system

Abstract

We provide the system. [Solution] A speech recognition means that converts voice input into text data, A control means that analyzes the intent of voice input using a generative artificial intelligence model and generates a response, A speech synthesis means that converts the response into audio data and outputs it, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many modern elderly people have difficulty using smartphones and Internet technologies. As a result, problems such as the widening of the information gap and concerns about being left behind due to the onlineization of public services have arisen. Also, the need to protect the elderly from crimes such as special fraud is increasing. Under such circumstances, there is a demand for a system that enables the elderly to more easily manage health care, asset management, and social communication and live their daily lives with peace of mind.

Means for Solving the Problems

[0005] The present invention provides a system comprising speech recognition means for converting voice input into text data, control means for analyzing the intent of voice input and generating a response using a generative artificial intelligence model, and speech synthesis means for converting the response into voice data and outputting it. Furthermore, by including database access means for managing health information, and natural language processing means for acquiring information in accordance with the user's health-related instructions and generating fraud detection and warning messages, it is possible to comprehensively solve the challenges faced by the elderly.

[0006] "Speech recognition means" refers to technology that receives speech input and converts that speech into text data.

[0007] A "generative artificial intelligence model" refers to a machine learning model that generates natural language responses or actions based on given input data.

[0008] "Control means" refers to technology that analyzes the intent of voice input and generates an appropriate response based on that analysis.

[0009] "Speech synthesis means" refers to technology that converts text data into speech data and outputs it.

[0010] "Database access means" refers to technologies for connecting to a database and obtaining or managing necessary information.

[0011] "Natural language processing means" refers to technology that analyzes natural language input and performs specific processing based on its content. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled 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, etc.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface that includes 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), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a personal AI support system designed to enable elderly people to more comfortably manage various aspects of their daily lives. The system mainly consists of means for speech recognition, generative artificial intelligence models, control, speech synthesis, database access, and natural language processing.

[0034] System Overview

[0035] Users can obtain various information and execute commands by speaking to a device (such as a smart speaker) installed in their home. Voice input is converted into text data by the device's speech recognition system, and this text data is sent to a server. A generative artificial intelligence model on the server analyzes the text data and interprets the user's intent. If necessary, health information and schedule information are obtained via a database access system, and the generated response is sent from the server to the device, where it is responded to the user as voice data by a speech synthesis system.

[0036] Specific examples of usage scenarios

[0037] 1. Health management support:

[0038] The user speaks to the device, saying, "Tell me yesterday's blood pressure measurement results." The device converts the voice to text and sends that data to the server.

[0039] The server analyzes the text and retrieves relevant health data from the database. It then generates the results in a format such as "Yesterday's blood pressure was 120 / 80" and sends it back to the terminal.

[0040] The device converts received messages into audio and notifies the user.

[0041] 2. Fraud prevention:

[0042] When a user asks, "Check if this call is a scam," regarding a suspicious phone call, the device sends this information to the server.

[0043] The server analyzes the content of the phone call using natural language processing to identify the possibility of fraud. If the possibility is high, it generates and sends a message warning, "This may be a scam."

[0044] The device will notify the user of the warning via voice.

[0045] This system can provide comprehensive support for the daily lives of the elderly and offer a sense of security by promptly providing necessary information.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user speaks into the device and inputs specific commands or questions by voice. For example, they might say, "Tell me yesterday's blood pressure reading."

[0049] Step 2:

[0050] The device detects the user's voice input and converts the voice into text data using its built-in speech recognition function. This converted text data is then prepared for transmission to the server.

[0051] Step 3:

[0052] The terminal sends the generated text data to the server via the internet. This communication is secure and fast.

[0053] Step 4:

[0054] The server inputs the received text data into a generative artificial intelligence model to interpret the user's intent. In this case, it determines that the user wants to "check their blood pressure measurement results."

[0055] Step 5:

[0056] The server, following its intent, uses database access means to access the health management database for the elderly and retrieves the relevant blood pressure measurement results.

[0057] Step 6:

[0058] The server generates a response message for the user based on the acquired data. For example, it might generate a message such as, "Your blood pressure yesterday was 120 / 80."

[0059] Step 7:

[0060] The server sends the generated response message to the terminal in text format.

[0061] Step 8:

[0062] The device converts received text messages into speech data using its text-to-speech function.

[0063] Step 9:

[0064] The device plays the generated audio data through its speaker and provides the user with the response, "Yesterday's blood pressure was 120 / 80."

[0065] (Example 1)

[0066] 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."

[0067] In the face of the growing need for comprehensive information processing systems to support the daily lives of the elderly, the ability to quickly and accurately acquire information and respond from voice input is essential. However, conventional systems have limitations in the speed and accuracy of voice recognition, AI analysis, and database access, making it difficult to provide users with real-time information. As a result, users cannot quickly obtain the information they need, leading to problems such as insufficient support, particularly in health management and security.

[0068] 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.

[0069] In this invention, the server includes speech recognition means for converting voice input into text data, control means for analyzing the intent of voice input using generation-based artificial intelligence means, acquiring information, and generating a response, speech synthesis means for converting the response into voice data and outputting it, and data transmission means for sending and receiving information between the terminal and the server via data communication. This enables rapid processing from voice input to information acquisition and response generation, allowing for the provision of appropriate information to the user in real time, thereby effectively supporting the lives of the elderly.

[0070] "Speech recognition means" refers to technology that analyzes speech input and converts its content into text data.

[0071] "Generation-based artificial intelligence means" refers to a system that uses artificial intelligence technology to process received data and instructions, and generates optimal responses and information for the user.

[0072] A "control means" is a component that plays a central role in operating the entire system and enables data analysis, processing, and coordination with other means.

[0073] "Speech synthesis means" refers to a technology that converts text data into speech data and outputs it to the user as speech information.

[0074] "Data transmission means" refers to communication methods and technologies for exchanging data between a terminal and a server, enabling the sending and receiving of information.

[0075] "Means of accessing a storage medium" refers to technology that accesses a storage medium in order to store, retrieve, or retrieve specific information.

[0076] "Natural language processing means" refers to functions that use technologies for analyzing, understanding, and generating human language on a computer to perform specific tasks.

[0077] This invention is a personal AI support system designed to help elderly people manage their daily lives more comfortably. The system has the ability to obtain information through a terminal installed in the user's home and execute voice commands.

[0078] The device uses a microphone to capture the user's voice input. Instructions and questions uttered by the user are converted into text data via speech recognition technology. Speech recognition APIs are often used for this purpose.

[0079] The converted text data is sent to a server via the internet. The server analyzes the data using generative artificial intelligence methods. This AI model utilizes a system capable of accurate multi-purpose natural language processing. The AI ​​extracts the user's intent from the text, obtains necessary information, and generates responses.

[0080] During the response generation process, the server accesses storage media as needed to retrieve the latest health information and schedules. For example, if a user asks, "What's on my schedule this week?", the AI ​​searches for the corresponding information and prepares a specific answer such as, "You have a doctor's appointment on Tuesday."

[0081] The response returned from the server is sent to the terminal. The terminal uses speech synthesis to convert the text into audio data and transmits it to the user through its speaker. At this time, the synthesized audio is played back using the terminal's speaker system.

[0082] As a concrete example, consider a scenario where a user wants to know information about their health. The user asks the device, "What was my blood pressure yesterday?" The device converts the voice into text and sends it to the server. The server consults a health information database to retrieve recent measurement results and generates a response such as, "Your blood pressure yesterday was 120 / 80." This response is then spoken aloud by the device and heard by the user.

[0083] As an example of a prompt, one could give the AI ​​model the instruction, "When the user asks about the next scheduled appointment, retrieve the relevant information from the storage medium and generate a response."

[0084] This system is intended to go beyond simply providing information and offer comprehensive support to older adults to improve their quality of life.

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

[0086] Step 1:

[0087] The user inputs questions and instructions by voice into the terminal. The user's voice is captured via the microphone. This voice data becomes the input.

[0088] Step 2:

[0089] The device converts the captured audio data into text data using speech recognition. Specifically, it analyzes the audio using a speech recognition API and outputs the corresponding text. This text data becomes the input for the next step.

[0090] Step 3:

[0091] The terminal sends the converted text data to the server via the internet. Here, the text data is passed to the server as input data. This data transmission is performed securely using the HTTPS protocol.

[0092] Step 4:

[0093] The server analyzes the received text data using a generating AI model. The AI ​​model extracts the user's intent from the input text and determines the necessary information. As a result of the analysis, search queries and actions to be taken are output.

[0094] Step 5:

[0095] The server retrieves relevant information from the storage medium based on the analysis results. During this process, it uses database queries to search for and output the requested data. For example, user health information and schedule information may be retrieved.

[0096] Step 6:

[0097] The server generates a response based on the acquired information. Using a generative AI model, it constructs a message to convey to the user in natural language and outputs a text-based response. This response is used in the next step.

[0098] Step 7:

[0099] The server sends the generated response text to the terminal. This is also done via secure data communication, and the text data is delivered to the terminal.

[0100] Step 8:

[0101] The terminal converts received text data into audio data using speech synthesis technology. It uses speech synthesis technology to change text into speech, and then outputs that speech to the user through the speaker.

[0102] Step 9:

[0103] The user receives voice messages output from the device. This provides timely information necessary for the user, thereby supporting their daily life.

[0104] (Application Example 1)

[0105] 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."

[0106] In the daily lives of the elderly, there is a need for intuitive and rapid information retrieval and instruction execution via voice, as well as improved daily peace of mind regarding health management and fraud prevention. However, existing systems often struggle to achieve sufficient voice recognition accuracy and flexible responses. Furthermore, there is a lack of user-friendly interfaces that comprehensively provide these functions.

[0107] 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.

[0108] In this invention, the server includes a voice processing means for converting voice information into text information, a control device that analyzes the intent of the voice information using a generative machine learning model and creates a response, a voice generation means for converting the response into voice information and outputting it, and a data exchange means for providing location information and schedule information. This makes it possible for elderly people to efficiently manage their health information and prevent fraud using voice in their daily lives, while obtaining information with intuitive operation.

[0109] "Speech processing means" refers to a device or function that uses technology to accurately convert speech information into text information.

[0110] A "generative machine learning model" is an advanced computational model used to analyze speech and text data, understand the user's intent, and generate appropriate responses.

[0111] A "control device" is a computer-based device used to manage and process the generated responses.

[0112] "Speech generation means" refers to technologies and devices that convert text information into speech information and convey it to users in an easily understandable way.

[0113] A "data exchange means" is a method for managing and providing various types of data, such as location information and schedule information, and for communicating with other systems and devices.

[0114] This invention provides a personal AI support system that enables elderly people to live their daily lives more safely and comfortably.

[0115] First, the user initiates operation by speaking into a terminal installed in their home. The terminal converts the spoken information into text using voice processing technology. This converted text is then sent to a server. The server uses a generative machine learning model to analyze the user's intent from the text data. In this process, the latest natural language processing technology is used as the generative machine learning model to achieve more accurate intent analysis.

[0116] The control unit generates an appropriate response based on the analysis results. For example, if a user asks, "When is my next hospital appointment?", it retrieves schedule information from the database and generates a response such as, "Your next hospital appointment is next Monday."

[0117] The generated response is converted into voice information by a voice generation device and transmitted from the terminal to the user. In this way, the user can obtain the necessary information through voice communication without requiring advanced operations.

[0118] Furthermore, through data exchange mechanisms, the device can acquire external location and schedule information in real time and update the data as needed. The device is designed so that users can receive the same services even when they are away from home, enhancing convenience.

[0119] For example, if a user gives a voice command such as "I want to check my schedule for this week," the terminal sends the command to the server, which generates a voice response such as "Your schedule for this week includes a hospital appointment on Monday and a medication pick-up on Thursday." An example of a prompt might be "Please check the user's schedule and respond with important appointments for next week." This system is an innovative solution that combines advanced analysis based on a generative AI model with a simple interface.

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

[0121] Step 1:

[0122] The user inputs voice into the device. The input voice is captured by the device's microphone.

[0123] Step 2:

[0124] The terminal uses voice processing to convert acquired voice information into text information. Specifically, it uses voice recognition technology to convert voice signals into text data. This text data becomes the input for the next process.

