System

Abstract

To provide a personal support system capable of appropriately coping with a user based on the age, individual personality and taste.SOLUTION: The system includes an input means for inputting user information as initial setting, a means for storing the inputted user information in a database, a voice input means for receiving a voice input from a user, a voice recognition means for converting the received voice input into text data, a response generation means for generating a response based on the user information by analyzing the converted text data, a voice conversion means for converting the generated response into voice data, a voice output means for reproducing the voice data, and a learning means for recording a use history and updating the user information based on the recorded use history.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, people of all ages, including the elderly and children, face the problem of not having anyone to whom they can easily turn for advice about their everyday concerns and worries. Elderly people, in particular, often feel lonely and socially isolated, which can have a negative impact on their mental health. Meanwhile, children often have worries and questions that they find difficult to discuss with their parents or teachers. To solve these problems, a personalized support system is needed that can respond appropriately based on age, individual personality, and hobbies and preferences. Furthermore, it would be ideal if the system could continuously learn from users' usage history and provide more appropriate responses. [Means for solving the problem]

[0005] The present invention provides a system that includes an input means for inputting user information as initial settings and stores the information input by the user in a database. It also includes a voice input means and a voice recognition means for accepting voice input from the user and converting it into text data. It further includes a response generation means for analyzing the converted text data and generating a response based on the user information, and includes a voice conversion means and a voice output means for converting the generated response into voice data. Additionally, the system includes a learning means for recording usage history and updating user information based on that record, thereby enabling the system to continuously provide optimal responses to users. These means provide an environment where a variety of users, including the elderly and children, can easily consult with a service provider about questions and concerns they may have in their daily lives, thereby helping to resolve their problems.

[0006] "Input means" is an interface for users to input their own detailed information.

[0007] A "database" is a storage device for storing and managing user information and usage history.

[0008] "Voice input means" refers to a device or software for receiving voice from the user and inputting it into the system.

[0009] "Speech recognition means" is a technology for converting received voice data into text data.

[0010] A "response generator" is logic or algorithm for generating appropriate text responses to user queries.

[0011] "Speech conversion means" refers to technology for converting the generated text response into voice data.

[0012] The "audio output means" is a device for reproducing audio data and letting the user hear it.

[0013] "Learning means" is a technology that allows the system to update user information based on usage history and improve the accuracy of responses. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention relates to a next-generation smartphone system that enables users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they may have in their daily lives. A specific embodiment of the present invention will be described in detail below.

[0036] System Configuration

[0037] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[0038] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[0039] Initial Setup

[0040] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[0041] Server: Stores the received user information in a database and generates a profile for each user.

[0042] Voice dialogue

[0043] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[0044] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[0045] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[0046] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[0047] Terminal: Plays back the audio data and conveys responses to the user.

[0048] Usage history and learning

[0049] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0050] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[0051] Specific examples

[0052] When a user checks the weather

[0053] 1. User: Asks into smartphone, "What's the weather like today?"

[0054] 2. Device: Records audio and sends it to the server.

[0055] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Based on the user's location information, the server obtains weather information and generates a text response such as "Today's weather is sunny."

[0056] 4. Server: Converts the text response into audio data and sends it to the device.

[0057] 5. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0058] If a user wants to check out gardening trends

[0059] 1. User: Says to their smartphone, "Tell me about the latest gardening trends."

[0060] 2. Device: Records audio and sends it to the server.

[0061] 3. Server: Converts the voice data into text and generates queries to obtain the latest information on gardening. Based on the user's hobbies and preferences, it searches and aggregates relevant trend information and generates a text response such as, "The latest gardening trend is arranging succulents."

[0062] 4. Server: Converts the created text response into audio data and sends it to the device.

[0063] 5. Terminal: Plays audio data and tells the user, "The latest gardening trend is arranging succulents."

[0064] summary

[0065] This invention provides a system that provides personalized responses based on detailed user information and continuously improves response accuracy by learning from usage history. This system allows users to easily consult with a service provider about their questions and concerns in their daily lives, making it particularly useful for the elderly and children.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[0069] Step 2:

[0070] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[0071] Step 3:

[0072] Terminal: Temporarily stores the entered information and sends it to the server.

[0073] Step 4:

[0074] Server: Generates a user profile based on the received user information and stores it in a database.

[0075] Step 5:

[0076] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[0077] Step 6:

[0078] Terminal: Records the user's voice and sends it to the server as audio data.

[0079] Step 7:

[0080] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[0081] Step 8:

[0082] Server: Analyzes the text data and generates an appropriate response by referencing the user profile. For example, it obtains weather information based on location information and generates a response such as "Today's weather is sunny."

[0083] Step 9:

[0084] Server: Converts the generated text response into audio data and sends it to the device.

[0085] Step 10:

[0086] Terminal: Plays back audio data and tells the user, "Today's weather is sunny."

[0087] Step 11:

[0088] Terminal: Records conversation history and usage status with the user and sends it to the server.

[0089] Step 12:

[0090] Server: Analyzes the received usage history using learning methods and updates the user profile. For example, if a user frequently asks a specific question, the server prioritizes obtaining the latest information on that topic.

[0091] The above is a specific flow of system processing according to the present invention.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] Conventional voice assistant systems could only provide standardized responses, making it difficult to provide responses based on a user's individual hobbies, preferences, or profile information. Furthermore, they lacked a mechanism for effectively learning from a user's usage history, making it impossible to improve response accuracy in the future. The objective of this invention is to provide a system that utilizes a user's individual information to provide more personalized and adaptive responses.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes an input means for inputting user information as initial settings, a means for saving the input user information in a database, and a voice input means for accepting voice input from the user, thereby making it possible to provide individually adapted responses based on the user's detailed information.

[0097] "User information" refers to detailed information such as name, date of birth, blood type, hobbies and preferences that the user enters during initial setup.

[0098] "Input means" refers to the interface through which a user inputs information into the system, including an on-screen keyboard and microphone input.

[0099] A "database" is a centralized data store that stores data such as user information and usage history, and allows for searching and updating as needed.

[0100] "Voice input means" refers to a device or interface for receiving voice input from a user, including a microphone.

[0101] "Speech recognition means" refers to technology or software for analyzing received voice input and converting it into corresponding text data.

[0102] The "response generation means" refers to a process or technology for generating an appropriate response based on the converted text data and user information.

[0103] "Speech conversion means" refers to technology or software for converting the generated text response into audio data.

[0104] "Audio output means" refers to a device or interface for conveying generated audio data to the user, including a speaker.

[0105] The "learning means" refers to a process or technology that analyzes usage history, updates user information, and improves response accuracy from the next time onward.

[0106] "Communication Method" means the technology or protocol used to transmit and receive voice input and response data over a network, including, but not limited to, the HTTP or HTTPS protocols.

[0107] A "generative AI model" is a model or technology that uses artificial intelligence to generate appropriate responses to user input. A specific example is a natural language processing model.

[0108] MODE FOR CARRYING OUT THE INVENTION

[0109] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A specific embodiment of the present invention will be described in detail below.

[0110] 1. System Configuration

[0111] The system mainly consists of devices (terminals) used by users and a server that processes data.

[0112] 1.1 Terminal

[0113] A terminal is a device operated by a user, such as a smartphone or tablet. The hardware includes a microphone, speaker, and touch screen, and the software includes a voice recording application and a network communication module (HTTP / HTTPS).

[0114] 1.2 Server

[0115] The server acts as a central processing unit and includes the following software modules:

[0116] Voice recognition software (e.g., Google® Speech-to-Text API)

[0117] Response generation software (e.g., OpenAI's GPT-3)

[0118] Text-to-speech software (e.g., Amazon Polly)

[0119] Database Management Systems

[0120] Learning Algorithm

[0121] 2. Initial Setup

[0122] When a user uses the system for the first time, they enter detailed information such as their name, date of birth, blood type, hobbies and preferences on the initial setup screen of their smartphone or tablet. This information is sent from the device to the server, where it is stored in a database and a profile is created.

[0123] 3. Voice Dialogue

[0124] Users can use this system to input everyday questions and inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[0125] 3.1 Voice input

[0126] When a user asks a question by voice, the device records the voice through a microphone, and the recorded voice data is sent to the server through the network communication module.

[0127] 3.2 Speech Recognition

[0128] The server converts the received voice data into text data using voice recognition software, specifically using a voice recognition service such as the Google Speech-to-Text API.

[0129] 3.3 Response Generation

[0130] The converted text data is then analyzed using a generative AI model (e.g., OpenAI's GPT-3). Based on the analyzed data and user profile information, an appropriate response is generated.

[0131] 4. Voice response

[0132] The generated text response is then converted back into speech using speech conversion software (e.g., Amazon Polly), which converts the text into speech and sends it to the device, which then plays it back to the user.

[0133] 5. Usage history and learning

[0134] The system records the user's conversation history and usage history. This history data is sent to the server and used as training data for the generative AI model. This data is continuously trained to improve response accuracy in future.

[0135] Specific examples

[0136] Check the weather information

[0137] User: "What's the weather like today?"

[0138] Device: Record audio and send it to the server

[0139] Server: Speech recognition, weather information acquisition, response generation such as "Today's weather is sunny"

[0140] Server: Converts the response into audio and sends it to the device

[0141] Device: Play "Today's weather is sunny" to the user

[0142] Check out gardening trends

[0143] User: "What are the latest gardening trends?"

[0144] Device: Record audio and send it to the server

[0145] Server: Speech recognition, analysis of the latest trend information, and generation of a response such as "The latest gardening trend is arranging succulents."

[0146] Server: Converts the response into audio and sends it to the device

[0147] Device: Play the message to the user, "The latest gardening trend is arranging succulents."

[0148] This invention allows users to quickly obtain more personalized and actionable information, which will be particularly beneficial for the elderly and children.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] User: Enters detailed information such as name, date of birth, blood type, hobbies, and preferences on the initial setup screen of a smartphone or tablet. As input, the user's individual information is provided to the system. As output, this information is sent from the device to the server.

[0152] Step 2:

[0153] Terminal: Collects information entered by the user and sends the aggregated data to the server. As input, the user's initial settings information is given. As output, the data is sent to the server in a structured data format.

[0154] Step 3:

[0155] Server: Stores the received user information in a database and generates a profile for each user. The input is the initial setting information sent from the terminal. The output is a user profile generated in the database.

[0156] Step 4:

[0157] User: Inputs a question or inquiry into the device by voice. For example, say, "What's the weather like today?" The user's voice is input into the device as input. The output is recorded as voice data.

[0158] Step 5:

[0159] Terminal: Records the user's voice through a microphone. The recorded voice data is sent to the server through a network communication module. As input, the user's voice data is obtained from the microphone. As output, a voice data file is generated that is sent to the server.

[0160] Step 6:

[0161] Server: Receives and temporarily stores voice data. Converts the voice data into text using speech recognition software (e.g., Google Speech-to-Text API). The input is the voice data sent from the device. The output is text data.

[0162] Step 7:

[0163] Server: Receives the converted text data and analyzes it. It uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. The input is the text data obtained from speech recognition. The output is a text response based on the user's profile.

[0164] Step 8:

[0165] Server: Converts the generated text response into voice data using speech conversion software (e.g., Amazon Polly). The input is the text data obtained from the response generation means. The output is voice data.

[0166] Step 9:

[0167] Server: The converted audio data is sent to the device using HTTP or HTTPS protocol. The converted audio data is stored on the server as input. The audio data sent to the device is generated as output.

[0168] Step 10:

[0169] Terminal: Plays the received audio data. Provides a voice response to the user. As input, it receives audio data from the server. As output, it generates audio that is played back to the user.

[0170] Step 11:

[0171] Terminal: Records conversation history and usage history with the user and periodically sends this to the server. As input, interaction data with the user is acquired from the microphone. As output, history data is generated that is sent to the server and recorded.

[0172] Step 12:

[0173] Server: Analyzes the received usage history and performs learning based on the generative AI model. This improves response accuracy from the next time onwards. The input is the usage history data sent from the device. The output is an improved generative AI model.

[0174] (Application example 1)

[0175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0176] There is a need for a system that allows users of a wide range of ages, from the elderly to children, to easily consult by voice about their questions and concerns in daily life, and that can receive appropriate dietary suggestions that take their health into consideration.Current systems have difficulty providing appropriate responses based on each user's health condition, hobbies, and preferences, so a method is needed to respond quickly and accurately to a wide range of individual requests.

[0177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0178] In this invention, the server includes input means for inputting user information as initial settings, means for saving the input user information in a database, voice input means for accepting voice input from the user, voice recognition means for converting the accepted voice input into text data, response generation means for analyzing the converted text data and generating a response based on the user information, voice conversion means for converting the generated response into voice data, voice output means for playing the voice data, learning means for recording usage history and updating user information based on the recorded usage history, and meal suggestion means for generating meal suggestions taking the user's health condition into consideration, thereby enabling the user to receive appropriate meal suggestions and providing responses tailored to the user's individual health condition, hobbies, and preferences.

[0179] "User information" refers to all data including the user's personal information, health status, hobbies, preferences, usage history, etc.

[0180] "Input means" refers to a device or interface used by a user to input information.

[0181] "Means for storing in a database" refers to a system that stores input user information in digital form.

[0182] "Voice input means" refers to a device or interface for collecting voice from a user and inputting it into the system.

[0183] "Speech recognition means" refers to a technique or device for converting received voice input into text data.

[0184] The "response generation means" refers to a function that analyzes the converted text data and generates an appropriate response based on the user information.

[0185] "Speech conversion means" refers to a technology or device that converts a generated text response into audio data.

[0186] "Audio output means" refers to a device for reproducing audio data and providing audio information to a user.

[0187] The "learning means" refers to a technology or device for recording usage history, updating user information based on the recorded usage history, and improving the accuracy of responses.

[0188] The "meal suggestion means" refers to a function that generates appropriate meal suggestions based on the user's health condition and preferences.

[0189] MODE FOR CARRYING OUT THE INVENTION

[0190] The present invention relates to a system for making dietary suggestions that take into account a user's health condition using voice input from the user. Specific embodiments will be described in detail below.

[0191] System Configuration

[0192] Hardware Configuration

[0193] 1. Device:

[0194] Voice input method: Use the microphone built into devices such as smartphones and tablets.

[0195] Audio output means: Speaker, headset, etc.

[0196] 2. Server:

[0197] Database: A database for storing user information and usage history.

[0198] Processor: A central processing unit that processes voice data, converts text, generates responses, and performs learning functions.

[0199] Software Configuration

[0200] 1. Speech Recognition Software:

[0201] speech_recognition: A speech-to-text library that converts what the user says into text form.

[0202] 2. Text-to-Speech Software:

[0203] gTTS: A text-to-speech library that converts generated text responses into audio data.

[0204] 3. Data management software:

[0205] Database management system: Stores and manages user profiles and usage history.

[0206] 4. Natural Language Processing Software:

[0207] Generative AI models: Used to generate appropriate responses to user questions.

[0208] Processing Flow

[0209] 1. Initial Setup:

[0210] Device: The user is asked to enter detailed information such as name, date of birth, hobbies, and preferences. This information is sent from the device to the server.

[0211] Server: Stores the received user information in a database and creates a profile for each user.

[0212] 2. Voice interaction:

[0213] User: Speaks a question, for example, "What are your meal recommendations?"

[0214] Device: Records voice input and sends it to the server.

[0215] Server: Converts voice data into text and generates appropriate responses based on the user's profile. Analyzes the questions using a generative AI model and creates meal suggestions based on the user's health status, hobbies, and preferences.

[0216] Server: Generates meal suggestions in text format, converts them into audio data, and sends them to the device.

[0217] Terminal: Plays the audio data and communicates responses to the user.

[0218] 3. Usage history and learning:

[0219] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0220] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[0221] Specific examples

[0222] When the user checks out meal suggestions

[0223] 1. User: Asks into smartphone, "What meal would you recommend?"

[0224] 2. Device: Records audio and sends it to the server.

[0225] 3. Server: Converts the voice data into text and generates meal suggestions based on the user's health status and preferences. For example, it generates a text response such as "The recommended meal is a low-carb salad."

[0226] 4. Server: Converts the text response into audio data and sends it to the device.

[0227] 5. Device: Plays audio data and tells the user, "The recommended meal is a low-carb salad."

[0228] Example prompt sentence:

[0229] "Prompt: 'Give me some meal suggestions. User is 68 years old and has diabetes so prefers a low-carb diet.'"

[0230] "Prompt: 'Based on the user's preferences, what are the recommended food delivery options? The user prefers Japanese food and is over 60 years old.'"

[0231] This allows users to receive meal suggestions based on their health status and preferences. The system learns from usage history to improve the accuracy of responses and realize personalized responses for each user.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] Initial Setup

[0235] Terminal: A screen is displayed for the user to enter detailed information such as name, date of birth, hobbies, preferences, and health status. The user enters this information.

[0236] Input: Information entered by the user.

[0237] Output: User details are sent from the device to the server.

[0238] Data processing / calculation: Compile user details into a single dataset.

[0239] Step 2:

[0240] Saving user information

[0241] Server: Stores the received user information in a database. Creates a profile for each user.

[0242] Input: User information sent from the device.

[0243] Output: User profile stored in the database.

[0244] Data processing / calculation: User information is organized as structured data and stored in a database.

[0245] Step 3:

[0246] Accepting voice input

[0247] User: Speaks questions into the smartphone.

[0248] Input: Spoken input such as "What meal would you recommend?"

[0249] Output: Recorded audio data is generated.

[0250] Specific behavior: Records the user's voice using a microphone.

[0251] Step 4:

[0252] Audio data conversion

[0253] Device: Sends recorded audio data to the server.

[0254] Input: Recorded audio data.

[0255] Output: The audio data is sent to the server.

[0256] Specific operation: Audio data is sent to the server using a transfer protocol (e.g. HTTP).

[0257] Step 5:

[0258] Voice Recognition

[0259] Server: Converts the audio data into text data.

[0260] Input: Received audio data.

[0261] Output: Questions converted to text data.

[0262] Data processing / calculation: Analyze the audio data using the speech_recognition library and convert it into text format.

[0263] Step 6:

[0264] Response Generation

[0265] Server: Analyzes text data and generates appropriate meal suggestions based on the user's health condition and preferences.

[0266] Input: Text data, user profile.

[0267] Output: A text response such as "Recommended meal is a low-carb salad."

[0268] Data processing / computation: A generative AI model is used to analyze the question and generate a response based on the prompt.

[0269] Step 7:

[0270] Text-to-speech response

[0271] Server: Converts the generated text response into audio data.

[0272] Input: A text response.

[0273] Output: The response converted into audio data.

[0274] Data processing / calculation: Convert text to speech data using the gTTS library.

[0275] Step 8:

[0276] Sending a voice response

[0277] Server: Sends audio data to the device.

[0278] Input: The response converted to audio data.

[0279] Output: The audio data sent to the device.

[0280] Specific operation: Audio data is sent to the device using a transfer protocol (e.g. HTTP).

