system

Abstract

Provide a system. [Solution] means for capturing a user's voice through voice input; a means for converting the captured audio data into a string; a means for parsing the converted string and generating an appropriate response; means for audibly communicating the generated response to the user; A means for receiving health management information from a user, analyzing the information, and storing the information in a database; A means for providing health advice based on physical condition data; A means for suggesting medicines and providing information to doctors as needed based on the user's physical condition data; A system including:

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] Elderly people and users unfamiliar with technology often find it difficult to input text, and are at high risk of health problems due to insufficient daily health management. In addition, there are limited means to quickly and accurately prescribe appropriate medications. In particular, for elderly people living alone, not having someone to talk to increases feelings of loneliness and mental stress, and there are concerns that this may have a negative impact on maintaining their health. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for capturing a user's voice through voice input; means for converting the captured voice data into text; means for analyzing the converted text and generating an appropriate response; means for audibly communicating the generated response to the user; means for receiving health management information from the user, analyzing it, and storing it in a database; means for providing health advice based on the health data; and means for suggesting medication and providing information to a doctor as needed based on the user's health data. This system enables even elderly people to easily input information by voice, allowing them to receive support in various aspects of daily life and effectively managing their health.

[0006] "Voice input" is a technology for capturing and analyzing a user's voice as digital data.

[0007] "Voice data" refers to information that is a digital recording of a user's voice.

[0008] "Text conversion" is the process of analyzing audio data and converting it into corresponding text data.

[0009] "Response generation" is the process of determining an appropriate response or action based on a user's voice input or string of characters and providing it in text or voice.

[0010] "Physical condition management information" refers to data relating to the physical condition reported by the user and the results of analyzing that data.

[0011] A "database" is an information accumulation system that stores analyzed health management information and allows it to be accessed and searched as needed.

[0012] "Health advice" refers to advice or recommendations provided based on the user's physical condition data.

[0013] "Medication suggestion" is the process of recommending appropriate medication based on the user's physical condition data.

[0014] "Providing information to doctors" is the process of providing the user's physical condition data and analysis results to medical professionals. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0036] Specifically, the configuration and operation of a system for implementing the present invention will be described in detail below. The system mainly includes five main functions: voice input, dialogue function, health management, and medicine suggestion and prescription.

[0037] 1. Voice Input

[0038] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the received voice data and converts it into text using a speech recognition algorithm. The converted text is then sent back to the device, allowing the user to confirm that their voice input was handled correctly.

[0039] 2. Interactive features

[0040] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves the necessary data from an appropriate source (for example, a weather information API). Based on the retrieved information, the server generates a response to the user, sends it to the device, and responds to the user via voice.

[0041] 3. Health Management

[0042] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required and generates basic health advice as necessary. The advice is provided to the user via the device via voice.

[0043] 4. Health Advice

[0044] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as, "I recommend you exercise a little more."

[0045] 5. Medication suggestions and prescriptions

[0046] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. Based on this data, the server generates a preliminary medication suggestion and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[0047] As such, the present invention is a system that seamlessly executes a series of processes, starting with voice input, including dialogue, physical condition management, health advice, and medication suggestions and prescriptions. Users can access many functions using only their voice, increasing convenience and effectiveness, especially for elderly people and users who are unfamiliar with technology.

[0048] The processing flow will be explained below.

[0049] Processing steps for voice input function

[0050] Step 1:

[0051] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[0052] Step 2:

[0053] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[0054] Step 3:

[0055] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[0056] Step 4:

[0057] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[0058] Step 5:

[0059] The server returns the converted text data to the terminal.

[0060] Step 6:

[0061] The terminal displays the received text data to the user and asks for confirmation of the input contents.

[0062] Interactive behavior processing steps

[0063] Step 1:

[0064] The user uses voice input to ask a question or interact, for example, saying "What's the weather like today?"

[0065] Step 2:

[0066] The device captures the audio and converts it into a string, which is then sent to the server.

[0067] Step 3:

[0068] The server analyzes the received string data using a natural language processing engine to understand the intent of the question.

[0069] Step 4:

[0070] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[0071] Step 5:

[0072] The server generates a response based on the acquired information. The generated response is an appropriate response to the user's question.

[0073] Step 6:

[0074] The server sends a response to the terminal.

[0075] Step 7:

[0076] The terminal responds to the user by voice with the received response sentence.

[0077] Health management function processing steps

[0078] Step 1:

[0079] The user reports their physical condition by voice, for example, saying, "I have a slight headache this morning."

[0080] Step 2:

[0081] The device captures the audio and converts it into a string, which is then sent to the server.

[0082] Step 3:

[0083] The server analyzes the received string data and extracts important keywords (e.g., headache).

[0084] Step 4:

[0085] The server stores the extracted data in a health management database.

[0086] Step 5:

[0087] The server generates basic health advice based on the reported physical condition.

[0088] Step 6:

[0089] The server transmits the generated advice to the terminal.

[0090] Step 7:

[0091] The terminal conveys the received advice to the user by voice.

[0092] Medication suggestion and prescription function processing steps

[0093] Step 1:

[0094] The user then speaks a follow-up question about their condition, for example, "What should I do if my headache persists?"

[0095] Step 2:

[0096] The device captures the audio and converts it into a string, which is then sent to the server.

[0097] Step 3:

[0098] The server analyzes the received character string data and compares it with the user's physical condition data.

[0099] Step 4:

[0100] The server transmits the information through an online platform to provide the data to a physician as needed.

[0101] Step 5:

[0102] The doctor reviews the data and generates the necessary prescriptions.

[0103] Step 6:

[0104] The server receives the prescription generated by the doctor and communicates drug suggestions to the user.

[0105] Step 7:

[0106] The terminal will notify the user that the prescription is ready and advise on next steps.

[0107] Example 1

[0108] 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."