[0125] Step 3:

[0126] The terminal sends the generated text information to the server. This text data serves as the basis for further analysis and processing on the server.

[0127] Step 4:

[0128] The server uses a generative machine learning model to analyze the received text data. This involves data computation that analyzes the text data using natural language processing techniques to identify the user's intent and the information they need.

[0129] Step 5:

[0130] The server, via the control unit, generates an appropriate response based on the analyzed data. This response generation is a data processing step that involves referencing a database to retrieve necessary information and creating an answer to the user's query.

[0131] Step 6:

[0132] The server sends the generated response to the terminal. At this time, the response data is transferred again in text format.

[0133] Step 7:

[0134] The device converts received text data into audio information using a speech generation method. Specifically, it utilizes speech synthesis technology to output the text as audio that the user can understand.

[0135] Step 8:

[0136] The device communicates voice responses to the user, allowing the user to obtain necessary information without visual interaction.

[0137] 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.

[0138] This invention combines an emotion engine with a personal AI supporter system that provides comprehensive support for the lives of the elderly based on user voice input. The system consists of voice recognition, a generative artificial intelligence model, control, speech synthesis, database access, natural language processing, and an emotion engine, enabling responses that take into account the user's emotional state.

[0139] System Overview

[0140] When a user speaks to a device (such as a smart speaker), the device captures voice data. Voice recognition converts the voice into text data, and an emotion engine analyzes the emotions from this text. The text and emotion data are sent to a server, where a generative artificial intelligence model generates a response combining the user's intent and emotions. During this process, necessary information (e.g., health data) is obtained via database access and analyzed by natural language processing. The final response is then synthesized into speech and provided to the user.

[0141] Specific examples of usage scenarios

[0142] 1. Integrating health management and emotional care:

[0143] If a user says, "I've been feeling a bit tired lately, so I'd like to check my health status," the device sends voice data to the server.

[0144] The server uses an emotion engine to detect fatigue and, accordingly, retrieves health management information, including advice on stress management and rest. It generates messages such as, "Your blood pressure has been normal recently, but we recommend you relax a little."

[0145] The device communicates this response to the user via voice.

[0146] 2. Reducing user anxiety in fraud prevention:

[0147] If a user asks in a worried tone, "Please check if this call is a scam," the emotion engine will detect anxiety.

[0148] The server checks for fraud information and generates a reassuring message such as, "There are no reports of fraud associated with this phone number, but caution is advised."

[0149] The device plays this for the user to alleviate their anxiety.

[0150] By incorporating an emotion engine in this way, the system can take into account the user's emotional state and provide more personalized support.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The user makes a specific voice input to the device, such as, "I've been feeling a bit tired lately, so I'd like to check my health status."

[0154] Step 2:

[0155] The device receives voice input and converts the voice into text data using its built-in speech recognition capabilities. This text data is also passed to the emotion engine.

[0156] Step 3:

[0157] The device uses an emotion engine to analyze the text extracted from the audio and identify the emotion (in this case, fatigue) contained in the user's statements.

[0158] Step 4:

[0159] The device sends text data and sentiment information to the server. This communication takes place in real time, enabling a rapid response.

[0160] Step 5:

[0161] The server inputs the received text and emotional information into a generative artificial intelligence model to accurately understand the user's request. It understands that the user wants to check their health status and is experiencing fatigue.

[0162] Step 6:

[0163] The server uses database access to retrieve the user's health information (e.g., latest blood pressure data) and generates additional advice tailored to their emotional state (e.g., suggestions for relaxation).

[0164] Step 7:

[0165] The server sends the generated response to the terminal in text format. This may include advice such as, "Your blood pressure has been normal recently, but we recommend that you relax."

[0166] Step 8:

[0167] The terminal converts text messages received from the server into speech data using its text-to-speech function. This speech data is then played back through the speaker.

[0168] Step 9:

[0169] The device uses synthesized voice to respond to the user with messages such as, "Your blood pressure has been normal recently, but we recommend that you relax a little," providing the user with information and advice.

[0170] (Example 2)

[0171] 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".

[0172] In modern society, comprehensively supporting the lives of the elderly requires not only providing information but also appropriately understanding their emotional state and responding accordingly. However, conventional systems provide uniform information without considering emotions, limiting the improvement of the user experience. Furthermore, with the increase in fraud, there is a demand for highly secure communication, but systems that can address this have been limited.

[0173] 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.

[0174] In this invention, the server includes a speech recognition device that converts speech into text data, a control device that analyzes the intent of speech input using a generative machine learning model and generates a response based on emotional information, a speech synthesis device that converts the response into acoustic data and outputs it, and an emotion analysis device that analyzes emotions. This makes it possible to provide personalized responses that take into account the user's emotions, improve the quality of life for the elderly, and realize safe communication.

[0175] A "speech recognition device" is a device that uses technology to convert speech data into text data.

[0176] A "generative machine learning model" is a model that has a learning algorithm used to analyze the intent behind input data and generate an appropriate response based on that analysis.

[0177] A "control device" is a device that analyzes the intent behind voice input and generates a response based on emotional information.

[0178] A "speech synthesis device" is a device that converts a generated response into acoustic data and outputs it as speech.

[0179] An "emotion analysis device" is a device that analyzes the user's emotional state based on the input information.

[0180] A "storage device access device" is a device used to retrieve necessary information from a storage device that manages specific data.

[0181] A "natural language processing device" is a device that has the technology to analyze text written in natural language and identify or generate specific information.

[0182] This invention is a system that provides comprehensive support for the lives of elderly people based on user voice input. This system consists of a voice recognition device, a control device employing a generative machine learning model, a voice synthesis device, an emotion analysis device, a memory access device, and a natural language processing device.

[0183] First, the user gives verbal instructions to a device (for example, a smart speaker). The device captures this voice using a microphone and converts it into text data using a speech recognition device. At this stage, widely available software can be used for speech recognition technology.

[0184] Next, the device sends string data to the server. The server uses an emotion analyzer to detect the user's emotional state and analyzes the emotion and intent using a generative machine learning model (e.g., common generative AI techniques). A response is generated based on this information. The generated response includes content tailored to the user's emotional state, such as advice to alleviate stress or fatigue.

[0185] Subsequently, the server uses a storage access device to retrieve necessary data, such as the user's health information, and performs further analysis via a natural language processing device. Finally, the generated response is converted into acoustic data through a speech synthesizer and provided to the user from the terminal.

[0186] As a concrete example, consider a scenario where a user says, "I've been feeling a bit tired lately, so I'd like to check my health status." The system analyzes the user's voice and detects fatigue using an emotion analyzer. The control unit generates a message such as, "Your blood pressure has been normal recently, but we recommend you relax a little," and the terminal delivers this message aloud.

[0187] Examples of prompts include, "Generate a conversation to understand the user's emotions and provide health advice to reduce stress," and "Create a response that provides appropriate reassurance when the user is feeling anxious."

[0188] This invention enables personalized information delivery based on the user's emotions and intentions, thereby improving the quality of life for the elderly.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user speaks into the device, for example, saying, "I've been feeling a bit tired lately, so I'd like to check my health status." This voice is captured by the device's microphone. The input is the user's voice data, and the output is this voice data. The device picks up the user's voice from the background noise through the microphone.

[0192] Step 2:

[0193] The terminal uses a speech recognition device to convert captured audio data into text data. The input is the user's voice data, and the output is text data. Specifically, the speech recognition software converts the audio to text in real time.

[0194] Step 3:

[0195] The terminal sends the generated string data to the server. The server uses an emotion analysis device to analyze the user's emotional state from the string data. The input is string data, and the output is the analysis result including emotional information. The emotion analysis software generates an emotional vector and uses it to detect if the user is feeling fatigued.

[0196] Step 4:

[0197] The server inputs emotional information and text data as prompts into a generative AI model, which then generates a response. The input consists of emotional information and text data, while the output is semantically adapted response data. Specifically, the generative AI model generates responses based on emotions and intentions in real time.

[0198] Step 5:

[0199] The server uses a storage access device to retrieve relevant information from a health database and analyzes this data using a natural language processing unit. The input is health-related data, and the output is detailed response information generated based on the analysis results. The server retrieves the user's past blood pressure data via database access and incorporates appropriate health advice.

[0200] Step 6:

[0201] The server converts the final response into speech data using a speech synthesizer and sends it to the terminal. The input is the response data, and the output is speech data. Speech synthesis software converts it into natural-sounding spoken language and generates the speech data.

[0202] Step 7:

[0203] The device plays audio data and provides responses to the user. For example, it might say, "Your blood pressure has been normal recently, but I recommend you relax a little." The input is audio data, and the output is an audio output to the user. High-quality audio responses are delivered to the user through the speaker.

[0204] (Application Example 2)

[0205] 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".

[0206] The need for health management, fraud prevention, and loneliness reduction among the elderly is increasing. However, managing these individually is difficult, and comprehensive and emotionally sensitive support is needed to improve their quality of life.

[0207] 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.

[0208] In this invention, the server includes speech recognition means for converting voice input into text data, control means for analyzing the intent and emotional state of the voice input using a generative artificial intelligence model, and emotion analysis means for analyzing emotions. This enables health management, fraud prevention, and reduction of feelings of loneliness through dialogue.

[0209] "Speech recognition means" refers to a device or method for converting speech input into text data.

[0210] A "generative artificial intelligence model" is an intelligent system that learns patterns from data and automatically generates responses that are appropriate to the user's intentions and circumstances.

[0211] "Control means" refers to methods or devices that adjust and manage various functions of a system and generate responses in accordance with user instructions.

[0212] "Speech synthesis means" refers to technologies and devices for converting text data into speech data and outputting it.

[0213] "Emotional analysis tools" refer to technologies and devices used to analyze a user's emotional state based on their statements.

[0214] A "dialogue provision means" refers to a device or method that supports communication with users and has the function of reducing users' feelings of loneliness through dialogue.

[0215] "Database access means" refers to the technologies and mechanisms used to retrieve necessary information from a database and process it.

[0216] "Natural language processing means" refers to technologies and devices that enable computers to understand, analyze, and generate natural language.

[0217] The system of the present invention functions as a personal AI supporter to assist the lives of the elderly. When a user inputs instructions into the terminal by voice, the voice is converted into text data by a speech recognition means. The text data is analyzed using a generative artificial intelligence model to generate a response that corresponds to the user's intentions and emotional state.

[0218] This control is performed by the server using a speech recognition API, sentiment analysis engine, natural language processing engine, and speech synthesis API. Specifically, speech recognition is handled by Google® Cloud Speech-to-Text API, sentiment analysis by IBM Watson® Tone Analyzer, automatic response generation by OpenAI® GPT-3®, and speech synthesis by Amazon Polly.

[0219] The emotion analysis tool detects the user's emotional state from their speech, and the dialogue provider enables communication with the user based on that emotion. For example, if a user says, "I feel lonely because I don't talk to my family much," the server analyzes the emotion as loneliness and generates a response such as, "I've detected the emotion of loneliness. Would you like to chat with me for a bit? Tell me about the news or your hobbies."

[0220] An example of a prompt is: "The user is talking about their recent state in a lonely voice. Generate a conversation that takes loneliness into consideration."

[0221] This system will enable comprehensive support for health management, fraud prevention, and reducing feelings of loneliness, thereby improving the quality of life for the elderly.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The user inputs instructions to the device using voice. The device captures this voice and sends it to a speech recognition API. The input is voice data, and the output is text data generated by the speech recognition API.

[0225] Step 2:

[0226] The server passes the received text data to the sentiment analysis engine. Here, the emotional state of the text data is analyzed. The input is text data, and the output is emotional data obtained by the sentiment analysis engine.

[0227] Step 3:

[0228] The server uses a generative artificial intelligence model to combine text data and sentiment data to generate responses based on the user's intent. The input is text data and sentiment data, and the output is text data as a response to the user.

[0229] Step 4:

[0230] The server sends the generated response text data to the speech synthesis API to form the audio data. The input is the response text data, and the output is the audio data generated by the speech synthesis API.