[0281] Step 9:

[0282] Playing a voice response

[0283] Terminal: Plays back the received audio data and conveys the response to the user.

[0284] Input: Audio data sent from the server.

[0285] Output: The audio response the user hears.

[0286] Specific behavior: Plays audio data through speakers or a headset.

[0287] Step 10:

[0288] Recording and learning usage history

[0289] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0290] Input: Conversation history, system usage history.

[0291] Output: Usage history sent to the server.

[0292] Specific operation: Organize usage history as structured data and send it.

[0293] Step 11:

[0294] Data Update

[0295] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[0296] Input: Submitted usage history.

[0297] Output: The updated user profile.

[0298] Data processing / calculation: Using a learning algorithm, usage history is analyzed and user profiles are updated.

[0299] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0300] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. The following describes in detail an embodiment of the present invention.

[0301] System Configuration

[0302] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[0303] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[0304] 3. Emotion engine: Provides technology to analyze the user's voice and facial expressions and recognize the user's emotions.

[0305] Initial Setup

[0306] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[0307] Server: Stores the received user information in a database and generates a profile for each user.

[0308] Voice dialogue

[0309] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[0310] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[0311] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[0312] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[0313] Terminal: Plays back the audio data and conveys responses to the user.

[0314] Emotion recognition and response regulation

[0315] Server: The emotion engine analyzes voice data and the user's facial expression data to recognize the user's emotional state. For example, it extracts emotions such as anger, sadness, and joy from the user's tone of voice and speaking style.

[0316] Server: Adjust the tone of the response based on the perceived emotion. For example, if the user sounds sad, change the response to something warmer.

[0317] Server: Converts emotion-adjusted responses into audio data and sends it to the device.

[0318] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[0319] Usage history and learning

[0320] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0321] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[0322] Server: As part of the learning process, the server reflects usage history to improve the accuracy of the emotion recognition engine.

[0323] Specific examples

[0324] When a user checks the weather

[0325] 1. User: Asks into smartphone, "What's the weather like today?"

[0326] 2. Device: Records audio and sends it to the server.

[0327] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[0328] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[0329] 5. Server: Converts the text response into audio data and sends it to the device.

[0330] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0331] When users speak with emotion

[0332] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[0333] 2. Device: Records audio and sends it to the server.

[0334] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[0335] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[0336] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[0337] 6. Server: Converts the text response into audio data and sends it to the device.

[0338] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[0339] summary

[0340] The present invention provides a system that provides personalized responses based on detailed user information and further improves the quality of responses by incorporating emotion recognition. This system allows users to easily consult with a system about their questions and concerns in daily life, and can be a valuable support for the elderly and children in particular.

[0341] The processing flow will be explained below.

[0342] Step 1:

[0343] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[0344] Step 2:

[0345] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[0346] Step 3:

[0347] Terminal: Temporarily stores the entered information and sends it to the server.

[0348] Step 4:

[0349] Server: Generates a user profile based on the received user information and stores it in a database.

[0350] Step 5:

[0351] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[0352] Step 6:

[0353] Terminal: Records the user's voice and sends it to the server as audio data.

[0354] Step 7:

[0355] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[0356] Step 8:

[0357] Server: The emotion engine analyzes the voice data and recognizes the user's emotions, for example, determining whether the user is happy or sad based on their tone and speaking style.

[0358] Step 9:

[0359] Server: Based on the text data and the recognized emotion data, the server refers to the user profile and generates an appropriate response. For example, when providing a sunny weather forecast, if the user is happy, the server generates "The weather is sunny today! Have a nice day!"

[0360] Step 10:

[0361] Server: Converts the generated text response into audio data and sends it to the device.

[0362] Step 11:

[0363] Terminal: Plays back the audio data and conveys responses to the user.

[0364] Step 12:

[0365] Terminal: Records conversation history and usage status with the user and sends it to the server.

[0366] Step 13:

[0367] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[0368] Specific examples

[0369] When a user checks the weather

[0370] 1. User: Asks into smartphone, "What's the weather like today?"

[0371] 2. Device: Records audio and sends it to the server.

[0372] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[0373] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[0374] 5. Server: Converts the text response into audio data and sends it to the device.

[0375] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0376] When users speak with emotion

[0377] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[0378] 2. Device: Records audio and sends it to the server.

[0379] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[0380] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[0381] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[0382] 6. Server: Converts the text response into audio data and sends it to the device.

[0383] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[0384] The above is a specific flow of system processing according to the present invention.

[0385] Example 2

[0386] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0387] In modern society, there is a demand for systems that allow users to easily consult with others about the questions and concerns they have in their daily lives. To provide appropriate support, particularly to a wide range of age groups, including the elderly and children, it is important to provide personalized support based on each user's hobbies, preferences, and emotional state. However, while conventional systems can accept voice input and generate basic responses, they lack the ability to recognize the user's emotions and adjust responses accordingly, making it difficult to provide higher-quality communication. Furthermore, they lacked effective learning methods to improve the accuracy of providing customized responses based on user profiles.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0389] In this invention, the server includes emotion recognition means for analyzing the user's voice tone and facial expression data to recognize the user's emotion, response adjustment means for adjusting the tone and content of responses based on the recognized emotion, and learning means for recording usage history and updating user information based on the recorded usage history, thereby enabling higher quality communication that takes the user's emotion into consideration.

[0390] "User Information" refers to basic personal information about a User, including name, date of birth, blood type, hobbies and preferences, etc.

[0391] "Input means" refers to an interface for inputting user information, such as a touch screen or keyboard on a smartphone or tablet.

[0392] A "database" is a system for storing and managing data such as user information and usage history.

[0393] The "voice input means" is a device for receiving voice input from the user, such as a microphone.

[0394] "Speech recognition means" is a technology for converting voice data into text data, and uses a voice recognition engine.

[0395] The "response generation means" is a technique for generating an appropriate response based on user information and analyzed text data.

[0396] The "voice conversion means" is a technique for converting the generated text data into voice data.

[0397] The "audio output means" is a device for transmitting audio data to the user, such as a speaker.

[0398] "Learning means" is a technology for updating user information based on recorded usage history and improving the system's response accuracy.

[0399] "Emotion recognition means" is a technology for analyzing the user's voice tone and facial expression data to recognize the user's emotional state.

[0400] "Response adjustment means" is a technology that adjusts the tone and content of a response based on the recognized emotion to provide communication that is appropriate for the user.

[0401] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A detailed description of a specific embodiment of this system is given below.

[0402] System Configuration

[0403] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. These terminals include input means (touchscreen or keyboard), voice input means (microphone), and voice output means (speaker).

[0404] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It includes a database (for storing user information), a voice recognition means (for converting voice data into text data), a response generation means (for analyzing text data and generating responses), a voice conversion means (for converting text data into voice data), a learning means (for analyzing usage history and updating profiles), an emotion recognition means (for analyzing voice tone and facial expressions to recognize emotions), and a response adjustment means (for adjusting responses based on recognized emotions).

[0405] Hardware and software details

[0406] Input method: Touchscreen and keyboard for entering user information.

[0407] Audio input means: A microphone that accepts audio input from the user.

[0408] Database: A data management system for storing user information and usage history.

[0409] Speech recognition means: A speech recognition engine (e.g., Google Speech-to-Text API) for converting voice data into text data.

[0410] Response generator: An algorithm (e.g., a natural language processing model) that analyzes the converted text data and generates a response based on user information.

[0411] Speech conversion means: A synthetic speech engine (e.g., Amazon Polly) that converts the generated text data into speech data.

[0412] Audio output means: A speaker that plays audio data.

[0413] Learning tools: Machine learning algorithms that update user profiles based on recorded usage history and improve response accuracy.

[0414] Emotion recognition means: An emotion analysis engine (e.g., Microsoft® Azure® Face API) that analyzes the user's tone of voice and facial expressions to recognize their emotions.

[0415] Response modifiers: Algorithms that adjust the tone and content of responses based on perceived emotions.

[0416] Specific examples

[0417] When a user checks the weather

[0418] 1. User: Asks into smartphone, "What's the weather like today?"

[0419] 2. Device: Records audio and sends it to the server.

[0420] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[0421] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[0422] 5. Server: Converts the text response into audio data and sends it to the device.

[0423] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0424] When users speak with emotion

[0425] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[0426] 2. Device: Records audio and sends it to the server.

[0427] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[0428] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[0429] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[0430] 6. Server: Converts the text response into audio data and sends it to the device.

[0431] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[0432] Prompt Sentence Examples

[0433] User: Asks smartphone, "What's the weather like today?"

[0434] Device: Records audio and sends it to the server.

[0435] Server: Converts voice data to text, retrieves weather information, and generates a response.

[0436] Terminal: The generated response is played back as audio data to convey information to the user.

[0437] This system allows users to receive individually tailored responses and high-quality communication that takes emotions into consideration in response to various questions and concerns in everyday life.

[0438] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0439] Step 1. Perform Initial Configuration

[0440] Input: The user uses the touchscreen or keyboard to enter information such as name, date of birth, blood type, hobbies and preferences into the device.

[0441] How it works: The device receives the information entered and sends it to the server, which stores it in a database and creates a profile for each user.

[0442] Output: A user profile is generated and stored in a database.

[0443] Step 2: Accepting voice input

[0444] Input: The user speaks into the smartphone's microphone to ask a question such as "What's the weather like today?"

[0445] How it works: The device records voice input and sends the voice data to the server.

[0446] Output: The audio data is sent to the server.

[0447] Step 3: Performing speech recognition

[0448] Input: The server receives the voice data sent from the terminal.

[0449] How it works: The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data.

[0450] Output: The audio data is converted to text data.

[0451] Step 4: Performing response generation

[0452] Input: Text data obtained through speech recognition and user profile information.

[0453] How it works: The server parses the text data and generates an adapted response based on user profile information. For example, it generates a query to get weather information and generates a response such as "Today's weather is sunny" based on information from a weather API (e.g., the OpenWeatherMap API).

[0454] Output: The generated text response.

[0455] Step 5: Perform audio conversion

[0456] Input: The server-generated text response.

[0457] How it works: The server uses a synthetic speech engine (e.g., Amazon Polly) to convert text data into audio data.

[0458] Output: Audio data is generated.

[0459] Step 6: Deliver a voice response

[0460] Input: Audio data is sent from the server to the device.

[0461] Operation: The terminal plays the received audio data and conveys the response to the user.

[0462] Output: A voice response such as "The weather is sunny today" is played to the user.

[0463] Step 7: Sentiment Analysis

[0464] Input: The server receives the user's voice data.

[0465] How it works: The server uses an emotion recognition engine (e.g., Microsoft Azure Face API) to analyze the user's voice tone and facial expression data to recognize emotions. For example, if the voice tone is low, it will recognize sadness.

[0466] Output: Recognized emotion information.

[0467] Step 8: Adjusting the response

[0468] Input: Recognized emotion information and generated text response.

[0469] How it works: The server adjusts the tone and content of the response based on the emotional information. For example, if the user speaks in a sad tone, the server changes the response to a warmer "I see, good job. Tell me something."

[0470] Output: The tailored text response.

[0471] Step 9: Convert the audio after adjustment

[0472] Input: The tailored text response.

[0473] How it works: The server again uses its synthetic speech engine to convert the adjusted text response into audio data.

[0474] Output: Adjusted audio data.

[0475] Step 10: Deliver emotionally sensitive voice responses

[0476] Input: The adjusted audio data is sent from the server to the device.

[0477] Action: The device plays back the received audio data and provides an emotionally sensitive response to the user.

[0478] Output: A voice response such as "Okay, great work. Tell us something" is played to the user.

[0479] Step 11: Record your usage history

[0480] Input: Conversation history with the user and system usage history.

[0481] Operation: The terminal records these histories and sends them to the server in batches in fixed groups.

[0482] Output: The usage history is sent to the server.

[0483] Step 12: Learning based on usage history

[0484] Input: Usage history sent to the server.

[0485] How it works: The server uses learning techniques to update the user's profile and improve the accuracy of responses based on this, as well as the accuracy of the emotion recognition engine.

[0486] Output: Updated user profile and improved training data.

[0487] (Application example 2)

[0488] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0489] While systems exist to help users of all ages, including the elderly and children, solve various questions and concerns in their daily lives, these systems lack the ability to appropriately understand users' emotions and tailor responses. Furthermore, certain services, such as food delivery, require personalized suggestions based on users' preferences and past ordering history, but existing systems do not adequately address these individual needs. Furthermore, technology is needed to effectively utilize voice input and enrich the user experience.

[0490] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for inputting user information as initial settings; means for saving the input user information in a database; voice input means for accepting voice input from the user; voice recognition means for converting the accepted voice input into text data; response generation means for analyzing the converted text data and generating a response based on the user information; voice conversion means for converting the generated response into voice data; voice output means for playing the voice data; learning means for recording usage history and updating user information based on the recorded usage history; emotion recognition means for analyzing user emotions; means for adjusting responses based on the emotion recognition; and means for suggesting personalized menus and services. This enables appropriate and personalized responses that take the user's emotions into consideration.

[0491] "User information" refers to individual data such as the system user's name, date of birth, blood type, hobbies and preferences.

[0492] "Input means" refers to a device or interface that allows a user to input detailed information.

[0493] A "database" is a storage device for storing user information and usage history.

[0494] "Voice input means" refers to a device or function that accepts voice instructions or questions from the user.

[0495] "Speech recognition means" refers to technology or a system that converts voice data into text data.

[0496] The "response generation means" is a device or program that analyzes text data and generates an appropriate response based on user information.

[0497] A "speech conversion means" is a technology or device that converts the generated text response into audio data.

[0498] The "audio output means" is a device that reproduces audio data and conveys a response to the user.

[0499] "Learning means" refers to functions and technologies that improve the system based on usage history and update user information.

[0500] "Emotion recognition means" refers to a system or technology that analyzes voice data and user facial expression data to recognize the user's emotions.

[0501] "Response tailoring" means techniques or programs that change the tone or content of responses based on perceived emotions.

[0502] "Means for making personalized menu and service suggestions" refers to technologies and systems that provide customized suggestions based on a user's hobbies, preferences, and past ordering history.

[0503] A "generative AI model" is an AI technology or program that generates individually adapted responses based on a user profile.

[0504] A "prompt" is an instruction or question that is input into a generative AI model to generate a specific response.

[0505] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns about their daily lives using voice input and emotion recognition. The following describes in detail an embodiment of the present invention.

[0506] System Configuration

[0507] Terminal: Refers to a device used by a user, such as a smartphone or tablet. Includes input means, voice input means, and voice output means.

[0508] Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, learning means, and emotion recognition means.

[0509] Initial Setup

[0510] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[0511] Server: Stores the received user information in a database and generates a profile for each user.

[0512] Voice dialogue

[0513] User: A user uses the system on a daily basis and inputs questions or inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[0514] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[0515] Server: Receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The converted text data is analyzed by a response generation method, and a response based on the user information is generated.

[0516] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[0517] Terminal: Plays back the audio data and conveys responses to the user.

[0518] Emotion recognition and response regulation

[0519] Server: The emotion recognition means analyzes the voice data and the user's facial expression data to recognize the user's emotional state. For example, emotions such as anger, sadness, and joy can be extracted from the voice tone and speaking style.

[0520] Server: Adjust the tone and content of the response based on the perceived emotion. If the user sounds sad, change the response to something warmer.

[0521] Server: Converts the adjusted response into audio data and sends it to the device.

[0522] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[0523] Usage history and learning

[0524] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0525] Server: Based on the received usage history, the server uses learning methods to update the user's profile, thereby improving the accuracy and relevance of future responses.

[0526] Server: As part of the learning process, the server reflects usage history to improve the accuracy of emotion recognition tools.

[0527] Food delivery application example

[0528] When a user says, "It's sunny today and I'm in a good mood, so I want to eat something special," the device records the voice and sends it to the server. The server converts the voice to text and uses emotion recognition to identify the "good mood." A generative AI model (e.g., OpenAI GPT-4 (registered trademark)) suggests a special menu item based on past ordering history and the user's preferences. The server converts the suggestion into voice data and sends it to the device, which responds, "How about a special sushi set today?"

[0529] Prompt Sentence Examples

[0530] User: "It's sunny today and I'm feeling great, so I want to eat something special."

[0531] Prompt the generative AI model:

[0532] Suggest special menu items based on past order history and user profile. The user is currently in a good mood.

[0533] Example response: "How about a special sushi set today?"

[0534] In this way, the present invention provides personalized responses based on the user's details and emotional state, which is particularly useful in food delivery and everyday query resolution.

[0535] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0536] Step 1:

[0537] The user inputs voice data. The user gives a command by voice, such as "It's sunny today and I'm in a good mood, so I want to eat something special." The input data is voice data.

[0538] Step 2:

[0539] The terminal records the voice data and transmits the voice data to the server. Here, the input is the user's voice data and the output is the transmission of the voice data to the server.

[0540] Step 3:

[0541] The server receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The input is the transmitted voice data, and the output is text data.

[0542] Step 4:

[0543] The server analyzes the text data and uses emotion recognition to analyze the user's emotions. For example, it extracts the positive emotion of "feeling good." The input is the text data converted from speech, and the output is the emotional state ("feeling good").

[0544] Step 5:

[0545] The server uses a generative AI model (e.g., OpenAI GPT-4) to suggest a personalized menu based on the user's past ordering history and profile. Here, a prompt sentence is input to the generative AI model. The inputs are the user profile, emotional state, and text data. The output is a suggestion: "How about a special sushi set today?"

[0546] Step 6:

[0547] The server converts the generated text response into speech data. A speech conversion means is used to convert the text data into speech data. The input is the proposed text data and the output is the speech data.

[0548] Step 7:

[0549] The server sends the audio data to the terminal. The input is the converted audio data, and the output is the data sent to the terminal.

[0550] Step 8:

[0551] The terminal plays back the audio data and makes a suggestion to the user by voice. Specifically, it says to the user, "How about a special sushi set today?" The input is the audio data sent from the server, and the output is the audio playback.

[0552] Step 9:

[0553] The terminal records the user's usage history and sends that data to the server. The input is the user's usage history data, and the output is transmission to the server.

[0554] Step 10:

[0555] The server updates the user profile based on the received usage history to improve the accuracy and relevance of future responses. The input is the transmitted usage history data, and the output is the updated user profile.

[0556] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0557] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0558] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0559] [Second embodiment]

[0560] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0561] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0562] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0563] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0564] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0565] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0566] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0567] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0568] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0569] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0570] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0571] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0572] The present invention relates to a next-generation smartphone system that enables users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they may have in their daily lives. A specific embodiment of the present invention will be described in detail below.

[0573] System Configuration

[0574] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[0575] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[0576] Initial Setup

[0577] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[0578] Server: Stores the received user information in a database and generates a profile for each user.

[0579] Voice dialogue

[0580] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[0581] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[0582] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[0583] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[0584] Terminal: Plays back the audio data and conveys responses to the user.