[0109] In modern society, it is difficult for elderly people and those unfamiliar with technology to efficiently obtain information and manage their health in daily life. While systems utilizing speech recognition and natural language processing exist, few systems seamlessly address a wide range of daily needs through these technologies. This creates a need for systems that allow users to easily and effectively gather information, manage their health, and receive medication recommendations through voice commands.

[0110] 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.

[0111] In this invention, the server includes means for capturing voice input at the terminal, compressing and encoding it, and transmitting it to the server, means for converting the voice data into text using a voice recognition algorithm at the server and returning the result to the terminal, and means for analyzing the intent of the user's question using a generative AI model and acquiring data from appropriate information sources. This enables users to efficiently acquire information and manage their health using voice, and to provide information to a doctor and receive medication recommendations as needed.

[0112] "Voice input" is a means by which a user provides information to a system using voice.

[0113] A "terminal" is a device that captures audio and transmits data to a server, such as a smartphone or smart speaker.

[0114] A "server" is a central computer system that analyzes voice data sent from a terminal and provides appropriate responses and services.

[0115] "Voice data" refers to data that is a digital recording of a user's voice input.

[0116] "Compression and encoding" refers to the process of reducing the size of captured audio data and converting it into a specific format for efficient transmission.

[0117] A "speech recognition algorithm" is a calculation method or program for converting voice data into a string of characters.

[0118] A "character string" is text data converted by a speech recognition algorithm.

[0119] A "natural language processing engine" is software that analyzes string data, understands its meaning, and generates appropriate responses.

[0120] A "generative AI model" is a type of artificial intelligence technology used to generate appropriate responses to user questions or requests.

[0121] "Physical condition management information" is data provided by a user regarding their own health condition.

[0122] A "database" is a system for storing and managing health management information and accumulated user data over a long period of time.

[0123] "Health advice" refers to suggestions and advice for improving and maintaining the user's health based on health management information.

[0124] "Medicine suggestions" are suggestions for recommending appropriate medicines based on the user's physical condition data.

[0125] "Information provision" is the process of transmitting the user's physical condition data and medication suggestions to doctors and other relevant parties.

[0126] "Data acquisition" is the process of obtaining the required information or data from an external source, such as an API.

[0127] A "trend or pattern" is a consistent change or specific behavior observed in a user's past physical condition data.

[0128] The present invention is a seamless system for efficiently meeting user needs through voice input. The system mainly includes five main functions: voice input, dialogue function, health management, health advice, and medicine suggestion and prescription.

[0129] 1. Voice Input

[0130] The device captures the user's voice using a built-in or external microphone, such as a smartphone or smart speaker. The captured voice data is compressed, encoded, and sent to a server as a data stream.

[0131] The server analyzes the received voice data and converts it into text using speech recognition algorithms such as Google® Cloud Speech-to-Text or Amazon Transcribe, and sends the results back to the device, allowing the user to confirm that their voice input was handled correctly.

[0132] Examples:

[0133] User: "What's the weather like today?"

[0134] Example prompt sentence:

[0135] "The user has spoken to ask about today's weather. The server should convert this into text using the speech recognition API, obtain the weather information, and respond."

[0136] 2. Interactive features

[0137] When a user asks a question or wants to have a conversation, they say, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server uses a generative AI model (e.g., GPT-3 (registered trademark)).5) to analyze the intent of the question and obtain the necessary data from appropriate sources. For example, based on information obtained from a weather information API, it generates an appropriate response and responds to the user via voice via the device.

[0138] Examples:

[0139] User: "What's the weather like today?"

[0140] Example prompt sentence:

[0141] "The user has asked about today's weather. Please provide an easy-to-understand answer based on the weather information you have obtained."

[0142] 3. Health Management

[0143] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required, and if necessary, generates basic health advice and provides it to the user via the device.

[0144] Examples:

[0145] User: "I have a bit of a headache this morning."

[0146] Example prompt sentence:

[0147] "A user reports how they felt this morning. Extract the information that they had a headache, store it in a database, and provide basic health advice based on that."

[0148] 4. Health Advice

[0149] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as "It is recommended that you exercise a little more."

[0150] Examples:

[0151] Server: "Looking at your recent health data, it seems like you're not getting enough exercise. I'd recommend you get a little more exercise."

[0152] Example prompt sentence:

[0153] "Based on the user's accumulated physical condition data, there is a tendency for lack of exercise. Based on that information, please provide appropriate health advice to the user."

[0154] 5. Medication suggestions and prescriptions

[0155] If a user asks, "I have a persistent headache. What should I do?", the device captures this speech, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. The server generates a preliminary medication suggestion based on this data and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[0156] Examples:

[0157] User: "I'm having a persistent headache. What should I do?"

[0158] Example prompt sentence:

[0159] "The user has reported a persistent headache and requested action. Please refer to their past physical condition data, generate a preliminary medication suggestion, and send it to the doctor for review. After the doctor's review, generate a formal prescription and notify the user."

[0160] In this way, the present invention is a system that seamlessly executes a series of processes, starting with the user's voice input, including dialogue functions, physical condition management, health advice, and drug suggestions and prescriptions. This allows users to use many functions using only their voice, providing convenience especially for elderly people and users who are unfamiliar with technology.

[0161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0162] Voice input

[0163] Step 1:

[0164] The user asks a question by voice, such as "What's the weather like today?" This voice becomes the input.

[0165] Step 2:

[0166] The device captures the user's voice using a built-in or external microphone, compresses and encodes the captured voice data as input, and outputs the encoded voice data.

[0167] Step 3:

[0168] The terminal transmits the encoded audio data to the server as a data stream, and the transmitted audio data becomes the input.

[0169] Step 4:

[0170] The server analyzes the received voice data using speech recognition algorithms such as Google Cloud Speech-to-Text and Amazon Transcribe. The voice data is converted into text and the text data is output.