[0231] Step 5:

[0232] The terminal plays the acquired audio data to the user. The user receives the audio response provided by the system and enters further instructions as needed. This process completes the interaction with the user.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention is a personal AI support system designed to enable elderly people to more comfortably manage various aspects of their daily lives. The system mainly consists of means for speech recognition, generative artificial intelligence models, control, speech synthesis, database access, and natural language processing.

[0250] System Overview

[0251] Users can obtain various information and execute commands by speaking to a device (such as a smart speaker) installed in their home. Voice input is converted into text data by the device's speech recognition system, and this text data is sent to a server. A generative artificial intelligence model on the server analyzes the text data and interprets the user's intent. If necessary, health information and schedule information are obtained via a database access system, and the generated response is sent from the server to the device, where it is responded to the user as voice data by a speech synthesis system.

[0252] Specific examples of usage scenarios

[0253] 1. Health management support:

[0254] The user speaks to the device, saying, "Tell me yesterday's blood pressure measurement results." The device converts the voice to text and sends that data to the server.

[0255] The server analyzes the text and retrieves relevant health data from the database. It then generates the results in a format such as "Yesterday's blood pressure was 120 / 80" and sends it back to the terminal.

[0256] The device converts received messages into audio and notifies the user.

[0257] 2. Fraud prevention:

[0258] When a user asks, "Check if this call is a scam," regarding a suspicious phone call, the device sends this information to the server.

[0259] The server analyzes the content of the phone call using natural language processing to identify the possibility of fraud. If the possibility is high, it generates and sends a message warning, "This may be a scam."

[0260] The device will notify the user of the warning via voice.

[0261] This system can provide comprehensive support for the daily lives of the elderly and offer a sense of security by promptly providing necessary information.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] The user speaks into the device and inputs specific commands or questions by voice. For example, they might say, "Tell me yesterday's blood pressure reading."

[0265] Step 2:

[0266] The device detects the user's voice input and converts the voice into text data using its built-in speech recognition function. This converted text data is then prepared for transmission to the server.

[0267] Step 3:

[0268] The terminal sends the generated text data to the server via the internet. This communication is secure and fast.

[0269] Step 4:

[0270] The server inputs the received text data into a generative artificial intelligence model to interpret the user's intent. In this case, it determines that the user wants to "check their blood pressure measurement results."

[0271] Step 5:

[0272] The server, following its intent, uses database access means to access the health management database for the elderly and retrieves the relevant blood pressure measurement results.

[0273] Step 6:

[0274] Based on the acquired data, the server generates a response message for the user. As an example, it generates a message such as "Yesterday's blood pressure was 120 / 80".

[0275] Step 7:

[0276] The server sends the generated response message to the terminal in text format.

[0277] Step 8:

[0278] The terminal converts the received text message into audio data using the speech synthesis function.

[0279] Step 9:

[0280] The terminal plays the generated audio data through the speaker and provides a response to the user saying "Yesterday's blood pressure was 120 / 80".

[0281] (Example 1)

[0282] Next, 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".

[0283] In the midst of the demand for a comprehensive information processing system to support the daily lives of the elderly, the ability to quickly and accurately acquire and respond to information from voice input is essential. However, in conventional systems, there are problems with the speed and accuracy of voice recognition, analysis by artificial intelligence, and database access, making it difficult to provide real-time information to users. For this reason, users cannot obtain the necessary information immediately, and there is a problem that support in terms of health management and security is not sufficient.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0285] In this invention, the server includes: speech recognition means for converting voice input into text data; control means for analyzing the intention of the voice input using artificial intelligence means based on generation, acquiring information, and generating a response; speech synthesis means for converting the response into voice data and outputting it; and data transmission means for transmitting and receiving information between the terminal and the server via data communication. Thereby, it is possible to quickly perform information acquisition and response generation from voice input, enable appropriate real-time information provision to the user, and effectively support the lives of the elderly.

[0286] The "speech recognition means" is a technology for analyzing voice input and converting its content into text data.

[0287] The "artificial intelligence means based on generation" is a mechanism that uses artificial intelligence technology to perform processing based on received data and instructions, and generate optimal responses and information for the user.

[0288] The "control means" is a component that performs the central function for operating the entire system and enables data analysis, processing, and cooperation with other means.

[0289] The "speech synthesis means" is a technology for converting text data into voice data and outputting it as voice information to the user.

[0290] The "data transmission means" refers to the communication method and technology for exchanging data between the terminal and the server, and enables the transmission and reception of information.

[0291] The "memory medium access means" refers to the technology for accessing a memory medium to store, acquire, and query specific information.

[0292] The "natural language processing means" is a function for performing specific tasks using technologies for analyzing, understanding, and generating human language on a computer.

[0293] This invention is a personal AI support system designed to help elderly people manage their daily lives more comfortably. The system has the ability to obtain information through a terminal installed in the user's home and execute voice commands.

[0294] The device uses a microphone to capture the user's voice input. Instructions and questions uttered by the user are converted into text data via speech recognition technology. Speech recognition APIs are often used for this purpose.

[0295] The converted text data is sent to a server via the internet. The server analyzes the data using generative artificial intelligence methods. This AI model utilizes a system capable of accurate multi-purpose natural language processing. The AI ​​extracts the user's intent from the text, obtains necessary information, and generates responses.

[0296] During the response generation process, the server accesses storage media as needed to retrieve the latest health information and schedules. For example, if a user asks, "What's on my schedule this week?", the AI ​​searches for the corresponding information and prepares a specific answer such as, "You have a doctor's appointment on Tuesday."

[0297] The response returned from the server is sent to the terminal. The terminal uses speech synthesis to convert the text into audio data and transmits it to the user through its speaker. At this time, the synthesized audio is played back using the terminal's speaker system.

[0298] As a concrete example, consider a scenario where a user wants to know information about their health. The user asks the device, "What was my blood pressure yesterday?" The device converts the voice into text and sends it to the server. The server consults a health information database to retrieve recent measurement results and generates a response such as, "Your blood pressure yesterday was 120 / 80." This response is then spoken aloud by the device and heard by the user.

[0299] As an example of a prompt sentence, it is conceivable to give an instruction such as "When the user asks about the next hospital visit schedule, please obtain relevant information from the storage medium and generate a response." to the generation AI model.

[0300] This system aims to provide comprehensive support for improving the quality of life for the elderly, going beyond mere information provision.

[0301] The flow of the specific process in Example 1 will be described using FIG. 11.

[0302] Step 1:

[0303] The user inputs questions or instructions verbally towards the terminal. The user's voice is captured through the microphone. This voice data serves as the input.

[0304] Step 2:

[0305] The terminal converts the captured voice data into text data by means of voice recognition. Specifically, the voice is analyzed using a voice recognition API, and the corresponding text is output. This text data becomes the input for the next step.

[0306] Step 3:

[0307] The terminal transmits the converted text data to the server via the Internet. Here, the text data is passed to the server as input data. This data transmission is carried out securely using the HTTPS protocol.

[0308] Step 4:

[0309] The server analyzes the received text data by means of the generation AI model. The AI model extracts the user's intention from the input text and determines the necessary information. As a result of the analysis, a search query or the action to be executed is output.

[0310] Step 5:

[0311] The server retrieves relevant information from the storage medium based on the analysis results. During this process, it uses database queries to search for and output the requested data. For example, user health information and schedule information may be retrieved.

[0312] Step 6:

[0313] The server generates a response based on the acquired information. Using a generative AI model, it constructs a message to convey to the user in natural language and outputs a text-based response. This response is used in the next step.

[0314] Step 7:

[0315] The server sends the generated response text to the terminal. This is also done via secure data communication, and the text data is delivered to the terminal.

[0316] Step 8:

[0317] The terminal converts received text data into audio data using speech synthesis technology. It uses speech synthesis technology to change text into speech, and then outputs that speech to the user through the speaker.

[0318] Step 9:

[0319] The user receives voice messages output from the device. This provides timely information necessary for the user, thereby supporting their daily life.

[0320] (Application Example 1)

[0321] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0322] In the daily lives of the elderly, there is a need for intuitive and rapid information retrieval and instruction execution via voice, as well as improved daily peace of mind regarding health management and fraud prevention. However, existing systems often struggle to achieve sufficient voice recognition accuracy and flexible responses. Furthermore, there is a lack of user-friendly interfaces that comprehensively provide these functions.

[0323] 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.

[0324] In this invention, the server includes a voice processing means for converting voice information into text information, a control device that analyzes the intent of the voice information using a generative machine learning model and creates a response, a voice generation means for converting the response into voice information and outputting it, and a data exchange means for providing location information and schedule information. This makes it possible for elderly people to efficiently manage their health information and prevent fraud using voice in their daily lives, while obtaining information with intuitive operation.

[0325] "Speech processing means" refers to a device or function that uses technology to accurately convert speech information into text information.

[0326] A "generative machine learning model" is an advanced computational model used to analyze speech and text data, understand the user's intent, and generate appropriate responses.

[0327] A "control device" is a computer-based device used to manage and process the generated responses.

[0328] "Speech generation means" refers to technologies and devices that convert text information into speech information and convey it to users in an easily understandable way.

[0329] A "data exchange means" is a method for managing and providing various types of data, such as location information and schedule information, and for communicating with other systems and devices.

[0330] This invention provides a personal AI support system that enables elderly people to live their daily lives more safely and comfortably.

[0331] First, the user initiates operation by speaking into a terminal installed in their home. The terminal converts the spoken information into text using voice processing technology. This converted text is then sent to a server. The server uses a generative machine learning model to analyze the user's intent from the text data. In this process, the latest natural language processing technology is used as the generative machine learning model to achieve more accurate intent analysis.

[0332] The control unit generates an appropriate response based on the analysis results. For example, if a user asks, "When is my next hospital appointment?", it retrieves schedule information from the database and generates a response such as, "Your next hospital appointment is next Monday."

[0333] The generated response is converted into voice information by a voice generation device and transmitted from the terminal to the user. In this way, the user can obtain the necessary information through voice communication without requiring advanced operations.

[0334] Furthermore, through data exchange mechanisms, the device can acquire external location and schedule information in real time and update the data as needed. The device is designed so that users can receive the same services even when they are away from home, enhancing convenience.

[0335] For example, if a user gives a voice command such as "I want to check my schedule for this week," the terminal sends the command to the server, which generates a voice response such as "Your schedule for this week includes a hospital appointment on Monday and a medication pick-up on Thursday." An example of a prompt might be "Please check the user's schedule and respond with important appointments for next week." This system is an innovative solution that combines advanced analysis based on a generative AI model with a simple interface.

[0336] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0337] Step 1:

[0338] The user inputs voice into the device. The input voice is captured by the device's microphone.

[0339] Step 2:

[0340] The terminal uses voice processing to convert acquired voice information into text information. Specifically, it uses voice recognition technology to convert voice signals into text data. This text data becomes the input for the next process.

[0341] Step 3:

[0342] The terminal sends the generated text information to the server. This text data serves as the basis for further analysis and processing on the server.

[0343] Step 4:

[0344] The server uses a generative machine learning model to analyze the received text data. This involves data computation that analyzes the text data using natural language processing techniques to identify the user's intent and the information they need.

[0345] Step 5:

[0346] The server, via the control unit, generates an appropriate response based on the analyzed data. This response generation is a data processing step that involves referencing a database to retrieve necessary information and creating an answer to the user's query.

[0347] Step 6:

[0348] The server sends the generated response to the terminal. At this time, the response data is transferred again in text format.

[0349] Step 7:

[0350] The device converts received text data into audio information using a speech generation method. Specifically, it utilizes speech synthesis technology to output the text as audio that the user can understand.

[0351] Step 8:

[0352] The device communicates voice responses to the user, allowing the user to obtain necessary information without visual interaction.

[0353] 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.

[0354] This invention combines an emotion engine with a personal AI supporter system that provides comprehensive support for the lives of the elderly based on user voice input. The system consists of voice recognition, a generative artificial intelligence model, control, speech synthesis, database access, natural language processing, and an emotion engine, enabling responses that take into account the user's emotional state.