[0585] Usage history and learning

[0586] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0587] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[0588] Specific examples

[0589] When a user checks the weather

[0590] 1. User: Asks into smartphone, "What's the weather like today?"

[0591] 2. Device: Records audio and sends it to the server.

[0592] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Based on the user's location information, the server obtains weather information and generates a text response such as "Today's weather is sunny."

[0593] 4. Server: Converts the text response into audio data and sends it to the device.

[0594] 5. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0595] If a user wants to check out gardening trends

[0596] 1. User: Says to their smartphone, "Tell me about the latest gardening trends."

[0597] 2. Device: Records audio and sends it to the server.

[0598] 3. Server: Converts the voice data into text and generates queries to obtain the latest information on gardening. Based on the user's hobbies and preferences, it searches and aggregates relevant trend information and generates a text response such as, "The latest gardening trend is arranging succulents."

[0599] 4. Server: Converts the created text response into audio data and sends it to the device.

[0600] 5. Terminal: Plays audio data and tells the user, "The latest gardening trend is arranging succulents."

[0601] summary

[0602] This invention provides a system that provides personalized responses based on detailed user information and continuously improves response accuracy by learning from usage history. This system allows users to easily consult with a service provider about their questions and concerns in their daily lives, making it particularly useful for the elderly and children.

[0603] The processing flow will be explained below.

[0604] Step 1:

[0605] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[0606] Step 2:

[0607] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[0608] Step 3:

[0609] Terminal: Temporarily stores the entered information and sends it to the server.

[0610] Step 4:

[0611] Server: Generates a user profile based on the received user information and stores it in a database.

[0612] Step 5:

[0613] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[0614] Step 6:

[0615] Terminal: Records the user's voice and sends it to the server as audio data.

[0616] Step 7:

[0617] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[0618] Step 8:

[0619] Server: Analyzes the text data and generates an appropriate response by referencing the user profile. For example, it obtains weather information based on location information and generates a response such as "Today's weather is sunny."

[0620] Step 9:

[0621] Server: Converts the generated text response into audio data and sends it to the device.

[0622] Step 10:

[0623] Terminal: Plays back audio data and tells the user, "Today's weather is sunny."

[0624] Step 11:

[0625] Terminal: Records conversation history and usage status with the user and sends it to the server.

[0626] Step 12:

[0627] Server: Analyzes the received usage history using learning methods and updates the user profile. For example, if a user frequently asks a specific question, the server prioritizes obtaining the latest information on that topic.

[0628] The above is a specific flow of system processing according to the present invention.

[0629] Example 1

[0630] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0631] Conventional voice assistant systems could only provide standardized responses, making it difficult to provide responses based on a user's individual hobbies, preferences, or profile information. Furthermore, they lacked a mechanism for effectively learning from a user's usage history, making it impossible to improve response accuracy in the future. The objective of this invention is to provide a system that utilizes a user's individual information to provide more personalized and adaptive responses.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0633] In this invention, the server includes an input means for inputting user information as initial settings, a means for saving the input user information in a database, and a voice input means for accepting voice input from the user, thereby making it possible to provide individually adapted responses based on the user's detailed information.

[0634] "User information" refers to detailed information such as name, date of birth, blood type, hobbies and preferences that the user enters during initial setup.

[0635] "Input means" refers to the interface through which a user inputs information into the system, including an on-screen keyboard and microphone input.

[0636] A "database" is a centralized data store that stores data such as user information and usage history, and allows for searching and updating as needed.

[0637] "Voice input means" refers to a device or interface for receiving voice input from a user, including a microphone.

[0638] "Speech recognition means" refers to technology or software for analyzing received voice input and converting it into corresponding text data.

[0639] The "response generation means" refers to a process or technology for generating an appropriate response based on the converted text data and user information.

[0640] "Speech conversion means" refers to technology or software for converting the generated text response into audio data.

[0641] "Audio output means" refers to a device or interface for conveying generated audio data to the user, including a speaker.

[0642] The "learning means" refers to a process or technology that analyzes usage history, updates user information, and improves response accuracy from the next time onward.

[0643] "Communication Method" means the technology or protocol used to transmit and receive voice input and response data over a network, including, but not limited to, the HTTP or HTTPS protocols.

[0644] A "generative AI model" is a model or technology that uses artificial intelligence to generate appropriate responses to user input. A specific example is a natural language processing model.

[0645] MODE FOR CARRYING OUT THE INVENTION

[0646] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A specific embodiment of the present invention will be described in detail below.

[0647] 1. System Configuration

[0648] The system mainly consists of devices (terminals) used by users and a server that processes data.

[0649] 1.1 Terminal

[0650] A terminal is a device operated by a user, such as a smartphone or tablet. The hardware includes a microphone, speaker, and touch screen, and the software includes a voice recording application and a network communication module (HTTP / HTTPS).

[0651] 1.2 Server

[0652] The server acts as a central processing unit and includes the following software modules:

[0653] Voice recognition software (e.g., Google Speech-to-Text API)

[0654] Response generation software (e.g., OpenAI's GPT-3)

[0655] Text-to-speech software (e.g., Amazon Polly)

[0656] Database Management Systems

[0657] Learning Algorithm

[0658] 2. Initial Setup

[0659] When a user uses the system for the first time, they enter detailed information such as their name, date of birth, blood type, hobbies and preferences on the initial setup screen of their smartphone or tablet. This information is sent from the device to the server, where it is stored in a database and a profile is created.

[0660] 3. Voice Dialogue

[0661] Users can use this system to input everyday questions and inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[0662] 3.1 Voice input

[0663] When a user asks a question by voice, the device records the voice through a microphone, and the recorded voice data is sent to the server through the network communication module.

[0664] 3.2 Speech Recognition

[0665] The server converts the received voice data into text data using voice recognition software, specifically using a voice recognition service such as the Google Speech-to-Text API.

[0666] 3.3 Response Generation

[0667] The converted text data is then analyzed using a generative AI model (e.g., OpenAI's GPT-3). Based on the analyzed data and user profile information, an appropriate response is generated.

[0668] 4. Voice response

[0669] The generated text response is then converted back into speech using speech conversion software (e.g., Amazon Polly), which converts the text into speech and sends it to the device, which then plays it back to the user.

[0670] 5. Usage history and learning

[0671] The system records the user's conversation history and usage history. This history data is sent to the server and used as training data for the generative AI model. This data is continuously trained to improve response accuracy in future.

[0672] Specific examples

[0673] Check the weather information

[0674] User: "What's the weather like today?"

[0675] Device: Record audio and send it to the server

[0676] Server: Speech recognition, weather information acquisition, response generation such as "Today's weather is sunny"

[0677] Server: Converts the response into audio and sends it to the device

[0678] Device: Play "Today's weather is sunny" to the user

[0679] Check out gardening trends

[0680] User: "What are the latest gardening trends?"

[0681] Device: Record audio and send it to the server

[0682] Server: Speech recognition, analysis of the latest trend information, and generation of a response such as "The latest gardening trend is arranging succulents."

[0683] Server: Converts the response into audio and sends it to the device

[0684] Device: Play the message to the user, "The latest gardening trend is arranging succulents."

[0685] This invention allows users to quickly obtain more personalized and actionable information, which will be particularly beneficial for the elderly and children.

[0686] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0687] Step 1:

[0688] User: Enters detailed information such as name, date of birth, blood type, hobbies, and preferences on the initial setup screen of a smartphone or tablet. As input, the user's individual information is provided to the system. As output, this information is sent from the device to the server.

[0689] Step 2:

[0690] Terminal: Collects information entered by the user and sends the aggregated data to the server. As input, the user's initial settings information is given. As output, the data is sent to the server in a structured data format.

[0691] Step 3:

[0692] Server: Stores the received user information in a database and generates a profile for each user. The input is the initial setting information sent from the terminal. The output is a user profile generated in the database.

[0693] Step 4:

[0694] User: Inputs a question or inquiry into the device by voice. For example, say, "What's the weather like today?" The user's voice is input into the device as input. The output is recorded as voice data.

[0695] Step 5:

[0696] Terminal: Records the user's voice through a microphone. The recorded voice data is sent to the server through a network communication module. As input, the user's voice data is obtained from the microphone. As output, a voice data file is generated that is sent to the server.

[0697] Step 6:

[0698] Server: Receives and temporarily stores voice data. Converts the voice data into text using speech recognition software (e.g., Google Speech-to-Text API). The input is the voice data sent from the device. The output is text data.

[0699] Step 7:

[0700] Server: Receives the converted text data and analyzes it. It uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. The input is the text data obtained from speech recognition. The output is a text response based on the user's profile.

[0701] Step 8:

[0702] Server: Converts the generated text response into voice data using speech conversion software (e.g., Amazon Polly). The input is the text data obtained from the response generation means. The output is voice data.

[0703] Step 9:

[0704] Server: The converted audio data is sent to the device using HTTP or HTTPS protocol. The converted audio data is stored on the server as input. The audio data sent to the device is generated as output.

[0705] Step 10:

[0706] Terminal: Plays the received audio data. Provides a voice response to the user. As input, it receives audio data from the server. As output, it generates audio that is played back to the user.

[0707] Step 11:

[0708] Terminal: Records conversation history and usage history with the user and periodically sends this to the server. As input, interaction data with the user is acquired from the microphone. As output, history data is generated that is sent to the server and recorded.

[0709] Step 12:

[0710] Server: Analyzes the received usage history and performs learning based on the generative AI model. This improves response accuracy from the next time onwards. The input is the usage history data sent from the device. The output is an improved generative AI model.

[0711] (Application example 1)

[0712] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0713] There is a need for a system that allows users of a wide range of ages, from the elderly to children, to easily consult by voice about their questions and concerns in daily life, and that can receive appropriate dietary suggestions that take their health into consideration.Current systems have difficulty providing appropriate responses based on each user's health condition, hobbies, and preferences, so a method is needed to respond quickly and accurately to a wide range of individual requests.

[0714] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0715] In this invention, the server includes input means for inputting user information as initial settings, means for saving the input user information in a database, voice input means for accepting voice input from the user, voice recognition means for converting the accepted voice input into text data, response generation means for analyzing the converted text data and generating a response based on the user information, voice conversion means for converting the generated response into voice data, voice output means for playing the voice data, learning means for recording usage history and updating user information based on the recorded usage history, and meal suggestion means for generating meal suggestions taking the user's health condition into consideration, thereby enabling the user to receive appropriate meal suggestions and providing responses tailored to the user's individual health condition, hobbies, and preferences.

[0716] "User information" refers to all data including the user's personal information, health status, hobbies, preferences, usage history, etc.

[0717] "Input means" refers to a device or interface used by a user to input information.

[0718] "Means for storing in a database" refers to a system that stores input user information in digital form.

[0719] "Voice input means" refers to a device or interface for collecting voice from a user and inputting it into the system.

[0720] "Speech recognition means" refers to a technique or device for converting received voice input into text data.

[0721] The "response generation means" refers to a function that analyzes the converted text data and generates an appropriate response based on the user information.

[0722] "Speech conversion means" refers to a technology or device that converts a generated text response into audio data.

[0723] "Audio output means" refers to a device for reproducing audio data and providing audio information to a user.

[0724] The "learning means" refers to a technology or device for recording usage history, updating user information based on the recorded usage history, and improving the accuracy of responses.

[0725] The "meal suggestion means" refers to a function that generates appropriate meal suggestions based on the user's health condition and preferences.

[0726] MODE FOR CARRYING OUT THE INVENTION

[0727] The present invention relates to a system for making dietary suggestions that take into account a user's health condition using voice input from the user. Specific embodiments will be described in detail below.

[0728] System Configuration

[0729] Hardware Configuration

[0730] 1. Device:

[0731] Voice input method: Use the microphone built into devices such as smartphones and tablets.

[0732] Audio output means: Speaker, headset, etc.

[0733] 2. Server:

[0734] Database: A database for storing user information and usage history.

[0735] Processor: A central processing unit that processes voice data, converts text, generates responses, and performs learning functions.

[0736] Software Configuration

[0737] 1. Speech Recognition Software:

[0738] speech_recognition: A speech-to-text library that converts what the user says into text form.

[0739] 2. Text-to-Speech Software:

[0740] gTTS: A text-to-speech library that converts generated text responses into audio data.

[0741] 3. Data management software:

[0742] Database management system: Stores and manages user profiles and usage history.

[0743] 4. Natural Language Processing Software:

[0744] Generative AI models: Used to generate appropriate responses to user questions.

[0745] Processing Flow

[0746] 1. Initial Setup:

[0747] Device: The user is asked to enter detailed information such as name, date of birth, hobbies, and preferences. This information is sent from the device to the server.

[0748] Server: Stores the received user information in a database and creates a profile for each user.

[0749] 2. Voice interaction:

[0750] User: Speaks a question, for example, "What are your meal recommendations?"

[0751] Device: Records voice input and sends it to the server.

[0752] Server: Converts voice data into text and generates appropriate responses based on the user's profile. Analyzes the questions using a generative AI model and creates meal suggestions based on the user's health status, hobbies, and preferences.

[0753] Server: Generates meal suggestions in text format, converts them into audio data, and sends them to the device.

[0754] Terminal: Plays the audio data and communicates responses to the user.

[0755] 3. Usage history and learning:

[0756] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0757] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[0758] Specific examples

[0759] When the user checks out meal suggestions

[0760] 1. User: Asks into smartphone, "What meal would you recommend?"

[0761] 2. Device: Records audio and sends it to the server.

[0762] 3. Server: Converts the voice data into text and generates meal suggestions based on the user's health status and preferences. For example, it generates a text response such as "The recommended meal is a low-carb salad."

[0763] 4. Server: Converts the text response into audio data and sends it to the device.

[0764] 5. Device: Plays audio data and tells the user, "The recommended meal is a low-carb salad."

[0765] Example prompt sentence:

[0766] "Prompt: 'Give me some meal suggestions. User is 68 years old and has diabetes so prefers a low-carb diet.'"

[0767] "Prompt: 'Based on the user's preferences, what are the recommended food delivery options? The user prefers Japanese food and is over 60 years old.'"

[0768] This allows users to receive meal suggestions based on their health status and preferences. The system learns from usage history to improve the accuracy of responses and realize personalized responses for each user.

[0769] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0770] Step 1:

[0771] Initial Setup

[0772] Terminal: A screen is displayed for the user to enter detailed information such as name, date of birth, hobbies, preferences, and health status. The user enters this information.

[0773] Input: Information entered by the user.

[0774] Output: User details are sent from the device to the server.

[0775] Data processing / calculation: Compile user details into a single dataset.

[0776] Step 2:

[0777] Saving user information

[0778] Server: Stores the received user information in a database. Creates a profile for each user.

[0779] Input: User information sent from the device.

[0780] Output: User profile stored in the database.

[0781] Data processing / calculation: User information is organized as structured data and stored in a database.

[0782] Step 3:

[0783] Accepting voice input

[0784] User: Speaks questions into the smartphone.

[0785] Input: Spoken input such as "What meal would you recommend?"

[0786] Output: Recorded audio data is generated.

[0787] Specific behavior: Records the user's voice using a microphone.

[0788] Step 4:

[0789] Audio data conversion

[0790] Device: Sends recorded audio data to the server.

[0791] Input: Recorded audio data.

[0792] Output: The audio data is sent to the server.

[0793] Specific operation: Audio data is sent to the server using a transfer protocol (e.g. HTTP).

[0794] Step 5:

[0795] Voice Recognition

[0796] Server: Converts the audio data into text data.

[0797] Input: Received audio data.

[0798] Output: Questions converted to text data.

[0799] Data processing / calculation: Analyze the audio data using the speech_recognition library and convert it into text format.

[0800] Step 6:

[0801] Response Generation

[0802] Server: Analyzes text data and generates appropriate meal suggestions based on the user's health condition and preferences.

[0803] Input: Text data, user profile.

[0804] Output: A text response such as "Recommended meal is a low-carb salad."

[0805] Data processing / computation: A generative AI model is used to analyze the question and generate a response based on the prompt.

[0806] Step 7:

[0807] Text-to-speech response

[0808] Server: Converts the generated text response into audio data.

[0809] Input: A text response.

[0810] Output: The response converted into audio data.

[0811] Data processing / calculation: Convert text to speech data using the gTTS library.

[0812] Step 8:

[0813] Sending a voice response

[0814] Server: Sends audio data to the device.

[0815] Input: The response converted to audio data.

[0816] Output: The audio data sent to the device.

[0817] Specific operation: Audio data is sent to the device using a transfer protocol (e.g. HTTP).

[0818] Step 9:

[0819] Playing a voice response

[0820] Terminal: Plays back the received audio data and conveys the response to the user.

[0821] Input: Audio data sent from the server.

[0822] Output: The audio response the user hears.

[0823] Specific behavior: Plays audio data through speakers or a headset.

[0824] Step 10:

[0825] Recording and learning usage history

[0826] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0827] Input: Conversation history, system usage history.

[0828] Output: Usage history sent to the server.

[0829] Specific operation: Organize usage history as structured data and send it.

[0830] Step 11:

[0831] Data Update

[0832] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[0833] Input: Submitted usage history.

[0834] Output: The updated user profile.

[0835] Data processing / calculation: Using a learning algorithm, usage history is analyzed and user profiles are updated.

[0836] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0837] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. The following describes in detail an embodiment of the present invention.

[0838] System Configuration

[0839] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[0840] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[0841] 3. Emotion engine: Provides technology to analyze the user's voice and facial expressions and recognize the user's emotions.

[0842] Initial Setup

[0843] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[0844] Server: Stores the received user information in a database and generates a profile for each user.

[0845] Voice dialogue

[0846] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[0847] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[0848] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[0849] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[0850] Terminal: Plays back the audio data and conveys responses to the user.

[0851] Emotion recognition and response regulation

[0852] Server: The emotion engine analyzes voice data and the user's facial expression data to recognize the user's emotional state. For example, it extracts emotions such as anger, sadness, and joy from the user's tone of voice and speaking style.

[0853] Server: Adjust the tone of the response based on the perceived emotion. For example, if the user sounds sad, change the response to something warmer.

[0854] Server: Converts emotion-adjusted responses into audio data and sends it to the device.

[0855] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[0856] Usage history and learning

[0857] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[0858] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[0859] Server: As part of the learning process, the server reflects usage history to improve the accuracy of the emotion recognition engine.

[0860] Specific examples

[0861] When a user checks the weather

[0862] 1. User: Asks into smartphone, "What's the weather like today?"

[0863] 2. Device: Records audio and sends it to the server.

[0864] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[0865] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[0866] 5. Server: Converts the text response into audio data and sends it to the device.

[0867] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0868] When users speak with emotion

[0869] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[0870] 2. Device: Records audio and sends it to the server.

[0871] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[0872] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[0873] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[0874] 6. Server: Converts the text response into audio data and sends it to the device.

[0875] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[0876] summary

[0877] The present invention provides a system that provides personalized responses based on detailed user information and further improves the quality of responses by incorporating emotion recognition. This system allows users to easily consult with a system about their questions and concerns in daily life, and can be a valuable support for the elderly and children in particular.