[0171] Step 5:

[0172] The server returns the converted string to the terminal, and the returned string data becomes the input.

[0173] Step 6:

[0174] The device will then display the received text data to the user or provide audio feedback, allowing the user to confirm that their voice input was handled correctly.

[0175] Interactive features

[0176] Step 1:

[0177] The user asks aloud, "What's the weather like today?" This speech becomes the input.

[0178] Step 2:

[0179] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[0180] Step 3:

[0181] The server uses a generative AI model (e.g., GPT-3.5) to analyze the intent of the question. The string data is input, and the intent, such as "I want to know the weather information," is extracted.

[0182] Step 4:

[0183] The server retrieves the necessary data from a weather information API (e.g., OpenWeatherMap) based on the extracted intent. This data is input, and weather information is output.

[0184] Step 5:

[0185] The server generates an appropriate response message based on the weather information it has acquired. The response message is then output.

[0186] Step 6:

[0187] The server sends the generated response text to the terminal, which then becomes the input.

[0188] Step 7:

[0189] The terminal responds to the user by voice with a response message, providing the user with an easy-to-understand voice output.

[0190] Health management

[0191] Step 1:

[0192] The user reports by voice, "I have a slight headache this morning." This voice becomes the input.

[0193] Step 2:

[0194] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[0195] Step 3:

[0196] The server analyzes the speech recognition data and extracts important key information (e.g., "headache"). The input is text data, and the extracted key information is output.

[0197] Step 4:

[0198] The server stores the analysis results in a database, and the stored data becomes the input.

[0199] Step 5:

[0200] The server determines whether immediate action is required and generates basic health advice such as "drink lots of water and get plenty of rest." The health advice is output.

[0201] Step 6:

[0202] The server transmits the generated advice to the terminal, and the terminal provides the information to the user by voice. Voice advice is output.

[0203] Health Advice

[0204] Step 1:

[0205] The server analyzes the user's accumulated health data and extracts trends and patterns. The accumulated data is input, and trends and patterns are output.

[0206] Step 2:

[0207] Based on the analysis results, the server automatically generates individual health advice, such as "We recommend you exercise a little more." The health advice is output.

[0208] Step 3:

[0209] The server transmits the generated advice to the terminal, which then provides it to the user by voice. The voice advice is output.

[0210] Medication suggestions and prescriptions

[0211] Step 1:

[0212] The user asks a question by voice, "I have a persistent headache. What should I do?" This voice becomes the input.

[0213] Step 2:

[0214] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[0215] Step 3:

[0216] The server analyzes the user's physical condition data, referencing past data and current status as necessary. Physical condition data is input, and the analysis results are output.

[0217] Step 4:

[0218] The server generates preliminary medication suggestions based on the analysis results. The medication suggestions are output.

[0219] Step 5:

[0220] The server then sends the generated data to an online platform that provides preliminary drug recommendations to physicians.

[0221] Step 6:

[0222] After the doctor checks the data, a formal prescription is generated. The prescription is output.

[0223] Step 7:

[0224] The server sends the prescription to the terminal and notifies the user, who can then select the option to order and collect the medication online as needed.

[0225] (Application example 1)

[0226] 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."

[0227] Although there are systems that allow elderly people and those unfamiliar with technology to receive health management and health advice through voice input, these systems are limited to individual functions and lack multifaceted support. Furthermore, they lack functions for suggesting meals related to health and easy ordering, so comprehensive support is needed to help elderly people stay safe and healthy.

[0228] 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.

[0229] In this invention, the server includes means for capturing a user's voice through voice input, means for converting the captured voice data into a character string, means for analyzing the converted character string and generating an appropriate response, means for audibly communicating the generated response to the user, means for receiving health management information from the user, analyzing it, and storing it in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a doctor as needed based on the user's health data, and means for suggesting and ordering appropriate meals based on the user's health data and voice input. This enables a user to perform comprehensive health management using only voice input, and to suggest and order meals based on their physical condition.

[0230] "Voice input" is the means by which the user's voice is captured and transmitted to the system.

[0231] "Voice data" refers to data that stores captured user voice information in digital form.

[0232] The "means for converting into character string" is a process for analyzing the audio data and converting it into corresponding text data.

[0233] The "means for generating an appropriate response" is the process of creating a response to the user's question or request based on the parsed string.

[0234] "Physical condition management information" is information relating to the user's health condition and symptoms.

[0235] "Physical condition data" refers to measurement and report data relating to the user's health condition and physical condition.

[0236] "Health advice" is advice about health provided based on the user's physical condition data.

[0237] "Medicine suggestion" is the act of recommending appropriate medicine based on the user's physical condition data.

[0238] "Providing information to a doctor" refers to the act of transmitting the user's physical condition data to a doctor and providing information for appropriate treatment and prescription.

[0239] "Meal suggestion" is the act of recommending an appropriate meal menu based on the user's physical condition data and voice input.

[0240] "Meal Ordering" is the process of ordering suggested meals online.

[0241] A "natural language processing engine" is software for understanding and analyzing natural language.

[0242] The configuration and operation of a system for implementing the present invention will be described in detail below.

[0243] 1. System Configuration

[0244] The system mainly consists of the following components:

[0245] Voice input means: Devices with built-in microphones, such as smartphones, smart glasses, and head-mounted displays.

[0246] String conversion method: Software that converts voice data into strings using the Google Cloud Speech-to-Text API.

[0247] Natural language processing means: OpenAI's (registered trademark) GPT-4 (registered trademark) model is used to analyze the converted strings and understand the user's intent.

[0248] Database: A database that uses Firebase to store and manage user physical condition data and voice data.

[0249] Food Delivery API: An API for suggesting and ordering meals using the Uber Eats API.

[0250] TTS Engine: A Text-to-Speech engine is used to generate responses that are then spoken to the user.