[0355] System Overview

[0356] When a user speaks to a device (such as a smart speaker), the device captures voice data. Voice recognition converts the voice into text data, and an emotion engine analyzes the emotions from this text. The text and emotion data are sent to a server, where a generative artificial intelligence model generates a response combining the user's intent and emotions. During this process, necessary information (e.g., health data) is obtained via database access and analyzed by natural language processing. The final response is then synthesized into speech and provided to the user.

[0357] Specific examples of usage scenarios

[0358] 1. Integrating health management and emotional care:

[0359] If a user says, "I've been feeling a bit tired lately, so I'd like to check my health status," the device sends voice data to the server.

[0360] The server uses an emotion engine to detect fatigue and, accordingly, retrieves health management information, including advice on stress management and rest. It generates messages such as, "Your blood pressure has been normal recently, but we recommend you relax a little."

[0361] The device communicates this response to the user via voice.

[0362] 2. Reducing user anxiety in fraud prevention:

[0363] If a user asks in a worried tone, "Please check if this call is a scam," the emotion engine will detect anxiety.

[0364] The server checks for fraud information and generates a reassuring message such as, "There are no reports of fraud associated with this phone number, but caution is advised."

[0365] The device plays this for the user to alleviate their anxiety.

[0366] By incorporating an emotion engine in this way, the system can take into account the user's emotional state and provide more personalized support.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] The user makes a specific voice input to the device, such as, "I've been feeling a bit tired lately, so I'd like to check my health status."

[0370] Step 2:

[0371] The device receives voice input and converts the voice into text data using its built-in speech recognition capabilities. This text data is also passed to the emotion engine.

[0372] Step 3:

[0373] The device uses an emotion engine to analyze the text extracted from the audio and identify the emotion (in this case, fatigue) contained in the user's statements.

[0374] Step 4:

[0375] The device sends text data and sentiment information to the server. This communication takes place in real time, enabling a rapid response.

[0376] Step 5:

[0377] The server inputs the received text and emotional information into a generative artificial intelligence model to accurately understand the user's request. It understands that the user wants to check their health status and is experiencing fatigue.

[0378] Step 6:

[0379] The server uses database access to retrieve the user's health information (e.g., latest blood pressure data) and generates additional advice tailored to their emotional state (e.g., suggestions for relaxation).

[0380] Step 7:

[0381] The server sends the generated response to the terminal in text format. This may include advice such as, "Your blood pressure has been normal recently, but we recommend that you relax."

[0382] Step 8:

[0383] The terminal converts text messages received from the server into speech data using its text-to-speech function. This speech data is then played back through the speaker.

[0384] Step 9:

[0385] The device uses synthesized voice to respond to the user with messages such as, "Your blood pressure has been normal recently, but we recommend that you relax a little," providing the user with information and advice.

[0386] (Example 2)

[0387] 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".

[0388] In modern society, comprehensively supporting the lives of the elderly requires not only providing information but also appropriately understanding their emotional state and responding accordingly. However, conventional systems provide uniform information without considering emotions, limiting the improvement of the user experience. Furthermore, with the increase in fraud, there is a demand for highly secure communication, but systems that can address this have been limited.

[0389] 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.

[0390] In this invention, the server includes a speech recognition device that converts speech into text data, a control device that analyzes the intent of speech input using a generative machine learning model and generates a response based on emotional information, a speech synthesis device that converts the response into acoustic data and outputs it, and an emotion analysis device that analyzes emotions. This makes it possible to provide personalized responses that take into account the user's emotions, improve the quality of life for the elderly, and realize safe communication.

[0391] A "speech recognition device" is a device that uses technology to convert speech data into text data.

[0392] A "generative machine learning model" is a model that has a learning algorithm used to analyze the intent behind input data and generate an appropriate response based on that analysis.

[0393] A "control device" is a device that analyzes the intent behind voice input and generates a response based on emotional information.

[0394] A "speech synthesis device" is a device that converts a generated response into acoustic data and outputs it as speech.

[0395] An "emotion analysis device" is a device that analyzes the user's emotional state based on the input information.

[0396] A "storage device access device" is a device used to retrieve necessary information from a storage device that manages specific data.

[0397] A "natural language processing device" is a device that has the technology to analyze text written in natural language and identify or generate specific information.

[0398] This invention is a system that provides comprehensive support for the lives of elderly people based on user voice input. This system consists of a voice recognition device, a control device employing a generative machine learning model, a voice synthesis device, an emotion analysis device, a memory access device, and a natural language processing device.

[0399] First, the user gives verbal instructions to a device (for example, a smart speaker). The device captures this voice using a microphone and converts it into text data using a speech recognition device. At this stage, widely available software can be used for speech recognition technology.

[0400] Next, the device sends string data to the server. The server uses an emotion analyzer to detect the user's emotional state and analyzes the emotion and intent using a generative machine learning model (e.g., common generative AI techniques). A response is generated based on this information. The generated response includes content tailored to the user's emotional state, such as advice to alleviate stress or fatigue.

[0401] Subsequently, the server uses a storage access device to retrieve necessary data, such as the user's health information, and performs further analysis via a natural language processing device. Finally, the generated response is converted into acoustic data through a speech synthesizer and provided to the user from the terminal.

[0402] As a concrete example, consider a scenario where a user says, "I've been feeling a bit tired lately, so I'd like to check my health status." The system analyzes the user's voice and detects fatigue using an emotion analyzer. The control unit generates a message such as, "Your blood pressure has been normal recently, but we recommend you relax a little," and the terminal delivers this message aloud.

[0403] Examples of prompts include, "Generate a conversation to understand the user's emotions and provide health advice to reduce stress," and "Create a response that provides appropriate reassurance when the user is feeling anxious."

[0404] This invention enables personalized information delivery based on the user's emotions and intentions, thereby improving the quality of life for the elderly.

[0405] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0406] Step 1:

[0407] The user speaks into the device, for example, saying, "I've been feeling a bit tired lately, so I'd like to check my health status." This voice is captured by the device's microphone. The input is the user's voice data, and the output is this voice data. The device picks up the user's voice from the background noise through the microphone.

[0408] Step 2:

[0409] The terminal uses a speech recognition device to convert captured audio data into text data. The input is the user's voice data, and the output is text data. Specifically, the speech recognition software converts the audio to text in real time.

[0410] Step 3:

[0411] The terminal sends the generated string data to the server. The server uses an emotion analysis device to analyze the user's emotional state from the string data. The input is string data, and the output is the analysis result including emotional information. The emotion analysis software generates an emotional vector and uses it to detect if the user is feeling fatigued.

[0412] Step 4:

[0413] The server inputs emotional information and text data as prompts into a generative AI model, which then generates a response. The input consists of emotional information and text data, while the output is semantically adapted response data. Specifically, the generative AI model generates responses based on emotions and intentions in real time.

[0414] Step 5:

[0415] The server uses a storage access device to retrieve relevant information from a health database and analyzes this data using a natural language processing unit. The input is health-related data, and the output is detailed response information generated based on the analysis results. The server retrieves the user's past blood pressure data via database access and incorporates appropriate health advice.

[0416] Step 6:

[0417] The server converts the final response into speech data using a speech synthesizer and sends it to the terminal. The input is the response data, and the output is speech data. Speech synthesis software converts it into natural-sounding spoken language and generates the speech data.

[0418] Step 7:

[0419] The device plays audio data and provides responses to the user. For example, it might say, "Your blood pressure has been normal recently, but I recommend you relax a little." The input is audio data, and the output is an audio output to the user. High-quality audio responses are delivered to the user through the speaker.

[0420] (Application Example 2)

[0421] 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 as the "terminal".

[0422] The need for health management, fraud prevention, and loneliness reduction among the elderly is increasing. However, managing these individually is difficult, and comprehensive and emotionally sensitive support is needed to improve their quality of life.

[0423] 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.

[0424] In this invention, the server includes speech recognition means for converting voice input into text data, control means for analyzing the intent and emotional state of the voice input using a generative artificial intelligence model, and emotion analysis means for analyzing emotions. This enables health management, fraud prevention, and reduction of feelings of loneliness through dialogue.

[0425] "Speech recognition means" refers to a device or method for converting speech input into text data.

[0426] A "generative artificial intelligence model" is an intelligent system that learns patterns from data and automatically generates responses that are appropriate to the user's intentions and circumstances.

[0427] "Control means" refers to methods or devices that adjust and manage various functions of a system and generate responses in accordance with user instructions.

[0428] "Speech synthesis means" refers to technologies and devices for converting text data into speech data and outputting it.

[0429] "Emotional analysis tools" refer to technologies and devices used to analyze a user's emotional state based on their statements.

[0430] A "dialogue provision means" refers to a device or method that supports communication with users and has the function of reducing users' feelings of loneliness through dialogue.

[0431] "Database access means" refers to the technologies and mechanisms used to retrieve necessary information from a database and process it.

[0432] "Natural language processing means" refers to technologies and devices that enable computers to understand, analyze, and generate natural language.

[0433] The system of the present invention functions as a personal AI supporter to assist the lives of the elderly. When a user inputs instructions into the terminal by voice, the voice is converted into text data by a speech recognition means. The text data is analyzed using a generative artificial intelligence model to generate a response that corresponds to the user's intentions and emotional state.

[0434] This control is performed by the server using a speech recognition API, sentiment analysis engine, natural language processing engine, and speech synthesis API. Specifically, speech recognition is handled by the Google Cloud Speech-to-Text API, sentiment analysis by IBM Watson Tone Analyzer, automated response generation by OpenAI GPT-3, and speech synthesis by Amazon Polly.

[0435] The emotion analysis tool detects the user's emotional state from their speech, and the dialogue provider enables communication with the user based on that emotion. For example, if a user says, "I feel lonely because I don't talk to my family much," the server analyzes the emotion as loneliness and generates a response such as, "I've detected the emotion of loneliness. Would you like to chat with me for a bit? Tell me about the news or your hobbies."

[0436] An example of a prompt is: "The user is talking about their recent state in a lonely voice. Generate a conversation that takes loneliness into consideration."

[0437] This system will enable comprehensive support for health management, fraud prevention, and reducing feelings of loneliness, thereby improving the quality of life for the elderly.

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] The user inputs instructions to the device using voice. The device captures this voice and sends it to a speech recognition API. The input is voice data, and the output is text data generated by the speech recognition API.

[0441] Step 2:

[0442] The server passes the received text data to the sentiment analysis engine. Here, the emotional state of the text data is analyzed. The input is text data, and the output is emotional data obtained by the sentiment analysis engine.

[0443] Step 3:

[0444] The server uses a generative artificial intelligence model to combine text data and sentiment data to generate responses based on the user's intent. The input is text data and sentiment data, and the output is text data as a response to the user.

[0445] Step 4:

[0446] The server sends the generated response text data to the speech synthesis API to form the audio data. The input is the response text data, and the output is the audio data generated by the speech synthesis API.

[0447] Step 5:

[0448] The terminal plays the acquired audio data to the user. The user receives the audio response provided by the system and enters further instructions as needed. This process completes the interaction with the user.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] [Third Embodiment]

[0453] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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".

[0465] This invention is a personal AI support system designed to enable elderly people to more comfortably manage various aspects of their daily lives. The system mainly consists of means for speech recognition, generative artificial intelligence models, control, speech synthesis, database access, and natural language processing.

[0466] System Overview

[0467] Users can obtain various information and execute commands by speaking to a device (such as a smart speaker) installed in their home. Voice input is converted into text data by the device's speech recognition system, and this text data is sent to a server. A generative artificial intelligence model on the server analyzes the text data and interprets the user's intent. If necessary, health information and schedule information are obtained via a database access system, and the generated response is sent from the server to the device, where it is responded to the user as voice data by a speech synthesis system.

[0468] Specific examples of usage scenarios

[0469] 1. Health management support:

[0470] The user speaks to the device, saying, "Tell me yesterday's blood pressure measurement results." The device converts the voice to text and sends that data to the server.

[0471] The server analyzes the text and retrieves relevant health data from the database. It then generates the results in a format such as "Yesterday's blood pressure was 120 / 80" and sends it back to the terminal.

[0472] The device converts received messages into audio and notifies the user.

[0473] 2. Fraud prevention:

[0474] When a user asks, "Check if this call is a scam," regarding a suspicious phone call, the device sends this information to the server.