[0878] The processing flow will be explained below.

[0879] Step 1:

[0880] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[0881] Step 2:

[0882] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[0883] Step 3:

[0884] Terminal: Temporarily stores the entered information and sends it to the server.

[0885] Step 4:

[0886] Server: Generates a user profile based on the received user information and stores it in a database.

[0887] Step 5:

[0888] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[0889] Step 6:

[0890] Terminal: Records the user's voice and sends it to the server as audio data.

[0891] Step 7:

[0892] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[0893] Step 8:

[0894] Server: The emotion engine analyzes the voice data and recognizes the user's emotions, for example, determining whether the user is happy or sad based on their tone and speaking style.

[0895] Step 9:

[0896] Server: Based on the text data and the recognized emotion data, the server refers to the user profile and generates an appropriate response. For example, when providing a sunny weather forecast, if the user is happy, the server generates "The weather is sunny today! Have a nice day!"

[0897] Step 10:

[0898] Server: Converts the generated text response into audio data and sends it to the device.

[0899] Step 11:

[0900] Terminal: Plays back the audio data and conveys responses to the user.

[0901] Step 12:

[0902] Terminal: Records conversation history and usage status with the user and sends it to the server.

[0903] Step 13:

[0904] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[0905] Specific examples

[0906] When a user checks the weather

[0907] 1. User: Asks into smartphone, "What's the weather like today?"

[0908] 2. Device: Records audio and sends it to the server.

[0909] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[0910] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[0911] 5. Server: Converts the text response into audio data and sends it to the device.

[0912] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0913] When users speak with emotion

[0914] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[0915] 2. Device: Records audio and sends it to the server.

[0916] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[0917] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[0918] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[0919] 6. Server: Converts the text response into audio data and sends it to the device.

[0920] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[0921] The above is a specific flow of system processing according to the present invention.

[0922] Example 2

[0923] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0924] In modern society, there is a demand for systems that allow users to easily consult with others about the questions and concerns they have in their daily lives. To provide appropriate support, particularly to a wide range of age groups, including the elderly and children, it is important to provide personalized support based on each user's hobbies, preferences, and emotional state. However, while conventional systems can accept voice input and generate basic responses, they lack the ability to recognize the user's emotions and adjust responses accordingly, making it difficult to provide higher-quality communication. Furthermore, they lacked effective learning methods to improve the accuracy of providing customized responses based on user profiles.

[0925] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0926] In this invention, the server includes emotion recognition means for analyzing the user's voice tone and facial expression data to recognize the user's emotion, response adjustment means for adjusting the tone and content of responses based on the recognized emotion, and learning means for recording usage history and updating user information based on the recorded usage history, thereby enabling higher quality communication that takes the user's emotion into consideration.

[0927] "User Information" refers to basic personal information about a User, including name, date of birth, blood type, hobbies and preferences, etc.

[0928] "Input means" refers to an interface for inputting user information, such as a touch screen or keyboard on a smartphone or tablet.

[0929] A "database" is a system for storing and managing data such as user information and usage history.

[0930] The "voice input means" is a device for receiving voice input from the user, such as a microphone.

[0931] "Speech recognition means" is a technology for converting voice data into text data, and uses a voice recognition engine.

[0932] The "response generation means" is a technique for generating an appropriate response based on user information and analyzed text data.

[0933] The "voice conversion means" is a technique for converting the generated text data into voice data.

[0934] The "audio output means" is a device for transmitting audio data to the user, such as a speaker.

[0935] "Learning means" is a technology for updating user information based on recorded usage history and improving the system's response accuracy.

[0936] "Emotion recognition means" is a technology for analyzing the user's voice tone and facial expression data to recognize the user's emotional state.

[0937] "Response adjustment means" is a technology that adjusts the tone and content of a response based on the recognized emotion to provide communication that is appropriate for the user.

[0938] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A detailed description of a specific embodiment of this system is given below.

[0939] System Configuration

[0940] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. These terminals include input means (touchscreen or keyboard), voice input means (microphone), and voice output means (speaker).

[0941] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It includes a database (for storing user information), a voice recognition means (for converting voice data into text data), a response generation means (for analyzing text data and generating responses), a voice conversion means (for converting text data into voice data), a learning means (for analyzing usage history and updating profiles), an emotion recognition means (for analyzing voice tone and facial expressions to recognize emotions), and a response adjustment means (for adjusting responses based on recognized emotions).

[0942] Hardware and software details

[0943] Input method: Touchscreen and keyboard for entering user information.

[0944] Audio input means: A microphone that accepts audio input from the user.

[0945] Database: A data management system for storing user information and usage history.

[0946] Speech recognition means: A speech recognition engine (e.g., Google Speech-to-Text API) for converting voice data into text data.

[0947] Response generator: An algorithm (e.g., a natural language processing model) that analyzes the converted text data and generates a response based on user information.

[0948] Speech conversion means: A synthetic speech engine (e.g., Amazon Polly) that converts the generated text data into speech data.

[0949] Audio output means: A speaker that plays audio data.

[0950] Learning tools: Machine learning algorithms that update user profiles based on recorded usage history and improve response accuracy.

[0951] Emotion recognition means: An emotion analysis engine (e.g., Microsoft Azure Face API) that analyzes the user's tone of voice and facial expressions to recognize their emotions.

[0952] Response modifiers: Algorithms that adjust the tone and content of responses based on perceived emotions.

[0953] Specific examples

[0954] When a user checks the weather

[0955] 1. User: Asks into smartphone, "What's the weather like today?"

[0956] 2. Device: Records audio and sends it to the server.

[0957] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[0958] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[0959] 5. Server: Converts the text response into audio data and sends it to the device.

[0960] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[0961] When users speak with emotion

[0962] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[0963] 2. Device: Records audio and sends it to the server.

[0964] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[0965] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[0966] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[0967] 6. Server: Converts the text response into audio data and sends it to the device.

[0968] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[0969] Prompt Sentence Examples

[0970] User: Asks smartphone, "What's the weather like today?"

[0971] Device: Records audio and sends it to the server.

[0972] Server: Converts voice data to text, retrieves weather information, and generates a response.

[0973] Terminal: The generated response is played back as audio data to convey information to the user.

[0974] This system allows users to receive individually tailored responses and high-quality communication that takes emotions into consideration in response to various questions and concerns in everyday life.

[0975] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0976] Step 1. Perform Initial Configuration

[0977] Input: The user uses the touchscreen or keyboard to enter information such as name, date of birth, blood type, hobbies and preferences into the device.

[0978] How it works: The device receives the information entered and sends it to the server, which stores it in a database and creates a profile for each user.

[0979] Output: A user profile is generated and stored in a database.

[0980] Step 2: Accepting voice input

[0981] Input: The user speaks into the smartphone's microphone to ask a question such as "What's the weather like today?"

[0982] How it works: The device records voice input and sends the voice data to the server.

[0983] Output: The audio data is sent to the server.

[0984] Step 3: Performing speech recognition

[0985] Input: The server receives the voice data sent from the terminal.

[0986] How it works: The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data.

[0987] Output: The audio data is converted to text data.

[0988] Step 4: Performing response generation

[0989] Input: Text data obtained through speech recognition and user profile information.

[0990] How it works: The server parses the text data and generates an adapted response based on user profile information. For example, it generates a query to get weather information and generates a response such as "Today's weather is sunny" based on information from a weather API (e.g., the OpenWeatherMap API).

[0991] Output: The generated text response.

[0992] Step 5: Perform audio conversion

[0993] Input: The server-generated text response.

[0994] How it works: The server uses a synthetic speech engine (e.g., Amazon Polly) to convert text data into audio data.

[0995] Output: Audio data is generated.

[0996] Step 6: Deliver a voice response

[0997] Input: Audio data is sent from the server to the device.

[0998] Operation: The terminal plays the received audio data and conveys the response to the user.

[0999] Output: A voice response such as "The weather is sunny today" is played to the user.

[1000] Step 7: Sentiment Analysis

[1001] Input: The server receives the user's voice data.

[1002] How it works: The server uses an emotion recognition engine (e.g., Microsoft Azure Face API) to analyze the user's voice tone and facial expression data to recognize emotions. For example, if the voice tone is low, it will recognize sadness.

[1003] Output: Recognized emotion information.

[1004] Step 8: Adjusting the response

[1005] Input: Recognized emotion information and generated text response.

[1006] How it works: The server adjusts the tone and content of the response based on the emotional information. For example, if the user speaks in a sad tone, the server changes the response to a warmer "I see, good job. Tell me something."

[1007] Output: The tailored text response.

[1008] Step 9: Convert the audio after adjustment

[1009] Input: The tailored text response.

[1010] How it works: The server again uses its synthetic speech engine to convert the adjusted text response into audio data.

[1011] Output: Adjusted audio data.

[1012] Step 10: Deliver emotionally sensitive voice responses

[1013] Input: The adjusted audio data is sent from the server to the device.

[1014] Action: The device plays back the received audio data and provides an emotionally sensitive response to the user.

[1015] Output: A voice response such as "Okay, great work. Tell us something" is played to the user.

[1016] Step 11: Record your usage history

[1017] Input: Conversation history with the user and system usage history.

[1018] Operation: The terminal records these histories and sends them to the server in batches in fixed groups.

[1019] Output: The usage history is sent to the server.

[1020] Step 12: Learning based on usage history

[1021] Input: Usage history sent to the server.

[1022] How it works: The server uses learning techniques to update the user's profile and improve the accuracy of responses based on this, as well as the accuracy of the emotion recognition engine.

[1023] Output: Updated user profile and improved training data.

[1024] (Application example 2)

[1025] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1026] While systems exist to help users of all ages, including the elderly and children, solve various questions and concerns in their daily lives, these systems lack the ability to appropriately understand users' emotions and tailor responses. Furthermore, certain services, such as food delivery, require personalized suggestions based on users' preferences and past ordering history, but existing systems do not adequately address these individual needs. Furthermore, technology is needed to effectively utilize voice input and enrich the user experience.

[1027] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for inputting user information as initial settings; means for saving the input user information in a database; voice input means for accepting voice input from the user; voice recognition means for converting the accepted voice input into text data; response generation means for analyzing the converted text data and generating a response based on the user information; voice conversion means for converting the generated response into voice data; voice output means for playing the voice data; learning means for recording usage history and updating user information based on the recorded usage history; emotion recognition means for analyzing user emotions; means for adjusting responses based on the emotion recognition; and means for suggesting personalized menus and services. This enables appropriate and personalized responses that take the user's emotions into consideration.

[1028] "User information" refers to individual data such as the system user's name, date of birth, blood type, hobbies and preferences.

[1029] "Input means" refers to a device or interface that allows a user to input detailed information.

[1030] A "database" is a storage device for storing user information and usage history.

[1031] "Voice input means" refers to a device or function that accepts voice instructions or questions from the user.

[1032] "Speech recognition means" refers to technology or a system that converts voice data into text data.

[1033] The "response generation means" is a device or program that analyzes text data and generates an appropriate response based on user information.

[1034] A "speech conversion means" is a technology or device that converts the generated text response into audio data.

[1035] The "audio output means" is a device that reproduces audio data and conveys a response to the user.

[1036] "Learning means" refers to functions and technologies that improve the system based on usage history and update user information.

[1037] "Emotion recognition means" refers to a system or technology that analyzes voice data and user facial expression data to recognize the user's emotions.

[1038] "Response tailoring" means techniques or programs that change the tone or content of responses based on perceived emotions.

[1039] "Means for making personalized menu and service suggestions" refers to technologies and systems that provide customized suggestions based on a user's hobbies, preferences, and past ordering history.

[1040] A "generative AI model" is an AI technology or program that generates individually adapted responses based on a user profile.

[1041] A "prompt" is an instruction or question that is input into a generative AI model to generate a specific response.

[1042] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns about their daily lives using voice input and emotion recognition. The following describes in detail an embodiment of the present invention.

[1043] System Configuration

[1044] Terminal: Refers to a device used by a user, such as a smartphone or tablet. Includes input means, voice input means, and voice output means.

[1045] Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, learning means, and emotion recognition means.

[1046] Initial Setup

[1047] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[1048] Server: Stores the received user information in a database and generates a profile for each user.

[1049] Voice dialogue

[1050] User: A user uses the system on a daily basis and inputs questions or inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[1051] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[1052] Server: Receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The converted text data is analyzed by a response generation method, and a response based on the user information is generated.

[1053] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[1054] Terminal: Plays back the audio data and conveys responses to the user.

[1055] Emotion recognition and response regulation

[1056] Server: The emotion recognition means analyzes the voice data and the user's facial expression data to recognize the user's emotional state. For example, emotions such as anger, sadness, and joy can be extracted from the voice tone and speaking style.

[1057] Server: Adjust the tone and content of the response based on the perceived emotion. If the user sounds sad, change the response to something warmer.

[1058] Server: Converts the adjusted response into audio data and sends it to the device.

[1059] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[1060] Usage history and learning

[1061] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1062] Server: Based on the received usage history, the server uses learning methods to update the user's profile, thereby improving the accuracy and relevance of future responses.

[1063] Server: As part of the learning process, the server reflects usage history to improve the accuracy of emotion recognition tools.

[1064] Food delivery application example

[1065] When a user says, "It's sunny today and I'm in a good mood, so I want to eat something special," the device records the speech and sends it to the server. The server converts the speech to text and uses emotion recognition to identify the "good mood." A generative AI model (e.g., OpenAI GPT-4) suggests a special menu item based on past ordering history and the user's preferences. The server converts the suggestion into voice data and sends it to the device, which responds, "How about a special sushi set today?"

[1066] Prompt Sentence Examples

[1067] User: "It's sunny today and I'm feeling great, so I want to eat something special."

[1068] Prompt the generative AI model:

[1069] Suggest special menu items based on past order history and user profile. The user is currently in a good mood.

[1070] Example response: "How about a special sushi set today?"

[1071] In this way, the present invention provides personalized responses based on the user's details and emotional state, which is particularly useful in food delivery and everyday query resolution.

[1072] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1073] Step 1:

[1074] The user inputs voice data. The user gives a command by voice, such as "It's sunny today and I'm in a good mood, so I want to eat something special." The input data is voice data.

[1075] Step 2:

[1076] The terminal records the voice data and transmits the voice data to the server. Here, the input is the user's voice data and the output is the transmission of the voice data to the server.

[1077] Step 3:

[1078] The server receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The input is the transmitted voice data, and the output is text data.

[1079] Step 4:

[1080] The server analyzes the text data and uses emotion recognition to analyze the user's emotions. For example, it extracts the positive emotion of "feeling good." The input is the text data converted from speech, and the output is the emotional state ("feeling good").

[1081] Step 5:

[1082] The server uses a generative AI model (e.g., OpenAI GPT-4) to suggest a personalized menu based on the user's past ordering history and profile. Here, a prompt sentence is input to the generative AI model. The inputs are the user profile, emotional state, and text data. The output is a suggestion: "How about a special sushi set today?"

[1083] Step 6:

[1084] The server converts the generated text response into speech data. A speech conversion means is used to convert the text data into speech data. The input is the proposed text data and the output is the speech data.

[1085] Step 7:

[1086] The server sends the audio data to the terminal. The input is the converted audio data, and the output is the data sent to the terminal.

[1087] Step 8:

[1088] The terminal plays back the audio data and makes a suggestion to the user by voice. Specifically, it says to the user, "How about a special sushi set today?" The input is the audio data sent from the server, and the output is the audio playback.

[1089] Step 9:

[1090] The terminal records the user's usage history and sends that data to the server. The input is the user's usage history data, and the output is transmission to the server.

[1091] Step 10:

[1092] The server updates the user profile based on the received usage history to improve the accuracy and relevance of future responses. The input is the transmitted usage history data, and the output is the updated user profile.

[1093] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1095] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1096] [Third embodiment]

[1097] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1098] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1100] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1101] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1105] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1107] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1108] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1109] The present invention relates to a next-generation smartphone system that enables users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they may have in their daily lives. A specific embodiment of the present invention will be described in detail below.

[1110] System Configuration

[1111] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[1112] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[1113] Initial Setup

[1114] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[1115] Server: Stores the received user information in a database and generates a profile for each user.

[1116] Voice dialogue

[1117] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[1118] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[1119] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[1120] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[1121] Terminal: Plays back the audio data and conveys responses to the user.

[1122] Usage history and learning

[1123] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1124] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[1125] Specific examples

[1126] When a user checks the weather

[1127] 1. User: Asks into smartphone, "What's the weather like today?"

[1128] 2. Device: Records audio and sends it to the server.

[1129] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Based on the user's location information, the server obtains weather information and generates a text response such as "Today's weather is sunny."

[1130] 4. Server: Converts the text response into audio data and sends it to the device.

[1131] 5. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[1132] If a user wants to check out gardening trends

[1133] 1. User: Says to their smartphone, "Tell me about the latest gardening trends."

[1134] 2. Device: Records audio and sends it to the server.

[1135] 3. Server: Converts the voice data into text and generates queries to obtain the latest information on gardening. Based on the user's hobbies and preferences, it searches and aggregates relevant trend information and generates a text response such as, "The latest gardening trend is arranging succulents."

[1136] 4. Server: Converts the created text response into audio data and sends it to the device.

[1137] 5. Terminal: Plays audio data and tells the user, "The latest gardening trend is arranging succulents."

[1138] summary

[1139] This invention provides a system that provides personalized responses based on detailed user information and continuously improves response accuracy by learning from usage history. This system allows users to easily consult with a service provider about their questions and concerns in their daily lives, making it particularly useful for the elderly and children.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[1143] Step 2:

[1144] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[1145] Step 3:

[1146] Terminal: Temporarily stores the entered information and sends it to the server.

[1147] Step 4:

[1148] Server: Generates a user profile based on the received user information and stores it in a database.

[1149] Step 5:

[1150] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[1151] Step 6:

[1152] Terminal: Records the user's voice and sends it to the server as audio data.

[1153] Step 7:

[1154] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[1155] Step 8:

[1156] Server: Analyzes the text data and generates an appropriate response by referencing the user profile. For example, it obtains weather information based on location information and generates a response such as "Today's weather is sunny."

[1157] Step 9:

[1158] Server: Converts the generated text response into audio data and sends it to the device.

[1159] Step 10:

[1160] Terminal: Plays back audio data and tells the user, "Today's weather is sunny."

[1161] Step 11:

[1162] Terminal: Records conversation history and usage status with the user and sends it to the server.

[1163] Step 12:

[1164] Server: Analyzes the received usage history using learning methods and updates the user profile. For example, if a user frequently asks a specific question, the server prioritizes obtaining the latest information on that topic.

[1165] The above is a specific flow of system processing according to the present invention.

[1166] Example 1

[1167] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1168] Conventional voice assistant systems could only provide standardized responses, making it difficult to provide responses based on a user's individual hobbies, preferences, or profile information. Furthermore, they lacked a mechanism for effectively learning from a user's usage history, making it impossible to improve response accuracy in the future. The objective of this invention is to provide a system that utilizes a user's individual information to provide more personalized and adaptive responses.

