collects user voice data and text data,

[0805] A data processing device that organizes and stores collected data for each individual user, and performs noise reduction and standardization.

[0806] An AI generation device that uses organized and stored data to model user-specific features by combining natural language processing and machine learning,

[0807] A response generation device that generates an appropriate response using an AI model generated based on user input,

[0808] A response provider that provides the generated response to the user,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, comprising means for continuously updating the AI ​​model for each user and maintaining its up-to-date state.

[0812] (Claim 3)

[0813] The system according to claim 1, comprising means provided as a usage fee for information and communication equipment in combination with a communication plan.

[0814] "Example 1"

[0815] (Claim 1)

[0816] A terminal means for acquiring user voice data and text data,

[0817] A server means for denoising and standardizing the acquired data,

[0818] An AI generation method that combines natural language processing and machine learning techniques based on standardized data to generate a generative AI model tailored to individual users,

[0819] A response generation means that generates a response based on user input using the generated AI model,

[0820] A response provision means that presents the generated response to the user,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, comprising means for dynamically updating a generated AI model based on the user's profile and historical data to adapt to the user's recent behavior and preferences.

[0824] (Claim 3)

[0825] The system according to claim 1, which is provided with means for using a communication equipment usage fee in combination with a communication rental contract.

[0826] "Application Example 1"

[0827] (Claim 1)

[0828] An information and communication device that collects user voice data and text data,

[0829] A data processing device that organizes and stores collected data for each individual user, and performs noise reduction and standardization.

[0830] An AI generation device that uses organized and stored data to model user-specific features by combining natural language processing and machine learning,

[0831] A response generation device that generates an appropriate response using an AI model generated based on user input,

[0832] A response provider that provides the generated response to the user,

[0833] A story generation device that generates stories based on the user's interests and themes,

[0834] A content distribution device that delivers generated stories to users,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, comprising means for continuously updating the AI ​​model for each user and maintaining its up-to-date state.

[0838] (Claim 3)

[0839] The system according to claim 1, comprising means provided as a usage fee for information and communication equipment in combination with a communication plan.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A device for collecting user communication content,

[0843] A processing device that individually organizes and processes the collected information, removes unwanted noise, and applies standardization,

[0844] A generative device that generates a model combining natural language processing and machine learning techniques based on organized information,

[0845] A device that analyzes the emotions of a user during communication and generates a selective response adapted to those emotions,

[0846] A device that outputs the generated response,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, comprising means for dynamically updating user-specific models and maintaining the latest state.

[0850] (Claim 3)

[0851] The system according to claim 1, comprising means provided in conjunction with a communication contract as consideration for the use of information and communication equipment.

[0852] "Application example 2 when combining with an emotional engine"

[0853] (Claim 1)

[0854] A communication device that collects user voice and text information,

[0855] A data processing means that organizes and stores the collected information for each individual consumer, and performs noise removal and standardization,

[0856] An AI generation device that uses organized and stored information to model consumer-specific characteristics by combining natural language understanding and machine learning,

[0857] A response generation means that generates an appropriate response using a generated AI model based on user input,

[0858] A response providing means that provides the generated response to the user,

[0859] A means of analyzing emotions from voice and text information, including an emotion analysis engine, to detect abnormal emotions and unnatural communication patterns,

[0860] Security measures that warn of potential dangers and scams and notify trusted contacts as needed,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising means for continuously updating the AI ​​model for each user and maintaining its up-to-date state.

[0864] (Claim 3)

[0865] The system according to claim 1, comprising means provided as a usage fee for communication equipment in combination with a communication plan. [Explanation of Symbols]

[0866] 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. Information and communication equipment that collects user voice data and text data, A data processing device that organizes and stores collected data for each individual user, and performs noise reduction and standardization, An AI generation device that uses organized and stored data to model user-specific features by combining natural language processing and machine learning, A response generation device that generates an appropriate response using an AI model generated based on user input, A response provider that provides the generated response to the user, A system that includes this.

2. The system according to claim 1, comprising means for continuously updating the AI ​​model for each user and maintaining the latest state.

3. The system according to claim 1, comprising means provided as a usage fee for information and communication equipment in combination with a communication plan.

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