[0475] The server analyzes the content of the phone call using natural language processing to identify the possibility of fraud. If the possibility is high, it generates and sends a message warning, "This may be a scam."

[0476] The device will notify the user of the warning via voice.

[0477] This system can provide comprehensive support for the daily lives of the elderly and offer a sense of security by promptly providing necessary information.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The user speaks into the device and inputs specific commands or questions by voice. For example, they might say, "Tell me yesterday's blood pressure reading."

[0481] Step 2:

[0482] The device detects the user's voice input and converts the voice into text data using its built-in speech recognition function. This converted text data is then prepared for transmission to the server.

[0483] Step 3:

[0484] The terminal sends the generated text data to the server via the internet. This communication is secure and fast.

[0485] Step 4:

[0486] The server inputs the received text data into a generative artificial intelligence model to interpret the user's intent. In this case, it determines that the user wants to "check their blood pressure measurement results."

[0487] Step 5:

[0488] The server, following its intent, uses database access means to access the health management database for the elderly and retrieves the relevant blood pressure measurement results.

[0489] Step 6:

[0490] The server generates a response message for the user based on the acquired data. For example, it might generate a message such as, "Your blood pressure yesterday was 120 / 80."

[0491] Step 7:

[0492] The server sends the generated response message to the terminal in text format.

[0493] Step 8:

[0494] The device converts received text messages into speech data using its text-to-speech function.

[0495] Step 9:

[0496] The device plays the generated audio data through its speaker and provides the user with the response, "Yesterday's blood pressure was 120 / 80."

[0497] (Example 1)

[0498] 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."

[0499] In the face of the growing need for comprehensive information processing systems to support the daily lives of the elderly, the ability to quickly and accurately acquire information and respond from voice input is essential. However, conventional systems have limitations in the speed and accuracy of voice recognition, AI analysis, and database access, making it difficult to provide users with real-time information. As a result, users cannot quickly obtain the information they need, leading to problems such as insufficient support, particularly in health management and security.

[0500] 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.

[0501] In this invention, the server includes speech recognition means for converting voice input into text data, control means for analyzing the intent of voice input using generation-based artificial intelligence means, acquiring information, and generating a response, speech synthesis means for converting the response into voice data and outputting it, and data transmission means for sending and receiving information between the terminal and the server via data communication. This enables rapid processing from voice input to information acquisition and response generation, allowing for the provision of appropriate information to the user in real time, thereby effectively supporting the lives of the elderly.

[0502] "Speech recognition means" refers to technology that analyzes speech input and converts its content into text data.

[0503] "Generation-based artificial intelligence means" refers to a system that uses artificial intelligence technology to process received data and instructions, and generates optimal responses and information for the user.

[0504] A "control means" is a component that plays a central role in operating the entire system and enables data analysis, processing, and coordination with other means.

[0505] "Speech synthesis means" refers to a technology that converts text data into speech data and outputs it to the user as speech information.

[0506] "Data transmission means" refers to communication methods and technologies for exchanging data between a terminal and a server, enabling the sending and receiving of information.

[0507] "Means of accessing a storage medium" refers to technology that accesses a storage medium in order to store, retrieve, or retrieve specific information.

[0508] "Natural language processing means" refers to functions that use technologies for analyzing, understanding, and generating human language on a computer to perform specific tasks.

[0509] This invention is a personal AI support system designed to help elderly people manage their daily lives more comfortably. The system has the ability to obtain information through a terminal installed in the user's home and execute voice commands.

[0510] The device uses a microphone to capture the user's voice input. Instructions and questions uttered by the user are converted into text data via speech recognition technology. Speech recognition APIs are often used for this purpose.

[0511] The converted text data is sent to a server via the internet. The server analyzes the data using generative artificial intelligence methods. This AI model utilizes a system capable of accurate multi-purpose natural language processing. The AI ​​extracts the user's intent from the text, obtains necessary information, and generates responses.

[0512] During the response generation process, the server accesses storage media as needed to retrieve the latest health information and schedules. For example, if a user asks, "What's on my schedule this week?", the AI ​​searches for the corresponding information and prepares a specific answer such as, "You have a doctor's appointment on Tuesday."

[0513] The response returned from the server is sent to the terminal. The terminal uses speech synthesis to convert the text into audio data and transmits it to the user through its speaker. At this time, the synthesized audio is played back using the terminal's speaker system.

[0514] As a concrete example, consider a scenario where a user wants to know information about their health. The user asks the device, "What was my blood pressure yesterday?" The device converts the voice into text and sends it to the server. The server consults a health information database to retrieve recent measurement results and generates a response such as, "Your blood pressure yesterday was 120 / 80." This response is then spoken aloud by the device and heard by the user.

[0515] As an example of a prompt, one could give the AI ​​model the instruction, "When the user asks about the next scheduled appointment, retrieve the relevant information from the storage medium and generate a response."

[0516] This system is intended to go beyond simply providing information and offer comprehensive support to older adults to improve their quality of life.

[0517] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0518] Step 1:

[0519] The user inputs questions and instructions by voice into the terminal. The user's voice is captured via the microphone. This voice data becomes the input.

[0520] Step 2:

[0521] The device converts the captured audio data into text data using speech recognition. Specifically, it analyzes the audio using a speech recognition API and outputs the corresponding text. This text data becomes the input for the next step.

[0522] Step 3:

[0523] The terminal sends the converted text data to the server via the internet. Here, the text data is passed to the server as input data. This data transmission is performed securely using the HTTPS protocol.

[0524] Step 4:

[0525] The server analyzes the received text data using a generating AI model. The AI ​​model extracts the user's intent from the input text and determines the necessary information. As a result of the analysis, search queries and actions to be taken are output.

[0526] Step 5:

[0527] The server retrieves relevant information from the storage medium based on the analysis results. During this process, it uses database queries to search for and output the requested data. For example, user health information and schedule information may be retrieved.

[0528] Step 6:

[0529] The server generates a response based on the acquired information. Using a generative AI model, it constructs a message to convey to the user in natural language and outputs a text-based response. This response is used in the next step.

[0530] Step 7:

[0531] The server sends the generated response text to the terminal. This is also done via secure data communication, and the text data is delivered to the terminal.

[0532] Step 8:

[0533] The terminal converts received text data into audio data using speech synthesis technology. It uses speech synthesis technology to change text into speech, and then outputs that speech to the user through the speaker.

[0534] Step 9:

[0535] The user receives voice messages output from the device. This provides timely information necessary for the user, thereby supporting their daily life.

[0536] (Application Example 1)

[0537] 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."

[0538] In the daily lives of the elderly, there is a need for intuitive and rapid information retrieval and instruction execution via voice, as well as improved daily peace of mind regarding health management and fraud prevention. However, existing systems often struggle to achieve sufficient voice recognition accuracy and flexible responses. Furthermore, there is a lack of user-friendly interfaces that comprehensively provide these functions.

[0539] 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.

[0540] In this invention, the server includes a voice processing means for converting voice information into text information, a control device that analyzes the intent of the voice information using a generative machine learning model and creates a response, a voice generation means for converting the response into voice information and outputting it, and a data exchange means for providing location information and schedule information. This makes it possible for elderly people to efficiently manage their health information and prevent fraud using voice in their daily lives, while obtaining information with intuitive operation.

[0541] "Speech processing means" refers to a device or function that uses technology to accurately convert speech information into text information.

[0542] A "generative machine learning model" is an advanced computational model used to analyze speech and text data, understand the user's intent, and generate appropriate responses.

[0543] A "control device" is a computer-based device used to manage and process the generated responses.

[0544] "Speech generation means" refers to technologies and devices that convert text information into speech information and convey it to users in an easily understandable way.

[0545] A "data exchange means" is a method for managing and providing various types of data, such as location information and schedule information, and for communicating with other systems and devices.

[0546] This invention provides a personal AI support system that enables elderly people to live their daily lives more safely and comfortably.

[0547] First, the user initiates operation by speaking into a terminal installed in their home. The terminal converts the spoken information into text using voice processing technology. This converted text is then sent to a server. The server uses a generative machine learning model to analyze the user's intent from the text data. In this process, the latest natural language processing technology is used as the generative machine learning model to achieve more accurate intent analysis.

[0548] The control unit generates an appropriate response based on the analysis results. For example, if a user asks, "When is my next hospital appointment?", it retrieves schedule information from the database and generates a response such as, "Your next hospital appointment is next Monday."

[0549] The generated response is converted into voice information by a voice generation device and transmitted from the terminal to the user. In this way, the user can obtain the necessary information through voice communication without requiring advanced operations.

[0550] Furthermore, through data exchange mechanisms, the device can acquire external location and schedule information in real time and update the data as needed. The device is designed so that users can receive the same services even when they are away from home, enhancing convenience.

[0551] For example, if a user gives a voice command such as "I want to check my schedule for this week," the terminal sends the command to the server, which generates a voice response such as "Your schedule for this week includes a hospital appointment on Monday and a medication pick-up on Thursday." An example of a prompt might be "Please check the user's schedule and respond with important appointments for next week." This system is an innovative solution that combines advanced analysis based on a generative AI model with a simple interface.

[0552] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0553] Step 1:

[0554] The user inputs voice into the device. The input voice is captured by the device's microphone.

[0555] Step 2:

[0556] The terminal uses voice processing to convert acquired voice information into text information. Specifically, it uses voice recognition technology to convert voice signals into text data. This text data becomes the input for the next process.

[0557] Step 3:

[0558] The terminal sends the generated text information to the server. This text data serves as the basis for further analysis and processing on the server.

[0559] Step 4:

[0560] The server uses a generative machine learning model to analyze the received text data. This involves data computation that analyzes the text data using natural language processing techniques to identify the user's intent and the information they need.

[0561] Step 5:

[0562] The server, via the control unit, generates an appropriate response based on the analyzed data. This response generation is a data processing step that involves referencing a database to retrieve necessary information and creating an answer to the user's query.

[0563] Step 6:

[0564] The server sends the generated response to the terminal. At this time, the response data is transferred again in text format.

[0565] Step 7:

[0566] The device converts received text data into audio information using a speech generation method. Specifically, it utilizes speech synthesis technology to output the text as audio that the user can understand.

[0567] Step 8:

[0568] The device communicates voice responses to the user, allowing the user to obtain necessary information without visual interaction.

[0569] 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.

[0570] This invention combines an emotion engine with a personal AI supporter system that provides comprehensive support for the lives of the elderly based on user voice input. The system consists of voice recognition, a generative artificial intelligence model, control, speech synthesis, database access, natural language processing, and an emotion engine, enabling responses that take into account the user's emotional state.

[0571] System Overview

[0572] When a user speaks to a device (such as a smart speaker), the device captures voice data. Voice recognition converts the voice into text data, and an emotion engine analyzes the emotions from this text. The text and emotion data are sent to a server, where a generative artificial intelligence model generates a response combining the user's intent and emotions. During this process, necessary information (e.g., health data) is obtained via database access and analyzed by natural language processing. The final response is then synthesized into speech and provided to the user.

[0573] Specific examples of usage scenarios

[0574] 1. Integrating health management and emotional care:

[0575] If a user says, "I've been feeling a bit tired lately, so I'd like to check my health status," the device sends voice data to the server.

[0576] The server uses an emotion engine to detect fatigue and, accordingly, retrieves health management information, including advice on stress management and rest. It generates messages such as, "Your blood pressure has been normal recently, but we recommend you relax a little."

[0577] The device communicates this response to the user via voice.

[0578] 2. Reducing user anxiety in fraud prevention:

[0579] If a user asks in a worried tone, "Please check if this call is a scam," the emotion engine will detect anxiety.

[0580] The server checks for fraud information and generates a reassuring message such as, "There are no reports of fraud associated with this phone number, but caution is advised."

[0581] The device plays this for the user to alleviate their anxiety.

[0582] By incorporating an emotion engine in this way, the system can take into account the user's emotional state and provide more personalized support.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The user makes a specific voice input to the device, such as, "I've been feeling a bit tired lately, so I'd like to check my health status."

[0586] Step 2:

[0587] The device receives voice input and converts the voice into text data using its built-in speech recognition capabilities. This text data is also passed to the emotion engine.