[0251] 2. Program Processing Overview

[0252] Voice Input and Recognition

[0253] When a user speaks a question or request, the device's built-in microphone captures the audio data, which is then sent to a server and converted into text using the Google Cloud Speech-to-Text API.

[0254] Natural Language Processing

[0255] The server receives the converted voice data and uses the OpenAI GPT-4 model to analyze the intent of the user's question, such as "What is your recommended meal today?", and generates appropriate meal suggestions.

[0256] Database Reference

[0257] The server accesses the Firebase database and references the user's physical condition data and allergy information, which allows it to provide health advice and dietary suggestions tailored to each individual user.

[0258] Food delivery API integration

[0259] Based on the analysis results and the user's physical condition data, the app will use the Uber Eats API to suggest appropriate meal options. For example, it will suggest specific menu items and provide a function to allow the user to proceed directly to the ordering process.

[0260] Response generation and communication

[0261] The server converts the generated responses and suggestions into speech using a TTS engine and transmits them to the user via the terminal.

[0262] 3. Examples of concrete examples and prompts

[0263] User: "What's your lunch recommendation today?"

[0264] Server side:

[0265] The app checks the user's physical condition data and suggests a "nutritious chicken salad" based on their recent lack of exercise.

[0266] Generates a voice response saying, "Uber Eats has a fresh chicken salad on the menu. Would you like to order it?"

[0267] Example prompt sentence:

[0268] User: "What's your recommendation today?"

[0269] Input to the AI ​​model:

[0270] Consider the user's profile and health data to suggest today's meal recommendations, including taking into account the user's allergies and dietary restrictions.

[0271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0272] Step 1:

[0273] Voice input

[0274] Input: The user speaks a question or request (e.g., "What's the recommendation today?").

[0275] Processing: The device's microphone captures the user's voice and obtains the voice data.

[0276] Output: The acquired audio data is sent to the server as is.

[0277] Step 2:

[0278] Voice Recognition

[0279] Input: Captured audio data.

[0280] Processing: The server uses the Google Cloud Speech-to-Text API to convert the audio data into text. Specifically, a speech recognition engine analyzes the audio signal and generates corresponding text.

[0281] Output: Text data (e.g., "What's your recommendation today?").

[0282] Step 3:

[0283] Natural Language Processing

[0284] Input: Text data.

[0285] Processing: The server passes the text data to OpenAI's GPT-4 model, which analyzes the intent of the user's question or request. Specifically, the natural language processing engine understands the meaning of the sentence to generate appropriate meal suggestions and responses based on the input text.

[0286] Output: Analysis results (user intent and specific requirements).

[0287] Step 4:

[0288] Database Reference

[0289] Input: Analysis results and user ID.

[0290] Processing: The server accesses the Firebase database to retrieve the user's physical condition data and allergy information. Specifically, it queries the database using the user ID as a key to retrieve related health information.

[0291] Output: User's health data and allergy information.

[0292] Step 5:

[0293] Meal suggestions

[0294] Input: Analysis results, physical condition data, and allergy information.

[0295] Processing: The server uses the Uber Eats API to suggest appropriate meal options based on the analysis results and the user's health data. Specifically, it filters the meal options and selects the most suitable menu for the user.

[0296] Output: A suggested meal (e.g., "chicken salad").

[0297] Step 6:

[0298] Response Generation

[0299] Input: A suggested meal menu.

[0300] Processing: The server generates a response text (e.g., "Uber Eats has fresh chicken salad on the menu. Would you like to order it?"). Specifically, it uses a natural language generation engine to generate a message to be conveyed to the user.

[0301] Output: The response text.

[0302] Step 7:

[0303] Voice response

[0304] Input: Response text.

[0305] Processing: The server uses a text-to-speech engine to convert the response text into voice data, specifically outputting the generated text as a voice signal and sending it to the terminal.

[0306] Output: Audio data.

[0307] Step 8:

[0308] Communication to users

[0309] Input: Audio data.

[0310] Processing: The device plays the audio data and gives the response to the user by voice. Specifically, the device's speaker outputs the audio data so that the user can hear it.

[0311] Output: A spoken response to the user.

[0312] 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.

[0313] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions, as well as an emotion recognition engine that recognizes the user's emotions. The detailed configuration and operation of this system are described below.

[0314] 1. Voice Input and Emotion Recognition

[0315] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the voice data and converts it into text using a voice recognition algorithm, while simultaneously analyzing the user's emotions using an emotion recognition engine. This emotion recognition allows the system to distinguish emotions extracted from the user's voice (e.g., joy, sadness, anger, etc.) and respond accordingly.

[0316] 2. Interactivity and emotional response

[0317] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves data from the necessary sources (e.g., weather information API). Based on the retrieved information, the server generates a response, taking into account the results of the emotion recognition engine. For example, if the user asks a question in a sad voice, the server can add a touch of kindness or encouragement to the response. The generated response is sent to the device and replies to the user via voice.

[0318] 3. Physical and emotional health management

[0319] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and then sends it to the server. The server analyzes the report, extracts important keywords, and stores them in a database along with emotion recognition results. The server takes the user's emotional state into account when generating advice based on the analysis results. For example, if the user is feeling stressed, it can add advice on how to relax. The advice is provided to the user via voice via the device.

[0320] 4. Health advice and emotional support

[0321] The server analyzes the user's accumulated physical and emotional data to extract trends and patterns. Based on this, it automatically generates advice and provides it via voice through the device. For example, if a user has been feeling down recently, it can provide positive advice such as "Try taking a walk or doing some light exercise."

[0322] 5. Medication suggestions and prescriptions

[0323] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition and emotional data and sends the information to a doctor via an online platform. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. At the same time, appropriate support is provided based on the emotion recognition results (e.g., a reassuring message).