[1169] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1170] In this invention, the server includes an input means for inputting user information as initial settings, a means for saving the input user information in a database, and a voice input means for accepting voice input from the user, thereby making it possible to provide individually adapted responses based on the user's detailed information.

[1171] "User information" refers to detailed information such as name, date of birth, blood type, hobbies and preferences that the user enters during initial setup.

[1172] "Input means" refers to the interface through which a user inputs information into the system, including an on-screen keyboard and microphone input.

[1173] A "database" is a centralized data store that stores data such as user information and usage history, and allows for searching and updating as needed.

[1174] "Voice input means" refers to a device or interface for receiving voice input from a user, including a microphone.

[1175] "Speech recognition means" refers to technology or software for analyzing received voice input and converting it into corresponding text data.

[1176] The "response generation means" refers to a process or technology for generating an appropriate response based on the converted text data and user information.

[1177] "Speech conversion means" refers to technology or software for converting the generated text response into audio data.

[1178] "Audio output means" refers to a device or interface for conveying generated audio data to the user, including a speaker.

[1179] The "learning means" refers to a process or technology that analyzes usage history, updates user information, and improves response accuracy from the next time onward.

[1180] "Communication Method" means the technology or protocol used to transmit and receive voice input and response data over a network, including, but not limited to, the HTTP or HTTPS protocols.

[1181] A "generative AI model" is a model or technology that uses artificial intelligence to generate appropriate responses to user input. A specific example is a natural language processing model.

[1182] MODE FOR CARRYING OUT THE INVENTION

[1183] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A specific embodiment of the present invention will be described in detail below.

[1184] 1. System Configuration

[1185] The system mainly consists of devices (terminals) used by users and a server that processes data.

[1186] 1.1 Terminal

[1187] A terminal is a device operated by a user, such as a smartphone or tablet. The hardware includes a microphone, speaker, and touch screen, and the software includes a voice recording application and a network communication module (HTTP / HTTPS).

[1188] 1.2 Server

[1189] The server acts as a central processing unit and includes the following software modules:

[1190] Voice recognition software (e.g., Google Speech-to-Text API)

[1191] Response generation software (e.g., OpenAI's GPT-3)

[1192] Text-to-speech software (e.g., Amazon Polly)

[1193] Database Management Systems

[1194] Learning Algorithm

[1195] 2. Initial Setup

[1196] When a user uses the system for the first time, they enter detailed information such as their name, date of birth, blood type, hobbies and preferences on the initial setup screen of their smartphone or tablet. This information is sent from the device to the server, where it is stored in a database and a profile is created.

[1197] 3. Voice Dialogue

[1198] Users can use this system to input everyday questions and inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[1199] 3.1 Voice input

[1200] When a user asks a question by voice, the device records the voice through a microphone, and the recorded voice data is sent to the server through the network communication module.

[1201] 3.2 Speech Recognition

[1202] The server converts the received voice data into text data using voice recognition software, specifically using a voice recognition service such as the Google Speech-to-Text API.

[1203] 3.3 Response Generation

[1204] The converted text data is then analyzed using a generative AI model (e.g., OpenAI's GPT-3). Based on the analyzed data and user profile information, an appropriate response is generated.

[1205] 4. Voice response

[1206] The generated text response is then converted back into speech using speech conversion software (e.g., Amazon Polly), which converts the text into speech and sends it to the device, which then plays it back to the user.

[1207] 5. Usage history and learning

[1208] The system records the user's conversation history and usage history. This history data is sent to the server and used as training data for the generative AI model. This data is continuously trained to improve response accuracy in future.

[1209] Specific examples

[1210] Check the weather information

[1211] User: "What's the weather like today?"

[1212] Device: Record audio and send it to the server

[1213] Server: Speech recognition, weather information acquisition, response generation such as "Today's weather is sunny"

[1214] Server: Converts the response into audio and sends it to the device

[1215] Device: Play "Today's weather is sunny" to the user

[1216] Check out gardening trends

[1217] User: "What are the latest gardening trends?"

[1218] Device: Record audio and send it to the server

[1219] Server: Speech recognition, analysis of the latest trend information, and generation of a response such as "The latest gardening trend is arranging succulents."

[1220] Server: Converts the response into audio and sends it to the device

[1221] Device: Play the message to the user, "The latest gardening trend is arranging succulents."

[1222] This invention allows users to quickly obtain more personalized and actionable information, which will be particularly beneficial for the elderly and children.

[1223] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1224] Step 1:

[1225] User: Enters detailed information such as name, date of birth, blood type, hobbies, and preferences on the initial setup screen of a smartphone or tablet. As input, the user's individual information is provided to the system. As output, this information is sent from the device to the server.

[1226] Step 2:

[1227] Terminal: Collects information entered by the user and sends the aggregated data to the server. As input, the user's initial settings information is given. As output, the data is sent to the server in a structured data format.

[1228] Step 3:

[1229] Server: Stores the received user information in a database and generates a profile for each user. The input is the initial setting information sent from the terminal. The output is a user profile generated in the database.

[1230] Step 4:

[1231] User: Inputs a question or inquiry into the device by voice. For example, say, "What's the weather like today?" The user's voice is input into the device as input. The output is recorded as voice data.

[1232] Step 5:

[1233] Terminal: Records the user's voice through a microphone. The recorded voice data is sent to the server through a network communication module. As input, the user's voice data is obtained from the microphone. As output, a voice data file is generated that is sent to the server.

[1234] Step 6:

[1235] Server: Receives and temporarily stores voice data. Converts the voice data into text using speech recognition software (e.g., Google Speech-to-Text API). The input is the voice data sent from the device. The output is text data.

[1236] Step 7:

[1237] Server: Receives the converted text data and analyzes it. It uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. The input is the text data obtained from speech recognition. The output is a text response based on the user's profile.

[1238] Step 8:

[1239] Server: Converts the generated text response into voice data using speech conversion software (e.g., Amazon Polly). The input is the text data obtained from the response generation means. The output is voice data.

[1240] Step 9:

[1241] Server: The converted audio data is sent to the device using HTTP or HTTPS protocol. The converted audio data is stored on the server as input. The audio data sent to the device is generated as output.

[1242] Step 10:

[1243] Terminal: Plays the received audio data. Provides a voice response to the user. As input, it receives audio data from the server. As output, it generates audio that is played back to the user.

[1244] Step 11:

[1245] Terminal: Records conversation history and usage history with the user and periodically sends this to the server. As input, interaction data with the user is acquired from the microphone. As output, history data is generated that is sent to the server and recorded.

[1246] Step 12:

[1247] Server: Analyzes the received usage history and performs learning based on the generative AI model. This improves response accuracy from the next time onwards. The input is the usage history data sent from the device. The output is an improved generative AI model.

[1248] (Application example 1)

[1249] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1250] There is a need for a system that allows users of a wide range of ages, from the elderly to children, to easily consult by voice about their questions and concerns in daily life, and that can receive appropriate dietary suggestions that take their health into consideration.Current systems have difficulty providing appropriate responses based on each user's health condition, hobbies, and preferences, so a method is needed to respond quickly and accurately to a wide range of individual requests.

[1251] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1252] In this invention, the server includes input means for inputting user information as initial settings, means for saving the input user information in a database, voice input means for accepting voice input from the user, voice recognition means for converting the accepted voice input into text data, response generation means for analyzing the converted text data and generating a response based on the user information, voice conversion means for converting the generated response into voice data, voice output means for playing the voice data, learning means for recording usage history and updating user information based on the recorded usage history, and meal suggestion means for generating meal suggestions taking the user's health condition into consideration, thereby enabling the user to receive appropriate meal suggestions and providing responses tailored to the user's individual health condition, hobbies, and preferences.

[1253] "User information" refers to all data including the user's personal information, health status, hobbies, preferences, usage history, etc.

[1254] "Input means" refers to a device or interface used by a user to input information.

[1255] "Means for storing in a database" refers to a system that stores input user information in digital form.

[1256] "Voice input means" refers to a device or interface for collecting voice from a user and inputting it into the system.

[1257] "Speech recognition means" refers to a technique or device for converting received voice input into text data.

[1258] The "response generation means" refers to a function that analyzes the converted text data and generates an appropriate response based on the user information.

[1259] "Speech conversion means" refers to a technology or device that converts a generated text response into audio data.

[1260] "Audio output means" refers to a device for reproducing audio data and providing audio information to a user.

[1261] The "learning means" refers to a technology or device for recording usage history, updating user information based on the recorded usage history, and improving the accuracy of responses.

[1262] The "meal suggestion means" refers to a function that generates appropriate meal suggestions based on the user's health condition and preferences.

[1263] MODE FOR CARRYING OUT THE INVENTION

[1264] The present invention relates to a system for making dietary suggestions that take into account a user's health condition using voice input from the user. Specific embodiments will be described in detail below.

[1265] System Configuration

[1266] Hardware Configuration

[1267] 1. Device:

[1268] Voice input method: Use the microphone built into devices such as smartphones and tablets.

[1269] Audio output means: Speaker, headset, etc.

[1270] 2. Server:

[1271] Database: A database for storing user information and usage history.

[1272] Processor: A central processing unit that processes voice data, converts text, generates responses, and performs learning functions.

[1273] Software Configuration

[1274] 1. Speech Recognition Software:

[1275] speech_recognition: A speech-to-text library that converts what the user says into text form.

[1276] 2. Text-to-Speech Software:

[1277] gTTS: A text-to-speech library that converts generated text responses into audio data.

[1278] 3. Data management software:

[1279] Database management system: Stores and manages user profiles and usage history.

[1280] 4. Natural Language Processing Software:

[1281] Generative AI models: Used to generate appropriate responses to user questions.

[1282] Processing Flow

[1283] 1. Initial Setup:

[1284] Device: The user is asked to enter detailed information such as name, date of birth, hobbies, and preferences. This information is sent from the device to the server.

[1285] Server: Stores the received user information in a database and creates a profile for each user.

[1286] 2. Voice interaction:

[1287] User: Speaks a question, for example, "What are your meal recommendations?"

[1288] Device: Records voice input and sends it to the server.

[1289] Server: Converts voice data into text and generates appropriate responses based on the user's profile. Analyzes the questions using a generative AI model and creates meal suggestions based on the user's health status, hobbies, and preferences.

[1290] Server: Generates meal suggestions in text format, converts them into audio data, and sends them to the device.

[1291] Terminal: Plays the audio data and communicates responses to the user.

[1292] 3. Usage history and learning:

[1293] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1294] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[1295] Specific examples

[1296] When the user checks out meal suggestions

[1297] 1. User: Asks into smartphone, "What meal would you recommend?"

[1298] 2. Device: Records audio and sends it to the server.

[1299] 3. Server: Converts the voice data into text and generates meal suggestions based on the user's health status and preferences. For example, it generates a text response such as "The recommended meal is a low-carb salad."

[1300] 4. Server: Converts the text response into audio data and sends it to the device.

[1301] 5. Device: Plays audio data and tells the user, "The recommended meal is a low-carb salad."

[1302] Example prompt sentence:

[1303] "Prompt: 'Give me some meal suggestions. User is 68 years old and has diabetes so prefers a low-carb diet.'"

[1304] "Prompt: 'Based on the user's preferences, what are the recommended food delivery options? The user prefers Japanese food and is over 60 years old.'"

[1305] This allows users to receive meal suggestions based on their health status and preferences. The system learns from usage history to improve the accuracy of responses and realize personalized responses for each user.

[1306] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1307] Step 1:

[1308] Initial Setup

[1309] Terminal: A screen is displayed for the user to enter detailed information such as name, date of birth, hobbies, preferences, and health status. The user enters this information.

[1310] Input: Information entered by the user.

[1311] Output: User details are sent from the device to the server.

[1312] Data processing / calculation: Compile user details into a single dataset.

[1313] Step 2:

[1314] Saving user information

[1315] Server: Stores the received user information in a database. Creates a profile for each user.

[1316] Input: User information sent from the device.

[1317] Output: User profile stored in the database.

[1318] Data processing / calculation: User information is organized as structured data and stored in a database.

[1319] Step 3:

[1320] Accepting voice input

[1321] User: Speaks questions into the smartphone.

[1322] Input: Spoken input such as "What meal would you recommend?"

[1323] Output: Recorded audio data is generated.

[1324] Specific behavior: Records the user's voice using a microphone.

[1325] Step 4:

[1326] Audio data conversion

[1327] Device: Sends recorded audio data to the server.

[1328] Input: Recorded audio data.

[1329] Output: The audio data is sent to the server.

[1330] Specific operation: Audio data is sent to the server using a transfer protocol (e.g. HTTP).

[1331] Step 5:

[1332] Voice Recognition

[1333] Server: Converts the audio data into text data.

[1334] Input: Received audio data.

[1335] Output: Questions converted to text data.

[1336] Data processing / calculation: Analyze the audio data using the speech_recognition library and convert it into text format.

[1337] Step 6:

[1338] Response Generation

[1339] Server: Analyzes text data and generates appropriate meal suggestions based on the user's health condition and preferences.

[1340] Input: Text data, user profile.

[1341] Output: A text response such as "Recommended meal is a low-carb salad."

[1342] Data processing / computation: A generative AI model is used to analyze the question and generate a response based on the prompt.

[1343] Step 7:

[1344] Text-to-speech response

[1345] Server: Converts the generated text response into audio data.

[1346] Input: A text response.

[1347] Output: The response converted into audio data.

[1348] Data processing / calculation: Convert text to speech data using the gTTS library.

[1349] Step 8:

[1350] Sending a voice response

[1351] Server: Sends audio data to the device.

[1352] Input: The response converted to audio data.

[1353] Output: The audio data sent to the device.

[1354] Specific operation: Audio data is sent to the device using a transfer protocol (e.g. HTTP).

[1355] Step 9:

[1356] Playing a voice response

[1357] Terminal: Plays back the received audio data and conveys the response to the user.

[1358] Input: Audio data sent from the server.

[1359] Output: The audio response the user hears.

[1360] Specific behavior: Plays audio data through speakers or a headset.

[1361] Step 10:

[1362] Recording and learning usage history

[1363] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1364] Input: Conversation history, system usage history.

[1365] Output: Usage history sent to the server.

[1366] Specific operation: Organize usage history as structured data and send it.

[1367] Step 11:

[1368] Data Update

[1369] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[1370] Input: Submitted usage history.

[1371] Output: The updated user profile.

[1372] Data processing / calculation: Using a learning algorithm, usage history is analyzed and user profiles are updated.

[1373] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1374] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. The following describes in detail an embodiment of the present invention.

[1375] System Configuration

[1376] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[1377] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[1378] 3. Emotion engine: Provides technology to analyze the user's voice and facial expressions and recognize the user's emotions.

[1379] Initial Setup

[1380] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[1381] Server: Stores the received user information in a database and generates a profile for each user.

[1382] Voice dialogue

[1383] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[1384] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[1385] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[1386] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[1387] Terminal: Plays back the audio data and conveys responses to the user.

[1388] Emotion recognition and response regulation

[1389] Server: The emotion engine analyzes voice data and the user's facial expression data to recognize the user's emotional state. For example, it extracts emotions such as anger, sadness, and joy from the user's tone of voice and speaking style.

[1390] Server: Adjust the tone of the response based on the perceived emotion. For example, if the user sounds sad, change the response to something warmer.

[1391] Server: Converts emotion-adjusted responses into audio data and sends it to the device.

[1392] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[1393] Usage history and learning

[1394] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1395] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[1396] Server: As part of the learning process, the server reflects usage history to improve the accuracy of the emotion recognition engine.

[1397] Specific examples

[1398] When a user checks the weather

[1399] 1. User: Asks into smartphone, "What's the weather like today?"

[1400] 2. Device: Records audio and sends it to the server.

[1401] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[1402] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[1403] 5. Server: Converts the text response into audio data and sends it to the device.

[1404] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[1405] When users speak with emotion

[1406] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[1407] 2. Device: Records audio and sends it to the server.

[1408] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[1409] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[1410] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[1411] 6. Server: Converts the text response into audio data and sends it to the device.

[1412] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[1413] summary

[1414] The present invention provides a system that provides personalized responses based on detailed user information and further improves the quality of responses by incorporating emotion recognition. This system allows users to easily consult with a system about their questions and concerns in daily life, and can be a valuable support for the elderly and children in particular.

[1415] The processing flow will be explained below.

[1416] Step 1:

[1417] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[1418] Step 2:

[1419] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[1420] Step 3:

[1421] Terminal: Temporarily stores the entered information and sends it to the server.

[1422] Step 4:

[1423] Server: Generates a user profile based on the received user information and stores it in a database.

[1424] Step 5:

[1425] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[1426] Step 6:

[1427] Terminal: Records the user's voice and sends it to the server as audio data.

[1428] Step 7:

[1429] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[1430] Step 8:

[1431] Server: The emotion engine analyzes the voice data and recognizes the user's emotions, for example, determining whether the user is happy or sad based on their tone and speaking style.

[1432] Step 9:

[1433] Server: Based on the text data and the recognized emotion data, the server refers to the user profile and generates an appropriate response. For example, when providing a sunny weather forecast, if the user is happy, the server generates "The weather is sunny today! Have a nice day!"

[1434] Step 10:

[1435] Server: Converts the generated text response into audio data and sends it to the device.

[1436] Step 11:

[1437] Terminal: Plays back the audio data and conveys responses to the user.

[1438] Step 12:

[1439] Terminal: Records conversation history and usage status with the user and sends it to the server.

[1440] Step 13:

[1441] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[1442] Specific examples

[1443] When a user checks the weather

[1444] 1. User: Asks into smartphone, "What's the weather like today?"

[1445] 2. Device: Records audio and sends it to the server.

[1446] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[1447] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[1448] 5. Server: Converts the text response into audio data and sends it to the device.

[1449] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[1450] When users speak with emotion

[1451] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[1452] 2. Device: Records audio and sends it to the server.

[1453] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[1454] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[1455] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[1456] 6. Server: Converts the text response into audio data and sends it to the device.

[1457] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[1458] The above is a specific flow of system processing according to the present invention.

[1459] Example 2

[1460] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1461] In modern society, there is a demand for systems that allow users to easily consult with others about the questions and concerns they have in their daily lives. To provide appropriate support, particularly to a wide range of age groups, including the elderly and children, it is important to provide personalized support based on each user's hobbies, preferences, and emotional state. However, while conventional systems can accept voice input and generate basic responses, they lack the ability to recognize the user's emotions and adjust responses accordingly, making it difficult to provide higher-quality communication. Furthermore, they lacked effective learning methods to improve the accuracy of providing customized responses based on user profiles.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1463] In this invention, the server includes emotion recognition means for analyzing the user's voice tone and facial expression data to recognize the user's emotion, response adjustment means for adjusting the tone and content of responses based on the recognized emotion, and learning means for recording usage history and updating user information based on the recorded usage history, thereby enabling higher quality communication that takes the user's emotion into consideration.