[0588] Step 3:

[0589] The device uses an emotion engine to analyze the text extracted from the audio and identify the emotion (in this case, fatigue) contained in the user's statements.

[0590] Step 4:

[0591] The device sends text data and sentiment information to the server. This communication takes place in real time, enabling a rapid response.

[0592] Step 5:

[0593] The server inputs the received text and emotional information into a generative artificial intelligence model to accurately understand the user's request. It understands that the user wants to check their health status and is experiencing fatigue.

[0594] Step 6:

[0595] The server uses database access to retrieve the user's health information (e.g., latest blood pressure data) and generates additional advice tailored to their emotional state (e.g., suggestions for relaxation).

[0596] Step 7:

[0597] The server sends the generated response to the terminal in text format. This may include advice such as, "Your blood pressure has been normal recently, but we recommend that you relax."

[0598] Step 8:

[0599] The terminal converts text messages received from the server into speech data using its text-to-speech function. This speech data is then played back through the speaker.

[0600] Step 9:

[0601] The device uses synthesized voice to respond to the user with messages such as, "Your blood pressure has been normal recently, but we recommend that you relax a little," providing the user with information and advice.

[0602] (Example 2)

[0603] 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."

[0604] In modern society, comprehensively supporting the lives of the elderly requires not only providing information but also appropriately understanding their emotional state and responding accordingly. However, conventional systems provide uniform information without considering emotions, limiting the improvement of the user experience. Furthermore, with the increase in fraud, there is a demand for highly secure communication, but systems that can address this have been limited.

[0605] 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.

[0606] In this invention, the server includes a speech recognition device that converts speech into text data, a control device that analyzes the intent of speech input using a generative machine learning model and generates a response based on emotional information, a speech synthesis device that converts the response into acoustic data and outputs it, and an emotion analysis device that analyzes emotions. This makes it possible to provide personalized responses that take into account the user's emotions, improve the quality of life for the elderly, and realize safe communication.

[0607] A "speech recognition device" is a device that uses technology to convert speech data into text data.

[0608] A "generative machine learning model" is a model that has a learning algorithm used to analyze the intent behind input data and generate an appropriate response based on that analysis.

[0609] A "control device" is a device that analyzes the intent behind voice input and generates a response based on emotional information.

[0610] A "speech synthesis device" is a device that converts a generated response into acoustic data and outputs it as speech.

[0611] An "emotion analysis device" is a device that analyzes the user's emotional state based on the input information.

[0612] A "storage device access device" is a device used to retrieve necessary information from a storage device that manages specific data.

[0613] A "natural language processing device" is a device that has the technology to analyze text written in natural language and identify or generate specific information.

[0614] This invention is a system that provides comprehensive support for the lives of elderly people based on user voice input. This system consists of a voice recognition device, a control device employing a generative machine learning model, a voice synthesis device, an emotion analysis device, a memory access device, and a natural language processing device.

[0615] First, the user gives verbal instructions to a device (for example, a smart speaker). The device captures this voice using a microphone and converts it into text data using a speech recognition device. At this stage, widely available software can be used for speech recognition technology.

[0616] Next, the device sends string data to the server. The server uses an emotion analyzer to detect the user's emotional state and analyzes the emotion and intent using a generative machine learning model (e.g., common generative AI techniques). A response is generated based on this information. The generated response includes content tailored to the user's emotional state, such as advice to alleviate stress or fatigue.

[0617] Subsequently, the server uses a storage access device to retrieve necessary data, such as the user's health information, and performs further analysis via a natural language processing device. Finally, the generated response is converted into acoustic data through a speech synthesizer and provided to the user from the terminal.

[0618] As a concrete example, consider a scenario where a user says, "I've been feeling a bit tired lately, so I'd like to check my health status." The system analyzes the user's voice and detects fatigue using an emotion analyzer. The control unit generates a message such as, "Your blood pressure has been normal recently, but we recommend you relax a little," and the terminal delivers this message aloud.

[0619] Examples of prompts include, "Generate a conversation to understand the user's emotions and provide health advice to reduce stress," and "Create a response that provides appropriate reassurance when the user is feeling anxious."

[0620] This invention enables personalized information delivery based on the user's emotions and intentions, thereby improving the quality of life for the elderly.

[0621] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0622] Step 1:

[0623] The user speaks into the device, for example, saying, "I've been feeling a bit tired lately, so I'd like to check my health status." This voice is captured by the device's microphone. The input is the user's voice data, and the output is this voice data. The device picks up the user's voice from the background noise through the microphone.

[0624] Step 2:

[0625] The terminal uses a speech recognition device to convert captured audio data into text data. The input is the user's voice data, and the output is text data. Specifically, the speech recognition software converts the audio to text in real time.

[0626] Step 3:

[0627] The terminal sends the generated string data to the server. The server uses an emotion analysis device to analyze the user's emotional state from the string data. The input is string data, and the output is the analysis result including emotional information. The emotion analysis software generates an emotional vector and uses it to detect if the user is feeling fatigued.

[0628] Step 4:

[0629] The server inputs emotional information and text data as prompts into a generative AI model, which then generates a response. The input consists of emotional information and text data, while the output is semantically adapted response data. Specifically, the generative AI model generates responses based on emotions and intentions in real time.

[0630] Step 5:

[0631] The server uses a storage access device to retrieve relevant information from a health database and analyzes this data using a natural language processing unit. The input is health-related data, and the output is detailed response information generated based on the analysis results. The server retrieves the user's past blood pressure data via database access and incorporates appropriate health advice.

[0632] Step 6:

[0633] The server converts the final response into speech data using a speech synthesizer and sends it to the terminal. The input is the response data, and the output is speech data. Speech synthesis software converts it into natural-sounding spoken language and generates the speech data.

[0634] Step 7:

[0635] The device plays audio data and provides responses to the user. For example, it might say, "Your blood pressure has been normal recently, but I recommend you relax a little." The input is audio data, and the output is an audio output to the user. High-quality audio responses are delivered to the user through the speaker.

[0636] (Application Example 2)

[0637] 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."

[0638] The need for health management, fraud prevention, and loneliness reduction among the elderly is increasing. However, managing these individually is difficult, and comprehensive and emotionally sensitive support is needed to improve their quality of life.

[0639] 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.

[0640] In this invention, the server includes speech recognition means for converting voice input into text data, control means for analyzing the intent and emotional state of the voice input using a generative artificial intelligence model, and emotion analysis means for analyzing emotions. This enables health management, fraud prevention, and reduction of feelings of loneliness through dialogue.

[0641] "Speech recognition means" refers to a device or method for converting speech input into text data.

[0642] A "generative artificial intelligence model" is an intelligent system that learns patterns from data and automatically generates responses that are appropriate to the user's intentions and circumstances.

[0643] "Control means" refers to methods or devices that adjust and manage various functions of a system and generate responses in accordance with user instructions.

[0644] "Speech synthesis means" refers to technologies and devices for converting text data into speech data and outputting it.

[0645] "Emotional analysis tools" refer to technologies and devices used to analyze a user's emotional state based on their statements.

[0646] A "dialogue provision means" refers to a device or method that supports communication with users and has the function of reducing users' feelings of loneliness through dialogue.

[0647] "Database access means" refers to the technologies and mechanisms used to retrieve necessary information from a database and process it.

[0648] "Natural language processing means" refers to technologies and devices that enable computers to understand, analyze, and generate natural language.

[0649] The system of the present invention functions as a personal AI supporter to assist the lives of the elderly. When a user inputs instructions into the terminal by voice, the voice is converted into text data by a speech recognition means. The text data is analyzed using a generative artificial intelligence model to generate a response that corresponds to the user's intentions and emotional state.

[0650] This control is performed by the server using a speech recognition API, sentiment analysis engine, natural language processing engine, and speech synthesis API. Specifically, speech recognition is handled by the Google Cloud Speech-to-Text API, sentiment analysis by IBM Watson Tone Analyzer, automated response generation by OpenAI GPT-3, and speech synthesis by Amazon Polly.

[0651] The emotion analysis tool detects the user's emotional state from their speech, and the dialogue provider enables communication with the user based on that emotion. For example, if a user says, "I feel lonely because I don't talk to my family much," the server analyzes the emotion as loneliness and generates a response such as, "I've detected the emotion of loneliness. Would you like to chat with me for a bit? Tell me about the news or your hobbies."

[0652] An example of a prompt is: "The user is talking about their recent state in a lonely voice. Generate a conversation that takes loneliness into consideration."

[0653] This system will enable comprehensive support for health management, fraud prevention, and reducing feelings of loneliness, thereby improving the quality of life for the elderly.

[0654] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0655] Step 1:

[0656] The user inputs instructions to the device using voice. The device captures this voice and sends it to a speech recognition API. The input is voice data, and the output is text data generated by the speech recognition API.

[0657] Step 2:

[0658] The server passes the received text data to the sentiment analysis engine. Here, the emotional state of the text data is analyzed. The input is text data, and the output is emotional data obtained by the sentiment analysis engine.

[0659] Step 3:

[0660] The server uses a generative artificial intelligence model to combine text data and sentiment data to generate responses based on the user's intent. The input is text data and sentiment data, and the output is text data as a response to the user.

[0661] Step 4:

[0662] The server sends the generated response text data to the speech synthesis API to form the audio data. The input is the response text data, and the output is the audio data generated by the speech synthesis API.

[0663] Step 5:

[0664] The terminal plays the acquired audio data to the user. The user receives the audio response provided by the system and enters further instructions as needed. This process completes the interaction with the user.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] [Fourth Embodiment]

[0669] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0670] 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.

[0671] 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).

[0672] 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.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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".

[0682] This invention is a personal AI support system designed to enable elderly people to more comfortably manage various aspects of their daily lives. The system mainly consists of means for speech recognition, generative artificial intelligence models, control, speech synthesis, database access, and natural language processing.

[0683] System Overview

[0684] Users can obtain various information and execute commands by speaking to a device (such as a smart speaker) installed in their home. Voice input is converted into text data by the device's speech recognition system, and this text data is sent to a server. A generative artificial intelligence model on the server analyzes the text data and interprets the user's intent. If necessary, health information and schedule information are obtained via a database access system, and the generated response is sent from the server to the device, where it is responded to the user as voice data by a speech synthesis system.

[0685] Specific examples of usage scenarios

[0686] 1. Health management support:

[0687] The user speaks to the device, saying, "Tell me yesterday's blood pressure measurement results." The device converts the voice to text and sends that data to the server.

[0688] The server analyzes the text and retrieves relevant health data from the database. It then generates the results in a format such as "Yesterday's blood pressure was 120 / 80" and sends it back to the terminal.

[0689] The device converts received messages into audio and notifies the user.

[0690] 2. Fraud prevention:

[0691] When a user asks, "Check if this call is a scam," regarding a suspicious phone call, the device sends this information to the server.

[0692] The server analyzes the content of the phone call using natural language processing to identify the possibility of fraud. If the possibility is high, it generates and sends a message warning, "This may be a scam."

[0693] The device will notify the user of the warning via voice.

[0694] This system can provide comprehensive support for the daily lives of the elderly and offer a sense of security by promptly providing necessary information.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The user speaks into the device and inputs specific commands or questions by voice. For example, they might say, "Tell me yesterday's blood pressure reading."

[0698] Step 2:

[0699] The device detects the user's voice input and converts the voice into text data using its built-in speech recognition function. This converted text data is then prepared for transmission to the server.

[0700] Step 3:

[0701] The terminal sends the generated text data to the server via the internet. This communication is secure and fast.

[0702] Step 4:

[0703] The server inputs the received text data into a generative artificial intelligence model to interpret the user's intent. In this case, it determines that the user wants to "check their blood pressure measurement results."

[0704] Step 5:

[0705] The server, following its intent, uses database access means to access the health management database for the elderly and retrieves the relevant blood pressure measurement results.

[0706] Step 6:

[0707] The server generates a response message for the user based on the acquired data. For example, it might generate a message such as, "Your blood pressure yesterday was 120 / 80."

[0708] Step 7:

[0709] The server sends the generated response message to the terminal in text format.

[0710] Step 8:

[0711] The device converts received text messages into speech data using its text-to-speech function.