[0324] In this way, the present invention is a system that provides more user-friendly and effective support by combining an emotion recognition engine with a series of processes that start with voice input and include dialogue functions, physical condition management, health advice, and medication suggestions and prescriptions.

[0325] The processing flow will be explained below.

[0326] Processing steps for voice input and emotion recognition functions

[0327] Step 1:

[0328] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[0329] Step 2:

[0330] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[0331] Step 3:

[0332] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[0333] Step 4:

[0334] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[0335] Step 5:

[0336] In parallel with the voice recognition, the server uses an emotion recognition engine to determine the user's emotions from the voice data. For example, it analyzes the intensity and rhythm of the sound to estimate emotions such as joy, sadness, and anger.

[0337] Step 6:

[0338] The server returns the converted text data and the determined emotion data to the terminal.

[0339] Step 7:

[0340] The terminal displays the received text data and emotion data to the user and asks for confirmation of the input contents.

[0341] Interactivity and emotional response processing steps

[0342] Step 1:

[0343] The user uses voice input to ask a question, saying "What's the weather like today?"

[0344] Step 2:

[0345] The device captures the audio and sends the data to a server, just like the voice input and emotion recognition functions mentioned earlier.

[0346] Step 3:

[0347] The server analyzes the received text data using a natural language processing engine to understand the intent of the question.

[0348] Step 4:

[0349] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[0350] Step 5:

[0351] The server generates a response based on the acquired information, taking into account the results of the emotion recognition engine, for example, generating a gentle tone of response if the user is depressed.

[0352] Step 6:

[0353] The server transmits the generated response sentence and tone information based on the emotion to the terminal.

[0354] Step 7:

[0355] The terminal responds to the user with the received response sentence in a voice tone that corresponds to the emotion.

[0356] Physical condition management and emotional consideration processing steps

[0357] Step 1:

[0358] The user reports their physical condition by voice, saying, "I have a slight headache this morning."

[0359] Step 2:

[0360] The terminal captures the voice, converts it into a string of characters, and then transmits the voice data and emotion data to the server.

[0361] Step 3:

[0362] The server analyzes the received health report, extracts important keywords, and determines the appropriate response based on the emotion recognition results.

[0363] Step 4:

[0364] The server stores the analysis results and emotion recognition results in a health management database.

[0365] Step 5:

[0366] The server generates advice based on the reported physical condition and emotions. For example, if the user shows signs of anxiety, it adds advice such as "relax and take a deep breath."

[0367] Step 6:

[0368] The server transmits the generated advice to the terminal.

[0369] Step 7:

[0370] The terminal conveys the received advice to the user by voice.

[0371] Medication suggestion and prescription function processing steps

[0372] Step 1:

[0373] The user then voice-inputs a follow-up question about their condition, saying, "I'm having a persistent headache. What should I do?"

[0374] Step 2:

[0375] The device captures the audio, converts it into a string of characters, and sends it to the server.

[0376] Step 3:

[0377] The server analyzes the received character string data and emotion data and compares them with the user's physical condition data.

[0378] Step 4:

[0379] The server transmits the information through an online platform to provide the data to a physician as needed.

[0380] Step 5:

[0381] The doctor reviews the data and generates the necessary prescriptions.

[0382] Step 6:

[0383] The server receives the prescription generated by the doctor and provides the user with medication suggestions, taking into account their emotions. For example, if the user shows anxiety, the server adds a message saying, "Don't worry, we'll prepare your medication for you."

[0384] Step 7:

[0385] The terminal will notify the user that the prescription is ready and advise them on next steps (such as traveling to a pharmacy or online collection).

[0386] Example 2

[0387] 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."

[0388] Conventional voice input systems can convert the user's speech into text and provide information, but they have difficulty recognizing the user's emotional state and responding appropriately. In particular, there are limitations to providing appropriate advice and medication suggestions to users who are in poor health or experiencing psychological stress, and more user-friendly and effective support is needed.

[0389] 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.

[0390] In this invention, the server includes means for converting voice data into character strings, means for analyzing the converted character strings and voice data to recognize the user's emotions, and means for generating an appropriate response based on the emotion recognition result, thereby making it possible to provide responses and advice that take the user's emotions into consideration.

[0391] "Voice input" is a means of capturing the user's voice through a microphone.

[0392] "Captured audio data" refers to audio captured by a microphone and converted into digital form.

[0393] "Means for converting into character string" refers to a technology that analyzes audio data and converts it into text format.

[0394] The "analyzing means" is a process for processing the converted text and audio data to identify its content and sentiment.

[0395] "Means for recognizing emotions" refers to technology for determining a user's emotional state from voice or text.

[0396] A "means for generating a response" is an algorithm or engine for generating an appropriate response based on the analysis results.

[0397] "Physical condition management information" is information relating to the physical condition and mood reported by the user.

[0398] The "means of storing in a database" is a system that organizes and safely stores collected health management information and emotional data.

[0399] The "means for providing health advice" is a technology that generates and provides appropriate health-related advice based on the user's physical condition and emotional data.

[0400] "Professional information provision" is the process of using collected data to provide appropriate information to physicians and other professionals.

[0401] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions with an emotion recognition engine that recognizes the user's emotions. This system is implemented using a specific hardware and software configuration. The detailed configuration and operation of the system are described below.

[0402] Voice Input and Emotion Recognition

[0403] The device captures the user's voice using a built-in or external microphone. The captured voice data is converted into a digital format, compressed, and encoded. This data is then sent as a data stream to the server. The server uses a speech recognition algorithm (e.g., Google Speech-to-Text API) to analyze the voice data and convert it into a string of characters. It also uses an emotion recognition engine (e.g., IBM Watson® Tone Analyzer) to analyze and identify the user's emotional state. For example, if the user says "I have a headache," the system will recognize the emotion of "sadness" from the voice.