[1464] "User Information" refers to basic personal information about a User, including name, date of birth, blood type, hobbies and preferences, etc.

[1465] "Input means" refers to an interface for inputting user information, such as a touch screen or keyboard on a smartphone or tablet.

[1466] A "database" is a system for storing and managing data such as user information and usage history.

[1467] The "voice input means" is a device for receiving voice input from the user, such as a microphone.

[1468] "Speech recognition means" is a technology for converting voice data into text data, and uses a voice recognition engine.

[1469] The "response generation means" is a technique for generating an appropriate response based on user information and analyzed text data.

[1470] The "voice conversion means" is a technique for converting the generated text data into voice data.

[1471] The "audio output means" is a device for transmitting audio data to the user, such as a speaker.

[1472] "Learning means" is a technology for updating user information based on recorded usage history and improving the system's response accuracy.

[1473] "Emotion recognition means" is a technology for analyzing the user's voice tone and facial expression data to recognize the user's emotional state.

[1474] "Response adjustment means" is a technology that adjusts the tone and content of a response based on the recognized emotion to provide communication that is appropriate for the user.

[1475] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A detailed description of a specific embodiment of this system is given below.

[1476] System Configuration

[1477] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. These terminals include input means (touchscreen or keyboard), voice input means (microphone), and voice output means (speaker).

[1478] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It includes a database (for storing user information), a voice recognition means (for converting voice data into text data), a response generation means (for analyzing text data and generating responses), a voice conversion means (for converting text data into voice data), a learning means (for analyzing usage history and updating profiles), an emotion recognition means (for analyzing voice tone and facial expressions to recognize emotions), and a response adjustment means (for adjusting responses based on recognized emotions).

[1479] Hardware and software details

[1480] Input method: Touchscreen and keyboard for entering user information.

[1481] Audio input means: A microphone that accepts audio input from the user.

[1482] Database: A data management system for storing user information and usage history.

[1483] Speech recognition means: A speech recognition engine (e.g., Google Speech-to-Text API) for converting voice data into text data.

[1484] Response generator: An algorithm (e.g., a natural language processing model) that analyzes the converted text data and generates a response based on user information.

[1485] Speech conversion means: A synthetic speech engine (e.g., Amazon Polly) that converts the generated text data into speech data.

[1486] Audio output means: A speaker that plays audio data.

[1487] Learning tools: Machine learning algorithms that update user profiles based on recorded usage history and improve response accuracy.

[1488] Emotion recognition means: An emotion analysis engine (e.g., Microsoft Azure Face API) that analyzes the user's tone of voice and facial expressions to recognize their emotions.

[1489] Response modifiers: Algorithms that adjust the tone and content of responses based on perceived emotions.

[1490] Specific examples

[1491] When a user checks the weather

[1492] 1. User: Asks into smartphone, "What's the weather like today?"

[1493] 2. Device: Records audio and sends it to the server.

[1494] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[1495] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[1496] 5. Server: Converts the text response into audio data and sends it to the device.

[1497] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[1498] When users speak with emotion

[1499] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[1500] 2. Device: Records audio and sends it to the server.

[1501] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[1502] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[1503] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[1504] 6. Server: Converts the text response into audio data and sends it to the device.

[1505] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[1506] Prompt Sentence Examples

[1507] User: Asks smartphone, "What's the weather like today?"

[1508] Device: Records audio and sends it to the server.

[1509] Server: Converts voice data to text, retrieves weather information, and generates a response.

[1510] Terminal: The generated response is played back as audio data to convey information to the user.

[1511] This system allows users to receive individually tailored responses and high-quality communication that takes emotions into consideration in response to various questions and concerns in everyday life.

[1512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1513] Step 1. Perform Initial Configuration

[1514] Input: The user uses the touchscreen or keyboard to enter information such as name, date of birth, blood type, hobbies and preferences into the device.

[1515] How it works: The device receives the information entered and sends it to the server, which stores it in a database and creates a profile for each user.

[1516] Output: A user profile is generated and stored in a database.

[1517] Step 2: Accepting voice input

[1518] Input: The user speaks into the smartphone's microphone to ask a question such as "What's the weather like today?"

[1519] How it works: The device records voice input and sends the voice data to the server.

[1520] Output: The audio data is sent to the server.

[1521] Step 3: Performing speech recognition

[1522] Input: The server receives the voice data sent from the terminal.

[1523] How it works: The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data.

[1524] Output: The audio data is converted to text data.

[1525] Step 4: Performing response generation

[1526] Input: Text data obtained through speech recognition and user profile information.

[1527] How it works: The server parses the text data and generates an adapted response based on user profile information. For example, it generates a query to get weather information and generates a response such as "Today's weather is sunny" based on information from a weather API (e.g., the OpenWeatherMap API).

[1528] Output: The generated text response.

[1529] Step 5: Perform audio conversion

[1530] Input: The server-generated text response.

[1531] How it works: The server uses a synthetic speech engine (e.g., Amazon Polly) to convert text data into audio data.

[1532] Output: Audio data is generated.

[1533] Step 6: Deliver a voice response

[1534] Input: Audio data is sent from the server to the device.

[1535] Operation: The terminal plays the received audio data and conveys the response to the user.

[1536] Output: A voice response such as "The weather is sunny today" is played to the user.

[1537] Step 7: Sentiment Analysis

[1538] Input: The server receives the user's voice data.

[1539] How it works: The server uses an emotion recognition engine (e.g., Microsoft Azure Face API) to analyze the user's voice tone and facial expression data to recognize emotions. For example, if the voice tone is low, it will recognize sadness.

[1540] Output: Recognized emotion information.

[1541] Step 8: Adjusting the response

[1542] Input: Recognized emotion information and generated text response.

[1543] How it works: The server adjusts the tone and content of the response based on the emotional information. For example, if the user speaks in a sad tone, the server changes the response to a warmer "I see, good job. Tell me something."

[1544] Output: The tailored text response.

[1545] Step 9: Convert the audio after adjustment

[1546] Input: The tailored text response.

[1547] How it works: The server again uses its synthetic speech engine to convert the adjusted text response into audio data.

[1548] Output: Adjusted audio data.

[1549] Step 10: Deliver emotionally sensitive voice responses

[1550] Input: The adjusted audio data is sent from the server to the device.

[1551] Action: The device plays back the received audio data and provides an emotionally sensitive response to the user.

[1552] Output: A voice response such as "Okay, great work. Tell us something" is played to the user.

[1553] Step 11: Record your usage history

[1554] Input: Conversation history with the user and system usage history.

[1555] Operation: The terminal records these histories and sends them to the server in batches in fixed groups.

[1556] Output: The usage history is sent to the server.

[1557] Step 12: Learning based on usage history

[1558] Input: Usage history sent to the server.

[1559] How it works: The server uses learning techniques to update the user's profile and improve the accuracy of responses based on this, as well as the accuracy of the emotion recognition engine.

[1560] Output: Updated user profile and improved training data.

[1561] (Application example 2)

[1562] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1563] While systems exist to help users of all ages, including the elderly and children, solve various questions and concerns in their daily lives, these systems lack the ability to appropriately understand users' emotions and tailor responses. Furthermore, certain services, such as food delivery, require personalized suggestions based on users' preferences and past ordering history, but existing systems do not adequately address these individual needs. Furthermore, technology is needed to effectively utilize voice input and enrich the user experience.

[1564] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for inputting user information as initial settings; means for saving the input user information in a database; voice input means for accepting voice input from the user; voice recognition means for converting the accepted voice input into text data; response generation means for analyzing the converted text data and generating a response based on the user information; voice conversion means for converting the generated response into voice data; voice output means for playing the voice data; learning means for recording usage history and updating user information based on the recorded usage history; emotion recognition means for analyzing user emotions; means for adjusting responses based on the emotion recognition; and means for suggesting personalized menus and services. This enables appropriate and personalized responses that take the user's emotions into consideration.

[1565] "User information" refers to individual data such as the system user's name, date of birth, blood type, hobbies and preferences.

[1566] "Input means" refers to a device or interface that allows a user to input detailed information.

[1567] A "database" is a storage device for storing user information and usage history.

[1568] "Voice input means" refers to a device or function that accepts voice instructions or questions from the user.

[1569] "Speech recognition means" refers to technology or a system that converts voice data into text data.

[1570] The "response generation means" is a device or program that analyzes text data and generates an appropriate response based on user information.

[1571] A "speech conversion means" is a technology or device that converts the generated text response into audio data.

[1572] The "audio output means" is a device that reproduces audio data and conveys a response to the user.

[1573] "Learning means" refers to functions and technologies that improve the system based on usage history and update user information.

[1574] "Emotion recognition means" refers to a system or technology that analyzes voice data and user facial expression data to recognize the user's emotions.

[1575] "Response tailoring" means techniques or programs that change the tone or content of responses based on perceived emotions.

[1576] "Means for making personalized menu and service suggestions" refers to technologies and systems that provide customized suggestions based on a user's hobbies, preferences, and past ordering history.

[1577] A "generative AI model" is an AI technology or program that generates individually adapted responses based on a user profile.

[1578] A "prompt" is an instruction or question that is input into a generative AI model to generate a specific response.

[1579] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns about their daily lives using voice input and emotion recognition. The following describes in detail an embodiment of the present invention.

[1580] System Configuration

[1581] Terminal: Refers to a device used by a user, such as a smartphone or tablet. Includes input means, voice input means, and voice output means.

[1582] Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, learning means, and emotion recognition means.

[1583] Initial Setup

[1584] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[1585] Server: Stores the received user information in a database and generates a profile for each user.

[1586] Voice dialogue

[1587] User: A user uses the system on a daily basis and inputs questions or inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[1588] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[1589] Server: Receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The converted text data is analyzed by a response generation method, and a response based on the user information is generated.

[1590] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[1591] Terminal: Plays back the audio data and conveys responses to the user.

[1592] Emotion recognition and response regulation

[1593] Server: The emotion recognition means analyzes the voice data and the user's facial expression data to recognize the user's emotional state. For example, emotions such as anger, sadness, and joy can be extracted from the voice tone and speaking style.

[1594] Server: Adjust the tone and content of the response based on the perceived emotion. If the user sounds sad, change the response to something warmer.

[1595] Server: Converts the adjusted response into audio data and sends it to the device.

[1596] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[1597] Usage history and learning

[1598] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1599] Server: Based on the received usage history, the server uses learning methods to update the user's profile, thereby improving the accuracy and relevance of future responses.

[1600] Server: As part of the learning process, the server reflects usage history to improve the accuracy of emotion recognition tools.

[1601] Food delivery application example

[1602] When a user says, "It's sunny today and I'm in a good mood, so I want to eat something special," the device records the speech and sends it to the server. The server converts the speech to text and uses emotion recognition to identify the "good mood." A generative AI model (e.g., OpenAI GPT-4) suggests a special menu item based on past ordering history and the user's preferences. The server converts the suggestion into voice data and sends it to the device, which responds, "How about a special sushi set today?"

[1603] Prompt Sentence Examples

[1604] User: "It's sunny today and I'm feeling great, so I want to eat something special."

[1605] Prompt the generative AI model:

[1606] Suggest special menu items based on past order history and user profile. The user is currently in a good mood.

[1607] Example response: "How about a special sushi set today?"

[1608] In this way, the present invention provides personalized responses based on the user's details and emotional state, which is particularly useful in food delivery and everyday query resolution.

[1609] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1610] Step 1:

[1611] The user inputs voice data. The user gives a command by voice, such as "It's sunny today and I'm in a good mood, so I want to eat something special." The input data is voice data.

[1612] Step 2:

[1613] The terminal records the voice data and transmits the voice data to the server. Here, the input is the user's voice data and the output is the transmission of the voice data to the server.

[1614] Step 3:

[1615] The server receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The input is the transmitted voice data, and the output is text data.

[1616] Step 4:

[1617] The server analyzes the text data and uses emotion recognition to analyze the user's emotions. For example, it extracts the positive emotion of "feeling good." The input is the text data converted from speech, and the output is the emotional state ("feeling good").

[1618] Step 5:

[1619] The server uses a generative AI model (e.g., OpenAI GPT-4) to suggest a personalized menu based on the user's past ordering history and profile. Here, a prompt sentence is input to the generative AI model. The inputs are the user profile, emotional state, and text data. The output is a suggestion: "How about a special sushi set today?"

[1620] Step 6:

[1621] The server converts the generated text response into speech data. A speech conversion means is used to convert the text data into speech data. The input is the proposed text data and the output is the speech data.

[1622] Step 7:

[1623] The server sends the audio data to the terminal. The input is the converted audio data, and the output is the data sent to the terminal.

[1624] Step 8:

[1625] The terminal plays back the audio data and makes a suggestion to the user by voice. Specifically, it says to the user, "How about a special sushi set today?" The input is the audio data sent from the server, and the output is the audio playback.

[1626] Step 9:

[1627] The terminal records the user's usage history and sends that data to the server. The input is the user's usage history data, and the output is transmission to the server.

[1628] Step 10:

[1629] The server updates the user profile based on the received usage history to improve the accuracy and relevance of future responses. The input is the transmitted usage history data, and the output is the updated user profile.

[1630] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1631] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1632] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1633] [Fourth embodiment]

[1634] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1635] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1636] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1637] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1638] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1639] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1640] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1641] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1642] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1643] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1644] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1645] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1646] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1647] The present invention relates to a next-generation smartphone system that enables users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they may have in their daily lives. A specific embodiment of the present invention will be described in detail below.

[1648] System Configuration

[1649] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[1650] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[1651] Initial Setup

[1652] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[1653] Server: Stores the received user information in a database and generates a profile for each user.

[1654] Voice dialogue

[1655] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[1656] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[1657] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[1658] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[1659] Terminal: Plays back the audio data and conveys responses to the user.

[1660] Usage history and learning

[1661] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1662] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[1663] Specific examples

[1664] When a user checks the weather

[1665] 1. User: Asks into smartphone, "What's the weather like today?"

[1666] 2. Device: Records audio and sends it to the server.

[1667] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Based on the user's location information, the server obtains weather information and generates a text response such as "Today's weather is sunny."

[1668] 4. Server: Converts the text response into audio data and sends it to the device.

[1669] 5. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[1670] If a user wants to check out gardening trends

[1671] 1. User: Says to their smartphone, "Tell me about the latest gardening trends."

[1672] 2. Device: Records audio and sends it to the server.

[1673] 3. Server: Converts the voice data into text and generates queries to obtain the latest information on gardening. Based on the user's hobbies and preferences, it searches and aggregates relevant trend information and generates a text response such as, "The latest gardening trend is arranging succulents."

[1674] 4. Server: Converts the created text response into audio data and sends it to the device.

[1675] 5. Terminal: Plays audio data and tells the user, "The latest gardening trend is arranging succulents."

[1676] summary

[1677] This invention provides a system that provides personalized responses based on detailed user information and continuously improves response accuracy by learning from usage history. This system allows users to easily consult with a service provider about their questions and concerns in their daily lives, making it particularly useful for the elderly and children.

[1678] The processing flow will be explained below.

[1679] Step 1:

[1680] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[1681] Step 2:

[1682] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[1683] Step 3:

[1684] Terminal: Temporarily stores the entered information and sends it to the server.

[1685] Step 4:

[1686] Server: Generates a user profile based on the received user information and stores it in a database.

[1687] Step 5:

[1688] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[1689] Step 6:

[1690] Terminal: Records the user's voice and sends it to the server as audio data.

[1691] Step 7:

[1692] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[1693] Step 8:

[1694] Server: Analyzes the text data and generates an appropriate response by referencing the user profile. For example, it obtains weather information based on location information and generates a response such as "Today's weather is sunny."

[1695] Step 9:

[1696] Server: Converts the generated text response into audio data and sends it to the device.

[1697] Step 10:

[1698] Terminal: Plays back audio data and tells the user, "Today's weather is sunny."

[1699] Step 11:

[1700] Terminal: Records conversation history and usage status with the user and sends it to the server.

[1701] Step 12:

[1702] Server: Analyzes the received usage history using learning methods and updates the user profile. For example, if a user frequently asks a specific question, the server prioritizes obtaining the latest information on that topic.

[1703] The above is a specific flow of system processing according to the present invention.

[1704] Example 1

[1705] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1706] Conventional voice assistant systems could only provide standardized responses, making it difficult to provide responses based on a user's individual hobbies, preferences, or profile information. Furthermore, they lacked a mechanism for effectively learning from a user's usage history, making it impossible to improve response accuracy in the future. The objective of this invention is to provide a system that utilizes a user's individual information to provide more personalized and adaptive responses.

[1707] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1708] In this invention, the server includes an input means for inputting user information as initial settings, a means for saving the input user information in a database, and a voice input means for accepting voice input from the user, thereby making it possible to provide individually adapted responses based on the user's detailed information.

[1709] "User information" refers to detailed information such as name, date of birth, blood type, hobbies and preferences that the user enters during initial setup.

[1710] "Input means" refers to the interface through which a user inputs information into the system, including an on-screen keyboard and microphone input.

[1711] A "database" is a centralized data store that stores data such as user information and usage history, and allows for searching and updating as needed.

[1712] "Voice input means" refers to a device or interface for receiving voice input from a user, including a microphone.

[1713] "Speech recognition means" refers to technology or software for analyzing received voice input and converting it into corresponding text data.

[1714] The "response generation means" refers to a process or technology for generating an appropriate response based on the converted text data and user information.

[1715] "Speech conversion means" refers to technology or software for converting the generated text response into audio data.

[1716] "Audio output means" refers to a device or interface for conveying generated audio data to the user, including a speaker.

[1717] The "learning means" refers to a process or technology that analyzes usage history, updates user information, and improves response accuracy from the next time onward.

[1718] "Communication Method" means the technology or protocol used to transmit and receive voice input and response data over a network, including, but not limited to, the HTTP or HTTPS protocols.

[1719] A "generative AI model" is a model or technology that uses artificial intelligence to generate appropriate responses to user input. A specific example is a natural language processing model.

[1720] MODE FOR CARRYING OUT THE INVENTION

[1721] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A specific embodiment of the present invention will be described in detail below.

[1722] 1. System Configuration

[1723] The system mainly consists of devices (terminals) used by users and a server that processes data.

[1724] 1.1 Terminal

[1725] A terminal is a device operated by a user, such as a smartphone or tablet. The hardware includes a microphone, speaker, and touch screen, and the software includes a voice recording application and a network communication module (HTTP / HTTPS).

[1726] 1.2 Server

[1727] The server acts as a central processing unit and includes the following software modules:

[1728] Voice recognition software (e.g., Google Speech-to-Text API)

[1729] Response generation software (e.g., OpenAI's GPT-3)

[1730] Text-to-speech software (e.g., Amazon Polly)

[1731] Database Management Systems

[1732] Learning Algorithm

[1733] 2. Initial Setup

[1734] When a user uses the system for the first time, they enter detailed information such as their name, date of birth, blood type, hobbies and preferences on the initial setup screen of their smartphone or tablet. This information is sent from the device to the server, where it is stored in a database and a profile is created.