[0712] Step 9:

[0713] The device plays the generated audio data through its speaker and provides the user with the response, "Yesterday's blood pressure was 120 / 80."

[0714] (Example 1)

[0715] 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".

[0716] In the face of the growing need for comprehensive information processing systems to support the daily lives of the elderly, the ability to quickly and accurately acquire information and respond from voice input is essential. However, conventional systems have limitations in the speed and accuracy of voice recognition, AI analysis, and database access, making it difficult to provide users with real-time information. As a result, users cannot quickly obtain the information they need, leading to problems such as insufficient support, particularly in health management and security.

[0717] 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.

[0718] In this invention, the server includes speech recognition means for converting voice input into text data, control means for analyzing the intent of voice input using generation-based artificial intelligence means, acquiring information, and generating a response, speech synthesis means for converting the response into voice data and outputting it, and data transmission means for sending and receiving information between the terminal and the server via data communication. This enables rapid processing from voice input to information acquisition and response generation, allowing for the provision of appropriate information to the user in real time, thereby effectively supporting the lives of the elderly.

[0719] "Speech recognition means" refers to technology that analyzes speech input and converts its content into text data.

[0720] "Generation-based artificial intelligence means" refers to a system that uses artificial intelligence technology to process received data and instructions, and generates optimal responses and information for the user.

[0721] A "control means" is a component that plays a central role in operating the entire system and enables data analysis, processing, and coordination with other means.

[0722] "Speech synthesis means" refers to a technology that converts text data into speech data and outputs it to the user as speech information.

[0723] "Data transmission means" refers to communication methods and technologies for exchanging data between a terminal and a server, enabling the sending and receiving of information.

[0724] "Means of accessing a storage medium" refers to technology that accesses a storage medium in order to store, retrieve, or retrieve specific information.

[0725] "Natural language processing means" refers to functions that use technologies for analyzing, understanding, and generating human language on a computer to perform specific tasks.

[0726] This invention is a personal AI support system designed to help elderly people manage their daily lives more comfortably. The system has the ability to obtain information through a terminal installed in the user's home and execute voice commands.

[0727] The device uses a microphone to capture the user's voice input. Instructions and questions uttered by the user are converted into text data via speech recognition technology. Speech recognition APIs are often used for this purpose.

[0728] The converted text data is sent to a server via the internet. The server analyzes the data using generative artificial intelligence methods. This AI model utilizes a system capable of accurate multi-purpose natural language processing. The AI ​​extracts the user's intent from the text, obtains necessary information, and generates responses.

[0729] During the response generation process, the server accesses storage media as needed to retrieve the latest health information and schedules. For example, if a user asks, "What's on my schedule this week?", the AI ​​searches for the corresponding information and prepares a specific answer such as, "You have a doctor's appointment on Tuesday."

[0730] The response returned from the server is sent to the terminal. The terminal uses speech synthesis to convert the text into audio data and transmits it to the user through its speaker. At this time, the synthesized audio is played back using the terminal's speaker system.

[0731] As a concrete example, consider a scenario where a user wants to know information about their health. The user asks the device, "What was my blood pressure yesterday?" The device converts the voice into text and sends it to the server. The server consults a health information database to retrieve recent measurement results and generates a response such as, "Your blood pressure yesterday was 120 / 80." This response is then spoken aloud by the device and heard by the user.

[0732] As an example of a prompt, one could give the AI ​​model the instruction, "When the user asks about the next scheduled appointment, retrieve the relevant information from the storage medium and generate a response."

[0733] This system is intended to go beyond simply providing information and offer comprehensive support to older adults to improve their quality of life.

[0734] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0735] Step 1:

[0736] The user inputs questions and instructions by voice into the terminal. The user's voice is captured via the microphone. This voice data becomes the input.

[0737] Step 2:

[0738] The device converts the captured audio data into text data using speech recognition. Specifically, it analyzes the audio using a speech recognition API and outputs the corresponding text. This text data becomes the input for the next step.

[0739] Step 3:

[0740] The terminal sends the converted text data to the server via the internet. Here, the text data is passed to the server as input data. This data transmission is performed securely using the HTTPS protocol.

[0741] Step 4:

[0742] The server analyzes the received text data using a generating AI model. The AI ​​model extracts the user's intent from the input text and determines the necessary information. As a result of the analysis, search queries and actions to be taken are output.

[0743] Step 5:

[0744] The server retrieves relevant information from the storage medium based on the analysis results. During this process, it uses database queries to search for and output the requested data. For example, user health information and schedule information may be retrieved.

[0745] Step 6:

[0746] The server generates a response based on the acquired information. Using a generative AI model, it constructs a message to convey to the user in natural language and outputs a text-based response. This response is used in the next step.

[0747] Step 7:

[0748] The server sends the generated response text to the terminal. This is also done via secure data communication, and the text data is delivered to the terminal.

[0749] Step 8:

[0750] The terminal converts received text data into audio data using speech synthesis technology. It uses speech synthesis technology to change text into speech, and then outputs that speech to the user through the speaker.

[0751] Step 9:

[0752] The user receives voice messages output from the device. This provides timely information necessary for the user, thereby supporting their daily life.

[0753] (Application Example 1)

[0754] 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".

[0755] In the daily lives of the elderly, there is a need for intuitive and rapid information retrieval and instruction execution via voice, as well as improved daily peace of mind regarding health management and fraud prevention. However, existing systems often struggle to achieve sufficient voice recognition accuracy and flexible responses. Furthermore, there is a lack of user-friendly interfaces that comprehensively provide these functions.

[0756] 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.

[0757] In this invention, the server includes a voice processing means for converting voice information into text information, a control device that analyzes the intent of the voice information using a generative machine learning model and creates a response, a voice generation means for converting the response into voice information and outputting it, and a data exchange means for providing location information and schedule information. This makes it possible for elderly people to efficiently manage their health information and prevent fraud using voice in their daily lives, while obtaining information with intuitive operation.

[0758] "Speech processing means" refers to a device or function that uses technology to accurately convert speech information into text information.

[0759] A "generative machine learning model" is an advanced computational model used to analyze speech and text data, understand the user's intent, and generate appropriate responses.

[0760] A "control device" is a computer-based device used to manage and process the generated responses.

[0761] "Speech generation means" refers to technologies and devices that convert text information into speech information and convey it to users in an easily understandable way.

[0762] A "data exchange means" is a method for managing and providing various types of data, such as location information and schedule information, and for communicating with other systems and devices.

[0763] This invention provides a personal AI support system that enables elderly people to live their daily lives more safely and comfortably.

[0764] First, the user initiates operation by speaking into a terminal installed in their home. The terminal converts the spoken information into text using voice processing technology. This converted text is then sent to a server. The server uses a generative machine learning model to analyze the user's intent from the text data. In this process, the latest natural language processing technology is used as the generative machine learning model to achieve more accurate intent analysis.

[0765] The control unit generates an appropriate response based on the analysis results. For example, if a user asks, "When is my next hospital appointment?", it retrieves schedule information from the database and generates a response such as, "Your next hospital appointment is next Monday."

[0766] The generated response is converted into voice information by a voice generation device and transmitted from the terminal to the user. In this way, the user can obtain the necessary information through voice communication without requiring advanced operations.

[0767] Furthermore, through data exchange mechanisms, the device can acquire external location and schedule information in real time and update the data as needed. The device is designed so that users can receive the same services even when they are away from home, enhancing convenience.

[0768] For example, if a user gives a voice command such as "I want to check my schedule for this week," the terminal sends the command to the server, which generates a voice response such as "Your schedule for this week includes a hospital appointment on Monday and a medication pick-up on Thursday." An example of a prompt might be "Please check the user's schedule and respond with important appointments for next week." This system is an innovative solution that combines advanced analysis based on a generative AI model with a simple interface.

[0769] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0770] Step 1:

[0771] The user inputs voice into the device. The input voice is captured by the device's microphone.

[0772] Step 2:

[0773] The terminal uses voice processing to convert acquired voice information into text information. Specifically, it uses voice recognition technology to convert voice signals into text data. This text data becomes the input for the next process.

[0774] Step 3:

[0775] The terminal sends the generated text information to the server. This text data serves as the basis for further analysis and processing on the server.

[0776] Step 4:

[0777] The server uses a generative machine learning model to analyze the received text data. This involves data computation that analyzes the text data using natural language processing techniques to identify the user's intent and the information they need.

[0778] Step 5:

[0779] The server, via the control unit, generates an appropriate response based on the analyzed data. This response generation is a data processing step that involves referencing a database to retrieve necessary information and creating an answer to the user's query.

[0780] Step 6:

[0781] The server sends the generated response to the terminal. At this time, the response data is transferred again in text format.

[0782] Step 7:

[0783] The device converts received text data into audio information using a speech generation method. Specifically, it utilizes speech synthesis technology to output the text as audio that the user can understand.

[0784] Step 8:

[0785] The device communicates voice responses to the user, allowing the user to obtain necessary information without visual interaction.

[0786] 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.

[0787] This invention combines an emotion engine with a personal AI supporter system that provides comprehensive support for the lives of the elderly based on user voice input. The system consists of voice recognition, a generative artificial intelligence model, control, speech synthesis, database access, natural language processing, and an emotion engine, enabling responses that take into account the user's emotional state.

[0788] System Overview

[0789] When a user speaks to a device (such as a smart speaker), the device captures voice data. Voice recognition converts the voice into text data, and an emotion engine analyzes the emotions from this text. The text and emotion data are sent to a server, where a generative artificial intelligence model generates a response combining the user's intent and emotions. During this process, necessary information (e.g., health data) is obtained via database access and analyzed by natural language processing. The final response is then synthesized into speech and provided to the user.

[0790] Specific examples of usage scenarios

[0791] 1. Integrating health management and emotional care:

[0792] If a user says, "I've been feeling a bit tired lately, so I'd like to check my health status," the device sends voice data to the server.

[0793] The server uses an emotion engine to detect fatigue and, accordingly, retrieves health management information, including advice on stress management and rest. It generates messages such as, "Your blood pressure has been normal recently, but we recommend you relax a little."

[0794] The device communicates this response to the user via voice.

[0795] 2. Reducing user anxiety in fraud prevention:

[0796] If a user asks in a worried tone, "Please check if this call is a scam," the emotion engine will detect anxiety.

[0797] The server checks for fraud information and generates a reassuring message such as, "There are no reports of fraud associated with this phone number, but caution is advised."

[0798] The device plays this for the user to alleviate their anxiety.

[0799] By incorporating an emotion engine in this way, the system can take into account the user's emotional state and provide more personalized support.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] The user makes a specific voice input to the device, such as, "I've been feeling a bit tired lately, so I'd like to check my health status."

[0803] Step 2:

[0804] The device receives voice input and converts the voice into text data using its built-in speech recognition capabilities. This text data is also passed to the emotion engine.

[0805] Step 3:

[0806] The device uses an emotion engine to analyze the text extracted from the audio and identify the emotion (in this case, fatigue) contained in the user's statements.

[0807] Step 4:

[0808] The device sends text data and sentiment information to the server. This communication takes place in real time, enabling a rapid response.

[0809] Step 5:

[0810] The server inputs the received text and emotional information into a generative artificial intelligence model to accurately understand the user's request. It understands that the user wants to check their health status and is experiencing fatigue.

[0811] Step 6:

[0812] The server uses database access to retrieve the user's health information (e.g., latest blood pressure data) and generates additional advice tailored to their emotional state (e.g., suggestions for relaxation).

[0813] Step 7:

[0814] The server sends the generated response to the terminal in text format. This may include advice such as, "Your blood pressure has been normal recently, but we recommend that you relax."

[0815] Step 8:

[0816] The terminal converts text messages received from the server into speech data using its text-to-speech function. This speech data is then played back through the speaker.

[0817] Step 9:

[0818] The device uses synthesized voice to respond to the user with messages such as, "Your blood pressure has been normal recently, but we recommend that you relax a little," providing the user with information and advice.

[0819] (Example 2)

[0820] 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".

[0821] In modern society, comprehensively supporting the lives of the elderly requires not only providing information but also appropriately understanding their emotional state and responding accordingly. However, conventional systems provide uniform information without considering emotions, limiting the improvement of the user experience. Furthermore, with the increase in fraud, there is a demand for highly secure communication, but systems that can address this have been limited.