[0404] Interactivity and emotional response

[0405] When a user says, "What's the weather like today?", the device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine (e.g., OpenAI's GPT-3) to analyze the intent of the question. It then retrieves the necessary information from a weather information API database (e.g., OpenWeatherMap) and generates a response based on that information. It also takes into account the results of the emotion recognition engine, and if the user asks sadly, it creates a gentle response such as, "It's sunny today. I hope you have a nice day." The generated response is sent to the device, where it is replied to the user audibly using speech synthesis technology.

[0406] Physical condition management and emotional consideration

[0407] When a user reports their physical condition by saying, "I have a slight headache this morning," the device captures this voice, converts it into text, and sends it to the server. The server analyzes the report and extracts keywords such as "headache." It also stores the emotion recognition results in a database. Next, it creates advice that takes into account the user's stress level and emotional state. For example, it might generate advice such as, "I have a headache, so try drinking some tea and relaxing." This advice is provided to the user via the device.

[0408] Health advice and emotional support

[0409] The server analyzes the user's accumulated physical and emotional data to identify specific trends and patterns. Based on this data, it automatically generates health advice and provides it to the user via voice via the device. For example, if it is known that the user has recently been prone to stress, the server may provide advice such as "Try going for a walk to relax."

[0410] Medication suggestions and prescriptions

[0411] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to a server. The server analyzes the user's physical condition and emotional data and sends it to a specialist via an online platform (e.g., a medical information system). The specialist reviews this data and generates an official prescription. This information is returned to the server and notified to the user via the device. At the same time, a message to ease the user's anxiety (e.g., "Take some medicine and you'll feel better") is also provided.

[0412] Through these processes, the system provides comprehensive and emotionally sensitive support to the user.Examples of prompts for the generative AI model include, "I've been having a bad headache lately. What should I do?" and "What's the weather like today?"

[0413] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0414] Step 1:

[0415] The user makes a verbal request. For example, "What's the weather like today?" The input is the user's voice. The device's microphone captures this voice.

[0416] Step 2:

[0417] The captured audio data is converted to a digital format on the device, which then compresses and encodes the converted audio data and sends it as a data stream to the server. The input is digital audio data, and the output is compressed and encoded audio data.

[0418] Step 3:

[0419] The server analyzes the received audio data and converts it into a text string using a speech recognition algorithm, using the Google Speech-to-Text API. The input is compressed and encoded audio data, and the output is a text string.

[0420] Step 4:

[0421] The server analyzes the converted text string and voice data with an emotion recognition engine to identify the user's emotional state. For example, it uses IBM Watson Tone Analyzer. The input is the text string and voice data, and the output is the emotion analysis result (e.g., the user has the emotion "sad").

[0422] Step 5:

[0423] Based on the emotional state and the text string, the server uses a natural language processing engine to generate a response. OpenAI's GPT-3 can be used here. The input is the emotion analysis result and the text string, and the output is the generated response. For example, a response such as "It's sunny today. Cheer up!" is generated.

[0424] Step 6:

[0425] The generated response is sent from the server to the device, which then uses speech synthesis technology to convert the text response into speech. The input is a text response, and the output is a speech response.

[0426] Step 7:

[0427] The device then transmits the synthesized response to the user through the speaker. The final output is a voice response that the user hears. For example, "It's a sunny day today. Cheer up." The dialogue is completed.

[0428] Step 8:

[0429] When a user reports their physical condition (e.g., "I have a slight headache this morning"), their voice is captured and the data is sent to the server. This data is analyzed, and the physical condition data and emotion data are stored in a database. The input is the voice data about their physical condition, and the output is the analyzed physical condition and emotion data.

[0430] Step 9:

[0431] The server generates health advice based on the accumulated physical condition data and emotional data, and provides it to the user via audio via the terminal. The input is the accumulated data, and the output is audio advice. For example, advice such as "Drink tea to relax" is provided.

[0432] Step 10:

[0433] When a user requests a medication suggestion (e.g., "I have a persistent headache. What should I do?"), the device captures the voice and sends it to the server. The server analyzes the user's physical condition and emotional data and provides the information to the doctor through the online platform. Finally, the doctor's prescription information is returned to the device via the server and notified to the user. The input is the user's question and physical condition data, and the output is the prescription information and a message to soothe the emotions.

[0434] (Application example 2)

[0435] 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."

[0436] Conventional food delivery applications do not suggest meals that take into account the user's physical condition or emotional state, making it difficult to provide services that are appropriate for each user's individual health and psychological state. Furthermore, there is a lack of systems that combine voice input and emotion recognition, and no method is provided for users to easily report their physical and emotional state and receive appropriate support based on that information. This has led to the issue of low user satisfaction.

[0437] The specific processing by the specific 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 means for capturing the user's voice through voice input, means for converting the captured voice data into character strings, means for analyzing the converted character strings and generating an appropriate response, means for audibly conveying the generated response to the user, means for receiving, analyzing, and storing health management information from the user in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a specialist as needed based on the user's health data, means for analyzing the user's emotional state using an emotion recognition engine, means for adjusting responses and advice based on the analyzed emotional state, and means for making individual dietary suggestions based on the user's physical condition and emotional state. This enables personalized dietary suggestions and responses based on the user's physical condition and emotional state.

[0438] "Voice input" is the ability to capture a user's voice and obtain it as digital data for processing.

[0439] "Captured audio data" refers to audio information obtained through audio input and stored and processed as digital data.

[0440] "Means for converting into text" refers to the process or technology that analyzes audio data and converts it into corresponding text.

[0441] The "converted character string" refers to data resulting from converting voice data into character information.

[0442] The "means for generating an appropriate response" is a mechanism for generating an appropriate reply or information for the user based on the parsed string.