[1735] 3. Voice Dialogue

[1736] Users can use this system to input everyday questions and inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[1737] 3.1 Voice input

[1738] When a user asks a question by voice, the device records the voice through a microphone, and the recorded voice data is sent to the server through the network communication module.

[1739] 3.2 Speech Recognition

[1740] The server converts the received voice data into text data using voice recognition software, specifically using a voice recognition service such as the Google Speech-to-Text API.

[1741] 3.3 Response Generation

[1742] The converted text data is then analyzed using a generative AI model (e.g., OpenAI's GPT-3). Based on the analyzed data and user profile information, an appropriate response is generated.

[1743] 4. Voice response

[1744] The generated text response is then converted back into speech using speech conversion software (e.g., Amazon Polly), which converts the text into speech and sends it to the device, which then plays it back to the user.

[1745] 5. Usage history and learning

[1746] The system records the user's conversation history and usage history. This history data is sent to the server and used as training data for the generative AI model. This data is continuously trained to improve response accuracy in future.

[1747] Specific examples

[1748] Check the weather information

[1749] User: "What's the weather like today?"

[1750] Device: Record audio and send it to the server

[1751] Server: Speech recognition, weather information acquisition, response generation such as "Today's weather is sunny"

[1752] Server: Converts the response into audio and sends it to the device

[1753] Device: Play "Today's weather is sunny" to the user

[1754] Check out gardening trends

[1755] User: "What are the latest gardening trends?"

[1756] Device: Record audio and send it to the server

[1757] Server: Speech recognition, analysis of the latest trend information, and generation of a response such as "The latest gardening trend is arranging succulents."

[1758] Server: Converts the response into audio and sends it to the device

[1759] Device: Play the message to the user, "The latest gardening trend is arranging succulents."

[1760] This invention allows users to quickly obtain more personalized and actionable information, which will be particularly beneficial for the elderly and children.

[1761] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1762] Step 1:

[1763] User: Enters detailed information such as name, date of birth, blood type, hobbies, and preferences on the initial setup screen of a smartphone or tablet. As input, the user's individual information is provided to the system. As output, this information is sent from the device to the server.

[1764] Step 2:

[1765] Terminal: Collects information entered by the user and sends the aggregated data to the server. As input, the user's initial settings information is given. As output, the data is sent to the server in a structured data format.

[1766] Step 3:

[1767] Server: Stores the received user information in a database and generates a profile for each user. The input is the initial setting information sent from the terminal. The output is a user profile generated in the database.

[1768] Step 4:

[1769] User: Inputs a question or inquiry into the device by voice. For example, say, "What's the weather like today?" The user's voice is input into the device as input. The output is recorded as voice data.

[1770] Step 5:

[1771] Terminal: Records the user's voice through a microphone. The recorded voice data is sent to the server through a network communication module. As input, the user's voice data is obtained from the microphone. As output, a voice data file is generated that is sent to the server.

[1772] Step 6:

[1773] Server: Receives and temporarily stores voice data. Converts the voice data into text using speech recognition software (e.g., Google Speech-to-Text API). The input is the voice data sent from the device. The output is text data.

[1774] Step 7:

[1775] Server: Receives the converted text data and analyzes it. It uses a generative AI model (e.g., OpenAI's GPT-3) to generate an appropriate response. The input is the text data obtained from speech recognition. The output is a text response based on the user's profile.

[1776] Step 8:

[1777] Server: Converts the generated text response into voice data using speech conversion software (e.g., Amazon Polly). The input is the text data obtained from the response generation means. The output is voice data.

[1778] Step 9:

[1779] Server: The converted audio data is sent to the device using HTTP or HTTPS protocol. The converted audio data is stored on the server as input. The audio data sent to the device is generated as output.

[1780] Step 10:

[1781] Terminal: Plays the received audio data. Provides a voice response to the user. As input, it receives audio data from the server. As output, it generates audio that is played back to the user.

[1782] Step 11:

[1783] Terminal: Records conversation history and usage history with the user and periodically sends this to the server. As input, interaction data with the user is acquired from the microphone. As output, history data is generated that is sent to the server and recorded.

[1784] Step 12:

[1785] Server: Analyzes the received usage history and performs learning based on the generative AI model. This improves response accuracy from the next time onwards. The input is the usage history data sent from the device. The output is an improved generative AI model.

[1786] (Application example 1)

[1787] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1788] There is a need for a system that allows users of a wide range of ages, from the elderly to children, to easily consult by voice about their questions and concerns in daily life, and that can receive appropriate dietary suggestions that take their health into consideration.Current systems have difficulty providing appropriate responses based on each user's health condition, hobbies, and preferences, so a method is needed to respond quickly and accurately to a wide range of individual requests.

[1789] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1790] In this invention, the server includes input means for inputting user information as initial settings, means for saving the input user information in a database, voice input means for accepting voice input from the user, voice recognition means for converting the accepted voice input into text data, response generation means for analyzing the converted text data and generating a response based on the user information, voice conversion means for converting the generated response into voice data, voice output means for playing the voice data, learning means for recording usage history and updating user information based on the recorded usage history, and meal suggestion means for generating meal suggestions taking the user's health condition into consideration, thereby enabling the user to receive appropriate meal suggestions and providing responses tailored to the user's individual health condition, hobbies, and preferences.

[1791] "User information" refers to all data including the user's personal information, health status, hobbies, preferences, usage history, etc.

[1792] "Input means" refers to a device or interface used by a user to input information.

[1793] "Means for storing in a database" refers to a system that stores input user information in digital form.

[1794] "Voice input means" refers to a device or interface for collecting voice from a user and inputting it into the system.

[1795] "Speech recognition means" refers to a technique or device for converting received voice input into text data.

[1796] The "response generation means" refers to a function that analyzes the converted text data and generates an appropriate response based on the user information.

[1797] "Speech conversion means" refers to a technology or device that converts a generated text response into audio data.

[1798] "Audio output means" refers to a device for reproducing audio data and providing audio information to a user.

[1799] The "learning means" refers to a technology or device for recording usage history, updating user information based on the recorded usage history, and improving the accuracy of responses.

[1800] The "meal suggestion means" refers to a function that generates appropriate meal suggestions based on the user's health condition and preferences.

[1801] MODE FOR CARRYING OUT THE INVENTION

[1802] The present invention relates to a system for making dietary suggestions that take into account a user's health condition using voice input from the user. Specific embodiments will be described in detail below.

[1803] System Configuration

[1804] Hardware Configuration

[1805] 1. Device:

[1806] Voice input method: Use the microphone built into devices such as smartphones and tablets.

[1807] Audio output means: Speaker, headset, etc.

[1808] 2. Server:

[1809] Database: A database for storing user information and usage history.

[1810] Processor: A central processing unit that processes voice data, converts text, generates responses, and performs learning functions.

[1811] Software Configuration

[1812] 1. Speech Recognition Software:

[1813] speech_recognition: A speech-to-text library that converts what the user says into text form.

[1814] 2. Text-to-Speech Software:

[1815] gTTS: A text-to-speech library that converts generated text responses into audio data.

[1816] 3. Data management software:

[1817] Database management system: Stores and manages user profiles and usage history.

[1818] 4. Natural Language Processing Software:

[1819] Generative AI models: Used to generate appropriate responses to user questions.

[1820] Processing Flow

[1821] 1. Initial Setup:

[1822] Device: The user is asked to enter detailed information such as name, date of birth, hobbies, and preferences. This information is sent from the device to the server.

[1823] Server: Stores the received user information in a database and creates a profile for each user.

[1824] 2. Voice interaction:

[1825] User: Speaks a question, for example, "What are your meal recommendations?"

[1826] Device: Records voice input and sends it to the server.

[1827] Server: Converts voice data into text and generates appropriate responses based on the user's profile. Analyzes the questions using a generative AI model and creates meal suggestions based on the user's health status, hobbies, and preferences.

[1828] Server: Generates meal suggestions in text format, converts them into audio data, and sends them to the device.

[1829] Terminal: Plays the audio data and communicates responses to the user.

[1830] 3. Usage history and learning:

[1831] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1832] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[1833] Specific examples

[1834] When the user checks out meal suggestions

[1835] 1. User: Asks into smartphone, "What meal would you recommend?"

[1836] 2. Device: Records audio and sends it to the server.

[1837] 3. Server: Converts the voice data into text and generates meal suggestions based on the user's health status and preferences. For example, it generates a text response such as "The recommended meal is a low-carb salad."

[1838] 4. Server: Converts the text response into audio data and sends it to the device.

[1839] 5. Device: Plays audio data and tells the user, "The recommended meal is a low-carb salad."

[1840] Example prompt sentence:

[1841] "Prompt: 'Give me some meal suggestions. User is 68 years old and has diabetes so prefers a low-carb diet.'"

[1842] "Prompt: 'Based on the user's preferences, what are the recommended food delivery options? The user prefers Japanese food and is over 60 years old.'"

[1843] This allows users to receive meal suggestions based on their health status and preferences. The system learns from usage history to improve the accuracy of responses and realize personalized responses for each user.

[1844] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1845] Step 1:

[1846] Initial Setup

[1847] Terminal: A screen is displayed for the user to enter detailed information such as name, date of birth, hobbies, preferences, and health status. The user enters this information.

[1848] Input: Information entered by the user.

[1849] Output: User details are sent from the device to the server.

[1850] Data processing / calculation: Compile user details into a single dataset.

[1851] Step 2:

[1852] Saving user information

[1853] Server: Stores the received user information in a database. Creates a profile for each user.

[1854] Input: User information sent from the device.

[1855] Output: User profile stored in the database.

[1856] Data processing / calculation: User information is organized as structured data and stored in a database.

[1857] Step 3:

[1858] Accepting voice input

[1859] User: Speaks questions into the smartphone.

[1860] Input: Spoken input such as "What meal would you recommend?"

[1861] Output: Recorded audio data is generated.

[1862] Specific behavior: Records the user's voice using a microphone.

[1863] Step 4:

[1864] Audio data conversion

[1865] Device: Sends recorded audio data to the server.

[1866] Input: Recorded audio data.

[1867] Output: The audio data is sent to the server.

[1868] Specific operation: Audio data is sent to the server using a transfer protocol (e.g. HTTP).

[1869] Step 5:

[1870] Voice Recognition

[1871] Server: Converts the audio data into text data.

[1872] Input: Received audio data.

[1873] Output: Questions converted to text data.

[1874] Data processing / calculation: Analyze the audio data using the speech_recognition library and convert it into text format.

[1875] Step 6:

[1876] Response Generation

[1877] Server: Analyzes text data and generates appropriate meal suggestions based on the user's health condition and preferences.

[1878] Input: Text data, user profile.

[1879] Output: A text response such as "Recommended meal is a low-carb salad."

[1880] Data processing / computation: A generative AI model is used to analyze the question and generate a response based on the prompt.

[1881] Step 7:

[1882] Text-to-speech response

[1883] Server: Converts the generated text response into audio data.

[1884] Input: A text response.

[1885] Output: The response converted into audio data.

[1886] Data processing / calculation: Convert text to speech data using the gTTS library.

[1887] Step 8:

[1888] Sending a voice response

[1889] Server: Sends audio data to the device.

[1890] Input: The response converted to audio data.

[1891] Output: The audio data sent to the device.

[1892] Specific operation: Audio data is sent to the device using a transfer protocol (e.g. HTTP).

[1893] Step 9:

[1894] Playing a voice response

[1895] Terminal: Plays back the received audio data and conveys the response to the user.

[1896] Input: Audio data sent from the server.

[1897] Output: The audio response the user hears.

[1898] Specific behavior: Plays audio data through speakers or a headset.

[1899] Step 10:

[1900] Recording and learning usage history

[1901] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1902] Input: Conversation history, system usage history.

[1903] Output: Usage history sent to the server.

[1904] Specific operation: Organize usage history as structured data and send it.

[1905] Step 11:

[1906] Data Update

[1907] Server: Updates user information based on the received usage history to improve the accuracy of responses from the next time onwards.

[1908] Input: Submitted usage history.

[1909] Output: The updated user profile.

[1910] Data processing / calculation: Using a learning algorithm, usage history is analyzed and user profiles are updated.

[1911] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1912] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. The following describes in detail an embodiment of the present invention.

[1913] System Configuration

[1914] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. It includes input means, voice input means, and voice output means.

[1915] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, and learning means.

[1916] 3. Emotion engine: Provides technology to analyze the user's voice and facial expressions and recognize the user's emotions.

[1917] Initial Setup

[1918] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[1919] Server: Stores the received user information in a database and generates a profile for each user.

[1920] Voice dialogue

[1921] Users: They use the system on a daily basis and input questions and inquiries by voice. For example, they ask questions such as, "What's the weather like today?" or "What are the latest gardening trends?"

[1922] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[1923] Server: Receives voice data and converts it into text data using a speech recognition means. The converted text data is analyzed by a response generation means, and an individually adapted response is generated based on the user's profile.

[1924] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[1925] Terminal: Plays back the audio data and conveys responses to the user.

[1926] Emotion recognition and response regulation

[1927] Server: The emotion engine analyzes voice data and the user's facial expression data to recognize the user's emotional state. For example, it extracts emotions such as anger, sadness, and joy from the user's tone of voice and speaking style.

[1928] Server: Adjust the tone of the response based on the perceived emotion. For example, if the user sounds sad, change the response to something warmer.

[1929] Server: Converts emotion-adjusted responses into audio data and sends it to the device.

[1930] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[1931] Usage history and learning

[1932] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[1933] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[1934] Server: As part of the learning process, the server reflects usage history to improve the accuracy of the emotion recognition engine.

[1935] Specific examples

[1936] When a user checks the weather

[1937] 1. User: Asks into smartphone, "What's the weather like today?"

[1938] 2. Device: Records audio and sends it to the server.

[1939] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[1940] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[1941] 5. Server: Converts the text response into audio data and sends it to the device.

[1942] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[1943] When users speak with emotion

[1944] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[1945] 2. Device: Records audio and sends it to the server.

[1946] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[1947] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[1948] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[1949] 6. Server: Converts the text response into audio data and sends it to the device.

[1950] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[1951] summary

[1952] The present invention provides a system that provides personalized responses based on detailed user information and further improves the quality of responses by incorporating emotion recognition. This system allows users to easily consult with a system about their questions and concerns in daily life, and can be a valuable support for the elderly and children in particular.

[1953] The processing flow will be explained below.

[1954] Step 1:

[1955] Terminal: Displays a screen prompting the user to enter detailed information. Provides input fields for "Name," "Date of Birth," "Blood Type," "Hobbies and Preferences," etc.

[1956] Step 2:

[1957] User: Follow the instructions on the input screen and enter your information. For example, enter "Name: Taro," "Birthdate: January 1, 1950," "Blood type: A," and "Hobbies: Reading and gardening."

[1958] Step 3:

[1959] Terminal: Temporarily stores the entered information and sends it to the server.

[1960] Step 4:

[1961] Server: Generates a user profile based on the received user information and stores it in a database.

[1962] Step 5:

[1963] User: When they have a question or need advice in their daily life, they speak to their smartphone and ask, "What's the weather like today?" or "What are the latest gardening trends?"

[1964] Step 6:

[1965] Terminal: Records the user's voice and sends it to the server as audio data.

[1966] Step 7:

[1967] Server: Receives voice data and converts it into text data using a voice recognition tool. For example, it converts the data into text such as "What's the weather like today?"

[1968] Step 8:

[1969] Server: The emotion engine analyzes the voice data and recognizes the user's emotions, for example, determining whether the user is happy or sad based on their tone and speaking style.

[1970] Step 9:

[1971] Server: Based on the text data and the recognized emotion data, the server refers to the user profile and generates an appropriate response. For example, when providing a sunny weather forecast, if the user is happy, the server generates "The weather is sunny today! Have a nice day!"

[1972] Step 10:

[1973] Server: Converts the generated text response into audio data and sends it to the device.

[1974] Step 11:

[1975] Terminal: Plays back the audio data and conveys responses to the user.

[1976] Step 12:

[1977] Terminal: Records conversation history and usage status with the user and sends it to the server.

[1978] Step 13:

[1979] Server: Updates the user profile using learning methods based on the received usage history, thereby improving the accuracy and relevance of future responses.

[1980] Specific examples

[1981] When a user checks the weather

[1982] 1. User: Asks into smartphone, "What's the weather like today?"

[1983] 2. Device: Records audio and sends it to the server.

[1984] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[1985] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[1986] 5. Server: Converts the text response into audio data and sends it to the device.

[1987] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[1988] When users speak with emotion

[1989] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[1990] 2. Device: Records audio and sends it to the server.

[1991] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[1992] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[1993] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[1994] 6. Server: Converts the text response into audio data and sends it to the device.

[1995] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[1996] The above is a specific flow of system processing according to the present invention.

[1997] Example 2

[1998] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1999] In modern society, there is a demand for systems that allow users to easily consult with others about the questions and concerns they have in their daily lives. To provide appropriate support, particularly to a wide range of age groups, including the elderly and children, it is important to provide personalized support based on each user's hobbies, preferences, and emotional state. However, while conventional systems can accept voice input and generate basic responses, they lack the ability to recognize the user's emotions and adjust responses accordingly, making it difficult to provide higher-quality communication. Furthermore, they lacked effective learning methods to improve the accuracy of providing customized responses based on user profiles.

[2000] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2001] In this invention, the server includes emotion recognition means for analyzing the user's voice tone and facial expression data to recognize the user's emotion, response adjustment means for adjusting the tone and content of responses based on the recognized emotion, and learning means for recording usage history and updating user information based on the recorded usage history, thereby enabling higher quality communication that takes the user's emotion into consideration.

[2002] "User Information" refers to basic personal information about a User, including name, date of birth, blood type, hobbies and preferences, etc.

[2003] "Input means" refers to an interface for inputting user information, such as a touch screen or keyboard on a smartphone or tablet.

[2004] A "database" is a system for storing and managing data such as user information and usage history.

[2005] The "voice input means" is a device for receiving voice input from the user, such as a microphone.

[2006] "Speech recognition means" is a technology for converting voice data into text data, and uses a voice recognition engine.

[2007] The "response generation means" is a technique for generating an appropriate response based on user information and analyzed text data.

[2008] The "voice conversion means" is a technique for converting the generated text data into voice data.

[2009] The "audio output means" is a device for transmitting audio data to the user, such as a speaker.

[2010] "Learning means" is a technology for updating user information based on recorded usage history and improving the system's response accuracy.

[2011] "Emotion recognition means" is a technology for analyzing the user's voice tone and facial expression data to recognize the user's emotional state.

[2012] "Response adjustment means" is a technology that adjusts the tone and content of a response based on the recognized emotion to provide communication that is appropriate for the user.

[2013] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns they have about their daily lives. A detailed description of a specific embodiment of this system is given below.