[0822] 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.

[0823] In this invention, the server includes a speech recognition device that converts speech into text data, a control device that analyzes the intent of speech input using a generative machine learning model and generates a response based on emotional information, a speech synthesis device that converts the response into acoustic data and outputs it, and an emotion analysis device that analyzes emotions. This makes it possible to provide personalized responses that take into account the user's emotions, improve the quality of life for the elderly, and realize safe communication.

[0824] A "speech recognition device" is a device that uses technology to convert speech data into text data.

[0825] A "generative machine learning model" is a model that has a learning algorithm used to analyze the intent behind input data and generate an appropriate response based on that analysis.

[0826] A "control device" is a device that analyzes the intent behind voice input and generates a response based on emotional information.

[0827] A "speech synthesis device" is a device that converts a generated response into acoustic data and outputs it as speech.

[0828] An "emotion analysis device" is a device that analyzes the user's emotional state based on the input information.

[0829] A "storage device access device" is a device used to retrieve necessary information from a storage device that manages specific data.

[0830] A "natural language processing device" is a device that has the technology to analyze text written in natural language and identify or generate specific information.

[0831] This invention is a system that provides comprehensive support for the lives of elderly people based on user voice input. This system consists of a voice recognition device, a control device employing a generative machine learning model, a voice synthesis device, an emotion analysis device, a memory access device, and a natural language processing device.

[0832] First, the user gives verbal instructions to a device (for example, a smart speaker). The device captures this voice using a microphone and converts it into text data using a speech recognition device. At this stage, widely available software can be used for speech recognition technology.

[0833] Next, the device sends string data to the server. The server uses an emotion analyzer to detect the user's emotional state and analyzes the emotion and intent using a generative machine learning model (e.g., common generative AI techniques). A response is generated based on this information. The generated response includes content tailored to the user's emotional state, such as advice to alleviate stress or fatigue.

[0834] Subsequently, the server uses a storage access device to retrieve necessary data, such as the user's health information, and performs further analysis via a natural language processing device. Finally, the generated response is converted into acoustic data through a speech synthesizer and provided to the user from the terminal.

[0835] As a concrete example, consider a scenario where a user says, "I've been feeling a bit tired lately, so I'd like to check my health status." The system analyzes the user's voice and detects fatigue using an emotion analyzer. The control unit generates a message such as, "Your blood pressure has been normal recently, but we recommend you relax a little," and the terminal delivers this message aloud.

[0836] Examples of prompts include, "Generate a conversation to understand the user's emotions and provide health advice to reduce stress," and "Create a response that provides appropriate reassurance when the user is feeling anxious."

[0837] This invention enables personalized information delivery based on the user's emotions and intentions, thereby improving the quality of life for the elderly.

[0838] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0839] Step 1:

[0840] The user speaks into the device, for example, saying, "I've been feeling a bit tired lately, so I'd like to check my health status." This voice is captured by the device's microphone. The input is the user's voice data, and the output is this voice data. The device picks up the user's voice from the background noise through the microphone.

[0841] Step 2:

[0842] The terminal uses a speech recognition device to convert captured audio data into text data. The input is the user's voice data, and the output is text data. Specifically, the speech recognition software converts the audio to text in real time.

[0843] Step 3:

[0844] The terminal sends the generated string data to the server. The server uses an emotion analysis device to analyze the user's emotional state from the string data. The input is string data, and the output is the analysis result including emotional information. The emotion analysis software generates an emotional vector and uses it to detect if the user is feeling fatigued.

[0845] Step 4:

[0846] The server inputs emotional information and text data as prompts into a generative AI model, which then generates a response. The input consists of emotional information and text data, while the output is semantically adapted response data. Specifically, the generative AI model generates responses based on emotions and intentions in real time.

[0847] Step 5:

[0848] The server uses a storage access device to retrieve relevant information from a health database and analyzes this data using a natural language processing unit. The input is health-related data, and the output is detailed response information generated based on the analysis results. The server retrieves the user's past blood pressure data via database access and incorporates appropriate health advice.

[0849] Step 6:

[0850] The server converts the final response into speech data using a speech synthesizer and sends it to the terminal. The input is the response data, and the output is speech data. Speech synthesis software converts it into natural-sounding spoken language and generates the speech data.

[0851] Step 7:

[0852] The device plays audio data and provides responses to the user. For example, it might say, "Your blood pressure has been normal recently, but I recommend you relax a little." The input is audio data, and the output is an audio output to the user. High-quality audio responses are delivered to the user through the speaker.

[0853] (Application Example 2)

[0854] 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".

[0855] The need for health management, fraud prevention, and loneliness reduction among the elderly is increasing. However, managing these individually is difficult, and comprehensive and emotionally sensitive support is needed to improve their quality of life.

[0856] 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.

[0857] In this invention, the server includes speech recognition means for converting voice input into text data, control means for analyzing the intent and emotional state of the voice input using a generative artificial intelligence model, and emotion analysis means for analyzing emotions. This enables health management, fraud prevention, and reduction of feelings of loneliness through dialogue.

[0858] "Speech recognition means" refers to a device or method for converting speech input into text data.

[0859] A "generative artificial intelligence model" is an intelligent system that learns patterns from data and automatically generates responses that are appropriate to the user's intentions and circumstances.

[0860] "Control means" refers to methods or devices that adjust and manage various functions of a system and generate responses in accordance with user instructions.

[0861] "Speech synthesis means" refers to technologies and devices for converting text data into speech data and outputting it.

[0862] "Emotional analysis tools" refer to technologies and devices used to analyze a user's emotional state based on their statements.

[0863] A "dialogue provision means" refers to a device or method that supports communication with users and has the function of reducing users' feelings of loneliness through dialogue.

[0864] "Database access means" refers to the technologies and mechanisms used to retrieve necessary information from a database and process it.

[0865] "Natural language processing means" refers to technologies and devices that enable computers to understand, analyze, and generate natural language.

[0866] The system of the present invention functions as a personal AI supporter to assist the lives of the elderly. When a user inputs instructions into the terminal by voice, the voice is converted into text data by a speech recognition means. The text data is analyzed using a generative artificial intelligence model to generate a response that corresponds to the user's intentions and emotional state.

[0867] This control is performed by the server using a speech recognition API, sentiment analysis engine, natural language processing engine, and speech synthesis API. Specifically, speech recognition is handled by the Google Cloud Speech-to-Text API, sentiment analysis by IBM Watson Tone Analyzer, automated response generation by OpenAI GPT-3, and speech synthesis by Amazon Polly.

[0868] The emotion analysis tool detects the user's emotional state from their speech, and the dialogue provider enables communication with the user based on that emotion. For example, if a user says, "I feel lonely because I don't talk to my family much," the server analyzes the emotion as loneliness and generates a response such as, "I've detected the emotion of loneliness. Would you like to chat with me for a bit? Tell me about the news or your hobbies."

[0869] An example of a prompt is: "The user is talking about their recent state in a lonely voice. Generate a conversation that takes loneliness into consideration."

[0870] This system will enable comprehensive support for health management, fraud prevention, and reducing feelings of loneliness, thereby improving the quality of life for the elderly.

[0871] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0872] Step 1:

[0873] The user inputs instructions to the device using voice. The device captures this voice and sends it to a speech recognition API. The input is voice data, and the output is text data generated by the speech recognition API.

[0874] Step 2:

[0875] The server passes the received text data to the sentiment analysis engine. Here, the emotional state of the text data is analyzed. The input is text data, and the output is emotional data obtained by the sentiment analysis engine.

[0876] Step 3:

[0877] The server uses a generative artificial intelligence model to combine text data and sentiment data to generate responses based on the user's intent. The input is text data and sentiment data, and the output is text data as a response to the user.

[0878] Step 4:

[0879] The server sends the generated response text data to the speech synthesis API to form the audio data. The input is the response text data, and the output is the audio data generated by the speech synthesis API.

[0880] Step 5:

[0881] The terminal plays the acquired audio data to the user. The user receives the audio response provided by the system and enters further instructions as needed. This process completes the interaction with the user.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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."

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] The following is further disclosed regarding the embodiments described above.

[0904] (Claim 1)

[0905] A speech recognition means that converts voice input into text data,

[0906] A control means that analyzes the intent of voice input using a generative artificial intelligence model and generates a response,

[0907] A speech synthesis means that converts the response into audio data and outputs it,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, comprising means for accessing a database for managing health information, and retrieving information from the database in response to a user's health-related instructions.

[0911] (Claim 3)

[0912] The system according to claim 1, comprising natural language processing means for detecting fraud and generating warning messages, and providing appropriate notifications when a user inquires about fraud-related instructions.

[0913] "Example 1"

[0914] (Claim 1)

[0915] A speech recognition means that converts voice input into text data,

[0916] A control means that analyzes the intent of voice input using generation-based artificial intelligence means, acquires information, and generates a response,

[0917] A speech synthesis means that converts the response into audio data and outputs it,

[0918] A data transmission means for sending and receiving information between a terminal and a server via data communication,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, comprising means for accessing a storage medium for managing health information, and for acquiring information from the storage medium in response to a user's health-related instructions.

[0922] (Claim 3)

[0923] The system according to claim 1, comprising natural language processing means for detecting fraud and generating warning information, and providing appropriate notifications when a user inquires about fraud-related instructions.

[0924] "Application Example 1"

[0925] (Claim 1)

[0926] A speech processing means for converting speech information into text information,

[0927] A control device that uses a generative machine learning model to analyze the intent of speech information and create a response,

[0928] A voice generation means that converts the response into voice information and outputs it,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, comprising data storage means for managing health status information, and retrieving information from the database in response to the user's health-related instructions.

[0932] (Claim 3)

[0933] The system according to claim 1, comprising a natural language processing unit for detecting fraud and generating warning messages, and providing appropriate notifications when a user inquires about fraud-related instructions.

[0934] (Claim 4)

[0935] The system according to claim 1, comprising recording voice data, a data exchange means for providing location information and schedule information, and assisting the user in checking their daily schedule.

[0936] "Example 2 of combining an emotion engine"

[0937] (Claim 1)

[0938] A speech recognition device that converts speech into text data,

[0939] A control device that analyzes the intent of voice input using a generative machine learning model and generates a response based on emotional information,

[0940] A speech synthesis device that converts responses into acoustic data and outputs them,

[0941] An emotion analysis device that analyzes emotions,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, comprising a storage device access device for managing health data, which retrieves information from the storage device in response to the user's health-related instructions and provides individualized health information based on the user's emotional state.

[0945] (Claim 3)

[0946] The system according to claim 1, comprising a natural language processing unit for identifying fraud and generating warning information, and providing appropriate notifications that take into account the emotional state when a user inquires about fraud-related instructions.

[0947] "Application example 2 when combining with an emotional engine"

[0948] (Claim 1)

[0949] A speech recognition means that converts voice input into text data,

[0950] A control means that analyzes the intent and emotional state of voice input using a generative artificial intelligence model and generates a response,

[0951] A speech synthesis means that converts the response into audio data and outputs it,

[0952] Emotional analysis tools for analyzing emotions,

[0953] A means of providing dialogue to alleviate feelings of loneliness through dialogue,

[0954] ...

[0955] A system that includes this.

[0956] (Claim 2)

[0957] The system according to claim 1, comprising means for accessing a database for managing health information and generating daily health advice, and for retrieving information from the database in response to the user's health-related instructions.

[0958] (Claim 3)

[0959] The system according to claim 1, comprising natural language processing means for detecting fraud and generating notification messages to provide reassurance, and taking appropriate action when a user inquires about fraud-related instructions. [Explanation of symbols]

[0960] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A speech recognition means that converts voice input into text data, A control means that analyzes the intent of voice input using a generative artificial intelligence model and generates a response, A speech synthesis means that converts the response into audio data and outputs it, A system that includes this.

2. The system according to claim 1, comprising means for accessing a database for managing health information, and for obtaining information from the database in response to a user's health-related instructions.

3. The system according to claim 1, comprising natural language processing means for detecting fraud and generating warning messages, and providing appropriate notifications when a user inquires about fraud-related instructions.

Images