[0443] "Physical condition management information" is information about the health condition provided by the user, and is used as the basis for analyzing and managing the physical condition.

[0444] "Physical condition data" refers to data relating to the user's current or past health condition.

[0445] "Means for providing health advice" refers to a function that analyzes the user's physical condition data and provides health advice and suggestions based on the results.

[0446] "Medication suggestion" is the process of recommending appropriate medications based on the user's health condition.

[0447] The "means for providing information to experts" is a mechanism for collecting information about the user's health condition and providing it to experts such as doctors and pharmacists.

[0448] An "emotion recognition engine" is an algorithm or technology that analyzes a user's voice data and identifies their emotional state.

[0449] "Emotional state" refers to the emotional state that the user is currently feeling, and includes emotions such as joy, sadness, and anger.

[0450] A "means for tailoring responses or advice" is a technique or process for tailoring the content of generated responses or health advice based on the user's emotional state.

[0451] The "means for making meal suggestions" is a function that suggests an appropriate meal menu according to the user's physical condition and emotional state.

[0452] This invention is a system that combines voice input, emotion recognition, physical condition management, health advice, and meal suggestions. The configuration and operation of this system will be described below.

[0453] System configuration

[0454] This system mainly consists of the following hardware and software:

[0455] Hardware: Smartphone (with built-in microphone and speaker)

[0456] Software: Speech recognition engine, emotion recognition engine, recommender system, natural language processing engine, cloud database

[0457] System Operation

[0458] 1. Audio capture and transmission:

[0459] The device (smartphone) captures the user's voice input. The captured voice data is temporarily stored on the device. This voice data is compressed and sent to a cloud server as digital data.

[0460] 2. Speech and Emotion Recognition:

[0461] The server converts the received voice data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). At the same time, it analyzes the user's emotional state using an emotion recognition engine (e.g., Azure® Cognitive Services Emotion API). The analyzed emotional state is output as a classification result, such as joy, sadness, or anger.

[0462] 3. Interaction processing and response generation:

[0463] The server stores the text data and emotion recognition results in a cloud database. It uses a natural language processing engine (e.g., GPT-4) to understand the user's intent from the analyzed text and generate an appropriate response. The response takes into account the emotion recognition results and adds elements of kindness and encouragement to the response. The generated response is returned to the device as audio and provided to the user.

[0464] 4. Physical fitness and health advice:

[0465] The server receives voice input from the user reporting their physical condition (e.g., "I have a slight headache today"). The server stores the physical condition data in a database and analyzes it to generate health advice. If the user is feeling stressed, it can also add advice on how to relax. Health advice based on the analysis results is provided to the user via the terminal as voice.

[0466] 5. Meal Suggestions:

[0467] It makes personalized meal suggestions based on the user's physical and emotional state. It uses a recommender system to select the meal menu that is best suited to the user's health condition. For example, if a user inputs, "I've been feeling stressed and tired lately," it will suggest meals that will help relieve fatigue. These suggestions are provided to the user via voice and text.

[0468] Specific examples

[0469] For example, if a user says, "I've been feeling stressed and tired lately," the process is as follows:

[0470] Example prompt sentence:

[0471] A user said, "I've been feeling stressed and tired lately." Please create an encouraging message and provide optimal meal suggestions for this user.

[0472] In response to this prompt, the system produces the following output:

[0473] Suggested menu: "Grilled Salmon Set Meal - A nutritionally balanced meal that will help you recover from fatigue."

[0474] Special message: "You seem a little tired today. Eat a balanced meal and relax. Take care!"

[0475] In this way, personalized meal suggestions and responses based on the user's physical condition and emotions are provided, which is expected to improve user satisfaction.

[0476] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0477] Step 1:

[0478] The device accepts voice input. The user speaks about their physical condition and emotions, and the device's microphone captures the voice. The captured voice data is saved as digital data. The input in this step is the user's voice, and the output is digital voice data.

[0479] Step 2:

[0480] The device sends the captured audio data to the cloud server, which then compresses the audio data and transfers it to the cloud server using data streaming technology. The input of this step is digital audio data, and the output is the audio data sent to the cloud server.

[0481] Step 3:

[0482] The server converts the received voice data into text using a speech recognition engine. The server then converts the voice data into text using a speech recognition engine such as Google Cloud Speech-to-Text. The input for this step is the voice data, and the output is the converted text.

[0483] Step 4:

[0484] The server inputs the converted string into an emotion recognition engine to analyze the user's emotional state. It uses the Azure Cognitive Services Emotion API to identify the emotional state (e.g., joy, sadness, anger, etc.). The input of this step is the converted string, and the output is the emotion recognition result.

[0485] Step 5:

[0486] The server saves the string data and emotion recognition results in a cloud database. It uses a database such as MySQL (registered trademark) or MongoDB to save the analysis results. The input of this step is the string data and emotion recognition results, and the output is the analysis results saved in the database.

[0487] Step 6:

[0488] The server uses a natural language processing engine to analyze the user's intent and generate an appropriate response. A generative AI model such as GPT-4 is used to understand the user's intent from the input string and generate a response. The input for this step is string data, and the output is the generated response.

[0489] Step 7:

[0490] The server adjusts the response sentence taking into account the emotion recognition results. For example, if the user shows sadness, it adds elements of kindness or encouragement. The input of this step is the generated response sentence and the emotion recognition results, and the output is the adjusted response sentence.

[0491] Step 8:

[0492] The server converts the adjusted response sentence into speech and sends it to the terminal. A speech synthesis engine is used to convert the text into speech data and send that data to the terminal. The input of this step is the adjusted response sentence, and the output is the speech data sent to the terminal.

[0493] Step 9:

[0494] The terminal plays the received voice data and conveys the response to the user. The voice data is played using the terminal's speaker. The input of this step is the voice data, and the output is the voice response conveyed to the user.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] [Second embodiment]

[0499] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0500] 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.