[2014] System Configuration

[2015] 1. Terminal: Refers to a device used by a user, such as a smartphone or tablet. These terminals include input means (touchscreen or keyboard), voice input means (microphone), and voice output means (speaker).

[2016] 2. Server: A central processing unit that processes input information and voice data from users and generates responses. It includes a database (for storing user information), a voice recognition means (for converting voice data into text data), a response generation means (for analyzing text data and generating responses), a voice conversion means (for converting text data into voice data), a learning means (for analyzing usage history and updating profiles), an emotion recognition means (for analyzing voice tone and facial expressions to recognize emotions), and a response adjustment means (for adjusting responses based on recognized emotions).

[2017] Hardware and software details

[2018] Input method: Touchscreen and keyboard for entering user information.

[2019] Audio input means: A microphone that accepts audio input from the user.

[2020] Database: A data management system for storing user information and usage history.

[2021] Speech recognition means: A speech recognition engine (e.g., Google Speech-to-Text API) for converting voice data into text data.

[2022] Response generator: An algorithm (e.g., a natural language processing model) that analyzes the converted text data and generates a response based on user information.

[2023] Speech conversion means: A synthetic speech engine (e.g., Amazon Polly) that converts the generated text data into speech data.

[2024] Audio output means: A speaker that plays audio data.

[2025] Learning tools: Machine learning algorithms that update user profiles based on recorded usage history and improve response accuracy.

[2026] Emotion recognition means: An emotion analysis engine (e.g., Microsoft Azure Face API) that analyzes the user's tone of voice and facial expressions to recognize their emotions.

[2027] Response modifiers: Algorithms that adjust the tone and content of responses based on perceived emotions.

[2028] Specific examples

[2029] When a user checks the weather

[2030] 1. User: Asks into smartphone, "What's the weather like today?"

[2031] 2. Device: Records audio and sends it to the server.

[2032] 3. Server: Converts the voice data into text and generates a query to obtain weather information. Retrieves weather information based on the user's location information and generates a response such as "Today's weather is sunny."

[2033] 4. Server: The emotion engine analyzes the user's tone of voice and generates a normal response if no emotion is recognized.

[2034] 5. Server: Converts the text response into audio data and sends it to the device.

[2035] 6. Terminal: Plays the audio data and tells the user, "Today's weather is sunny."

[2036] When users speak with emotion

[2037] 1. User: Says sadly to their smartphone, "I'm not feeling very well today."

[2038] 2. Device: Records audio and sends it to the server.

[2039] 3. Server: Converts the speech data to text and recognizes that the user is saying, "I'm not feeling very well today."

[2040] 4. Server: The emotion engine analyzes the sad tone of the user and recognizes the emotion of sadness.

[2041] 5. Server: Adjust the response based on the perceived emotion and generate a warm response like, "I see, great work. Tell me something."

[2042] 6. Server: Converts the text response into audio data and sends it to the device.

[2043] 7. Terminal: Plays the audio data and tells the user, "I see, good work. Please tell us something."

[2044] Prompt Sentence Examples

[2045] User: Asks smartphone, "What's the weather like today?"

[2046] Device: Records audio and sends it to the server.

[2047] Server: Converts voice data to text, retrieves weather information, and generates a response.

[2048] Terminal: The generated response is played back as audio data to convey information to the user.

[2049] This system allows users to receive individually tailored responses and high-quality communication that takes emotions into consideration in response to various questions and concerns in everyday life.

[2050] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2051] Step 1. Perform Initial Configuration

[2052] Input: The user uses the touchscreen or keyboard to enter information such as name, date of birth, blood type, hobbies and preferences into the device.

[2053] How it works: The device receives the information entered and sends it to the server, which stores it in a database and creates a profile for each user.

[2054] Output: A user profile is generated and stored in a database.

[2055] Step 2: Accepting voice input

[2056] Input: The user speaks into the smartphone's microphone to ask a question such as "What's the weather like today?"

[2057] How it works: The device records voice input and sends the voice data to the server.

[2058] Output: The audio data is sent to the server.

[2059] Step 3: Performing speech recognition

[2060] Input: The server receives the voice data sent from the terminal.

[2061] How it works: The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data.

[2062] Output: The audio data is converted to text data.

[2063] Step 4: Performing response generation

[2064] Input: Text data obtained through speech recognition and user profile information.

[2065] How it works: The server parses the text data and generates an adapted response based on user profile information. For example, it generates a query to get weather information and generates a response such as "Today's weather is sunny" based on information from a weather API (e.g., the OpenWeatherMap API).

[2066] Output: The generated text response.

[2067] Step 5: Perform audio conversion

[2068] Input: The server-generated text response.

[2069] How it works: The server uses a synthetic speech engine (e.g., Amazon Polly) to convert text data into audio data.

[2070] Output: Audio data is generated.

[2071] Step 6: Deliver a voice response

[2072] Input: Audio data is sent from the server to the device.

[2073] Operation: The terminal plays the received audio data and conveys the response to the user.

[2074] Output: A voice response such as "The weather is sunny today" is played to the user.

[2075] Step 7: Sentiment Analysis

[2076] Input: The server receives the user's voice data.

[2077] How it works: The server uses an emotion recognition engine (e.g., Microsoft Azure Face API) to analyze the user's voice tone and facial expression data to recognize emotions. For example, if the voice tone is low, it will recognize sadness.

[2078] Output: Recognized emotion information.

[2079] Step 8: Adjusting the response

[2080] Input: Recognized emotion information and generated text response.

[2081] How it works: The server adjusts the tone and content of the response based on the emotional information. For example, if the user speaks in a sad tone, the server changes the response to a warmer "I see, good job. Tell me something."

[2082] Output: The tailored text response.

[2083] Step 9: Convert the audio after adjustment

[2084] Input: The tailored text response.

[2085] How it works: The server again uses its synthetic speech engine to convert the adjusted text response into audio data.

[2086] Output: Adjusted audio data.

[2087] Step 10: Deliver emotionally sensitive voice responses

[2088] Input: The adjusted audio data is sent from the server to the device.

[2089] Action: The device plays back the received audio data and provides an emotionally sensitive response to the user.

[2090] Output: A voice response such as "Okay, great work. Tell us something" is played to the user.

[2091] Step 11: Record your usage history

[2092] Input: Conversation history with the user and system usage history.

[2093] Operation: The terminal records these histories and sends them to the server in batches in fixed groups.

[2094] Output: The usage history is sent to the server.

[2095] Step 12: Learning based on usage history

[2096] Input: Usage history sent to the server.

[2097] How it works: The server uses learning techniques to update the user's profile and improve the accuracy of responses based on this, as well as the accuracy of the emotion recognition engine.

[2098] Output: Updated user profile and improved training data.

[2099] (Application example 2)

[2100] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2101] While systems exist to help users of all ages, including the elderly and children, solve various questions and concerns in their daily lives, these systems lack the ability to appropriately understand users' emotions and tailor responses. Furthermore, certain services, such as food delivery, require personalized suggestions based on users' preferences and past ordering history, but existing systems do not adequately address these individual needs. Furthermore, technology is needed to effectively utilize voice input and enrich the user experience.

[2102] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for inputting user information as initial settings; means for saving the input user information in a database; voice input means for accepting voice input from the user; voice recognition means for converting the accepted voice input into text data; response generation means for analyzing the converted text data and generating a response based on the user information; voice conversion means for converting the generated response into voice data; voice output means for playing the voice data; learning means for recording usage history and updating user information based on the recorded usage history; emotion recognition means for analyzing user emotions; means for adjusting responses based on the emotion recognition; and means for suggesting personalized menus and services. This enables appropriate and personalized responses that take the user's emotions into consideration.

[2103] "User information" refers to individual data such as the system user's name, date of birth, blood type, hobbies and preferences.

[2104] "Input means" refers to a device or interface that allows a user to input detailed information.

[2105] A "database" is a storage device for storing user information and usage history.

[2106] "Voice input means" refers to a device or function that accepts voice instructions or questions from the user.

[2107] "Speech recognition means" refers to technology or a system that converts voice data into text data.

[2108] The "response generation means" is a device or program that analyzes text data and generates an appropriate response based on user information.

[2109] A "speech conversion means" is a technology or device that converts the generated text response into audio data.

[2110] The "audio output means" is a device that reproduces audio data and conveys a response to the user.

[2111] "Learning means" refers to functions and technologies that improve the system based on usage history and update user information.

[2112] "Emotion recognition means" refers to a system or technology that analyzes voice data and user facial expression data to recognize the user's emotions.

[2113] "Response tailoring" means techniques or programs that change the tone or content of responses based on perceived emotions.

[2114] "Means for making personalized menu and service suggestions" refers to technologies and systems that provide customized suggestions based on a user's hobbies, preferences, and past ordering history.

[2115] A "generative AI model" is an AI technology or program that generates individually adapted responses based on a user profile.

[2116] A "prompt" is an instruction or question that is input into a generative AI model to generate a specific response.

[2117] The present invention relates to a next-generation smartphone system that allows users of a wide range of ages, from the elderly to children, to seek advice on questions and concerns about their daily lives using voice input and emotion recognition. The following describes in detail an embodiment of the present invention.

[2118] System Configuration

[2119] Terminal: Refers to a device used by a user, such as a smartphone or tablet. Includes input means, voice input means, and voice output means.

[2120] Server: A central processing unit that processes input information and voice data from users and generates responses. It is equipped with a database, voice recognition means, response generation means, voice conversion means, learning means, and emotion recognition means.

[2121] Initial Setup

[2122] Terminal: During initial setup, an input screen is displayed to the user to input detailed information such as name, date of birth, blood type, hobbies, preferences, etc. Once the user enters the required information, it is sent to the server.

[2123] Server: Stores the received user information in a database and generates a profile for each user.

[2124] Voice dialogue

[2125] User: A user uses the system on a daily basis and inputs questions or inquiries by voice, such as "What's the weather like today?" or "What are the latest gardening trends?"

[2126] Terminal: Accepts voice input from the user and sends the recorded voice data to the server.

[2127] Server: Receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The converted text data is analyzed by a response generation method, and a response based on the user information is generated.

[2128] Server: Converts the generated text response into voice data using a voice conversion means and transmits it to the terminal.

[2129] Terminal: Plays back the audio data and conveys responses to the user.

[2130] Emotion recognition and response regulation

[2131] Server: The emotion recognition means analyzes the voice data and the user's facial expression data to recognize the user's emotional state. For example, emotions such as anger, sadness, and joy can be extracted from the voice tone and speaking style.

[2132] Server: Adjust the tone and content of the response based on the perceived emotion. If the user sounds sad, change the response to something warmer.

[2133] Server: Converts the adjusted response into audio data and sends it to the device.

[2134] Terminal: Plays back the audio data and conveys emotionally sensitive responses to the user.

[2135] Usage history and learning

[2136] Terminal: Records conversation history with the user and system usage history and sends them to the server.

[2137] Server: Based on the received usage history, the server uses learning methods to update the user's profile, thereby improving the accuracy and relevance of future responses.

[2138] Server: As part of the learning process, the server reflects usage history to improve the accuracy of emotion recognition tools.

[2139] Food delivery application example

[2140] When a user says, "It's sunny today and I'm in a good mood, so I want to eat something special," the device records the speech and sends it to the server. The server converts the speech to text and uses emotion recognition to identify the "good mood." A generative AI model (e.g., OpenAI GPT-4) suggests a special menu item based on past ordering history and the user's preferences. The server converts the suggestion into voice data and sends it to the device, which responds, "How about a special sushi set today?"

[2141] Prompt Sentence Examples

[2142] User: "It's sunny today and I'm feeling great, so I want to eat something special."

[2143] Prompt the generative AI model:

[2144] Suggest special menu items based on past order history and user profile. The user is currently in a good mood.

[2145] Example response: "How about a special sushi set today?"

[2146] In this way, the present invention provides personalized responses based on the user's details and emotional state, which is particularly useful in food delivery and everyday query resolution.

[2147] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2148] Step 1:

[2149] The user inputs voice data. The user gives a command by voice, such as "It's sunny today and I'm in a good mood, so I want to eat something special." The input data is voice data.

[2150] Step 2:

[2151] The terminal records the voice data and transmits the voice data to the server. Here, the input is the user's voice data and the output is the transmission of the voice data to the server.

[2152] Step 3:

[2153] The server receives the voice data and converts it into text data using a speech recognition method (e.g., Google Speech-to-Text API). The input is the transmitted voice data, and the output is text data.

[2154] Step 4:

[2155] The server analyzes the text data and uses emotion recognition to analyze the user's emotions. For example, it extracts the positive emotion of "feeling good." The input is the text data converted from speech, and the output is the emotional state ("feeling good").

[2156] Step 5:

[2157] The server uses a generative AI model (e.g., OpenAI GPT-4) to suggest a personalized menu based on the user's past ordering history and profile. Here, a prompt sentence is input to the generative AI model. The inputs are the user profile, emotional state, and text data. The output is a suggestion: "How about a special sushi set today?"

[2158] Step 6:

[2159] The server converts the generated text response into speech data. A speech conversion means is used to convert the text data into speech data. The input is the proposed text data and the output is the speech data.

[2160] Step 7:

[2161] The server sends the audio data to the terminal. The input is the converted audio data, and the output is the data sent to the terminal.

[2162] Step 8:

[2163] The terminal plays back the audio data and makes a suggestion to the user by voice. Specifically, it says to the user, "How about a special sushi set today?" The input is the audio data sent from the server, and the output is the audio playback.

[2164] Step 9:

[2165] The terminal records the user's usage history and sends that data to the server. The input is the user's usage history data, and the output is transmission to the server.

[2166] Step 10:

[2167] The server updates the user profile based on the received usage history to improve the accuracy and relevance of future responses. The input is the transmitted usage history data, and the output is the updated user profile.

[2168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2169] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2170] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2172] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2178] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2179] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2181] 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.

[2182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2183] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2184] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2189] The following is further disclosed regarding the above embodiment.

[2190] (Claim 1)

[2191] an input means for inputting user information as an initial setting;

[2192] A means for storing the input user information in a database;

[2193] a voice input means for accepting voice input from a user;

[2194] a speech recognition means for converting received speech input into text data;

[2195] a response generating means for analyzing the converted text data and generating a response based on the user information;

[2196] a voice conversion means for converting the generated response into voice data;

[2197] audio output means for reproducing audio data;

[2198] a learning means for recording a usage history and updating user information based on the recorded usage history;

[2199] A system including:

[2200] (Claim 2)

[2201] 10. The system of claim 1, further comprising means for customizing responses based on user preferences.

[2202] (Claim 3)

[2203] 10. The system of claim 1, further comprising means for generating a user profile and providing individually adapted responses based on the user profile.

[2204] "Example 1"

[2205] (Claim 1)

[2206] an input means for inputting user information as an initial setting;

[2207] A means for storing the input user information in a database;

[2208] a voice input means for accepting voice input from a user;

[2209] a speech recognition means for converting received speech input into text data;

[2210] a response generating means for analyzing the converted text data and generating a response based on the user information;

[2211] a voice conversion means for converting the generated response into voice data;

[2212] audio output means for reproducing audio data;

[2213] a learning means for recording a usage history and updating user information based on the recorded usage history;

[2214] communication means for transmitting packets of speech input from a user;

[2215] A means for generating a specific response using a generative AI model; and

[2216] A system including:

[2217] (Claim 2)

[2218] 10. The system of claim 1, further comprising means for customizing responses based on user preferences.

[2219] (Claim 3)

[2220] 10. The system of claim 1, further comprising means for generating a user profile and providing individually adapted responses based on the user profile.

[2221] "Application Example 1"

[2222] (Claim 1)

[2223] an input means for inputting user information as an initial setting;

[2224] A means for storing the input user information in a database;

[2225] a voice input means for accepting voice input from a user;

[2226] a speech recognition means for converting received speech input into text data;

[2227] a response generating means for analyzing the converted text data and generating a response based on the user information;

[2228] a voice conversion means for converting the generated response into voice data;

[2229] audio output means for reproducing audio data;

[2230] a learning means for recording a usage history and updating user information based on the recorded usage history;

[2231] a meal suggestion means for generating meal suggestions in consideration of the user's health condition;

[2232] A system including:

[2233] (Claim 2)

[2234] 10. The system of claim 1, further comprising means for customizing responses based on user preferences.

[2235] (Claim 3)

[2236] 10. The system of claim 1, further comprising means for generating a user profile and providing individually adapted responses based on the user profile.

[2237] "Example 2: Combining Emotion Engines"

[2238] (Claim 1)

[2239] an input means for inputting user information as an initial setting;

[2240] A means for storing the input user information in a database;

[2241] a voice input means for accepting voice input from a user;

[2242] a speech recognition means for converting received speech input into text data;

[2243] a response generating means for analyzing the converted text data and generating a response based on the user information;

[2244] a voice conversion means for converting the generated response into voice data;

[2245] audio output means for reproducing audio data;

[2246] a learning means for recording a usage history and updating user information based on the recorded usage history;

[2247] emotion recognition means for analyzing the user's voice tone and facial expression data and recognizing the user's emotion;

[2248] a response adjustment means for adjusting the tone and content of a response based on the perceived emotion;

[2249] A system including:

[2250] (Claim 2)

[2251] 10. The system of claim 1, further comprising means for customizing responses based on user preferences.

[2252] (Claim 3)

[2253] 10. The system of claim 1, further comprising means for generating a user profile and providing individually adapted responses based on the user profile.

[2254] "Application example 2 when combining emotion engines"

[2255] (Claim 1)

[2256] an input means for inputting user information as an initial setting;

[2257] A means for storing the input user information in a database;

[2258] a voice input means for accepting voice input from a user;

[2259] a speech recognition means for converting received speech input into text data;

[2260] a response generating means for analyzing the converted text data and generating a response based on the user information;

[2261] a voice conversion means for converting the generated response into voice data;

[2262] audio output means for reproducing audio data;

[2263] a learning means for recording a usage history and updating user information based on the recorded usage history;

[2264] emotion recognition means for analyzing the emotion of a user;

[2265] a means for adjusting a response based on emotion recognition;

[2266] A means of making personalized menu and service suggestions;

[2267] A system including:

[2268] (Claim 2)

[2269] 10. The system of claim 1, further comprising means for customizing responses based on the user's preferences and past ordering history.

[2270] (Claim 3)

[2271] 10. The system of claim 1, including means for using a generative AI model to provide individually adapted responses based on a user profile. [Explanation of symbols]

[2272] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting user information as an initial setting; A means for storing the input user information in a database; a voice input means for accepting voice input from a user; a speech recognition means for converting received speech input into text data; a response generating means for analyzing the converted text data and generating a response based on the user information; a voice conversion means for converting the generated response into voice data; audio output means for reproducing audio data; a learning means for recording a usage history and updating user information based on the recorded usage history; A system including:

2. 10. The system of claim 1, further comprising means for customizing responses based on user preferences.

3. 10. The system of claim 1, further comprising means for generating a user profile and providing individually adapted responses based on the user profile.

Images