[0501] 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).

[0502] 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.

[0503] 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.

[0504] 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).

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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.

[0509] 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.

[0510] 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."

[0511] Specifically, the configuration and operation of a system for implementing the present invention will be described in detail below. The system mainly includes five main functions: voice input, dialogue function, health management, and medicine suggestion and prescription.

[0512] 1. Voice Input

[0513] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the received voice data and converts it into text using a speech recognition algorithm. The converted text is then sent back to the device, allowing the user to confirm that their voice input was handled correctly.

[0514] 2. Interactive features

[0515] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves the necessary data from an appropriate source (for example, a weather information API). Based on the retrieved information, the server generates a response to the user, sends it to the device, and responds to the user via voice.

[0516] 3. Health Management

[0517] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required and generates basic health advice as necessary. The advice is provided to the user via the device via voice.

[0518] 4. Health Advice

[0519] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as, "I recommend you exercise a little more."

[0520] 5. Medication suggestions and prescriptions

[0521] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. Based on this data, the server generates a preliminary medication suggestion and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[0522] As such, the present invention is a system that seamlessly executes a series of processes, starting with voice input, including dialogue, physical condition management, health advice, and medication suggestions and prescriptions. Users can access many functions using only their voice, increasing convenience and effectiveness, especially for elderly people and users who are unfamiliar with technology.

[0523] The processing flow will be explained below.

[0524] Processing steps for voice input function

[0525] Step 1:

[0526] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[0527] Step 2:

[0528] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[0529] Step 3:

[0530] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[0531] Step 4:

[0532] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[0533] Step 5:

[0534] The server returns the converted text data to the terminal.

[0535] Step 6:

[0536] The terminal displays the received text data to the user and asks for confirmation of the input contents.

[0537] Interactive behavior processing steps

[0538] Step 1:

[0539] The user uses voice input to ask a question or interact, for example, saying "What's the weather like today?"

[0540] Step 2:

[0541] The device captures the audio and converts it into a string, which is then sent to the server.

[0542] Step 3:

[0543] The server analyzes the received string data using a natural language processing engine to understand the intent of the question.

[0544] Step 4:

[0545] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[0546] Step 5:

[0547] The server generates a response based on the acquired information. The generated response is an appropriate response to the user's question.

[0548] Step 6:

[0549] The server sends a response to the terminal.

[0550] Step 7:

[0551] The terminal responds to the user by voice with the received response sentence.

[0552] Health management function processing steps

[0553] Step 1:

[0554] The user reports their physical condition by voice, for example, saying, "I have a slight headache this morning."

[0555] Step 2:

[0556] The device captures the audio and converts it into a string, which is then sent to the server.

[0557] Step 3:

[0558] The server analyzes the received string data and extracts important keywords (e.g., headache).

[0559] Step 4:

[0560] The server stores the extracted data in a health management database.

[0561] Step 5:

[0562] The server generates basic health advice based on the reported physical condition.

[0563] Step 6:

[0564] The server transmits the generated advice to the terminal.

[0565] Step 7:

[0566] The terminal conveys the received advice to the user by voice.

[0567] Medication suggestion and prescription function processing steps

[0568] Step 1:

[0569] The user then speaks a follow-up question about their condition, for example, "What should I do if my headache persists?"

[0570] Step 2:

[0571] The device captures the audio and converts it into a string, which is then sent to the server.

[0572] Step 3:

[0573] The server analyzes the received character string data and compares it with the user's physical condition data.

[0574] Step 4:

[0575] The server transmits the information through an online platform to provide the data to a physician as needed.

[0576] Step 5:

[0577] The doctor reviews the data and generates the necessary prescriptions.

[0578] Step 6:

[0579] The server receives the prescription generated by the doctor and communicates drug suggestions to the user.

[0580] Step 7:

[0581] The terminal will notify the user that the prescription is ready and advise on next steps.

[0582] Example 1

[0583] 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."

[0584] In modern society, it is difficult for elderly people and those unfamiliar with technology to efficiently obtain information and manage their health in daily life. While systems utilizing speech recognition and natural language processing exist, few systems seamlessly address a wide range of daily needs through these technologies. This creates a need for systems that allow users to easily and effectively gather information, manage their health, and receive medication recommendations through voice commands.

[0585] 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.

[0586] In this invention, the server includes means for capturing voice input at the terminal, compressing and encoding it, and transmitting it to the server, means for converting the voice data into text using a voice recognition algorithm at the server and returning the result to the terminal, and means for analyzing the intent of the user's question using a generative AI model and acquiring data from appropriate information sources. This enables users to efficiently acquire information and manage their health using voice, and to provide information to a doctor and receive medication recommendations as needed.

[0587] "Voice input" is a means by which a user provides information to a system using voice.

[0588] A "terminal" is a device that captures audio and transmits data to a server, such as a smartphone or smart speaker.

[0589] A "server" is a central computer system that analyzes voice data sent from a terminal and provides appropriate responses and services.

[0590] "Voice data" refers to data that is a digital recording of a user's voice input.

[0591] "Compression and encoding" refers to the process of reducing the size of captured audio data and converting it into a specific format for efficient transmission.

[0592] A "speech recognition algorithm" is a calculation method or program for converting voice data into a string of characters.

[0593] A "character string" is text data converted by a speech recognition algorithm.

[0594] A "natural language processing engine" is software that analyzes string data, understands its meaning, and generates appropriate responses.

[0595] A "generative AI model" is a type of artificial intelligence technology used to generate appropriate responses to user questions or requests.

[0596] "Physical condition management information" is data provided by a user regarding their own health condition.

[0597] A "database" is a system for storing and managing health management information and accumulated user data over a long period of time.

[0598] "Health advice" refers to suggestions and advice for improving and maintaining the user's health based on health management information.

[0599] "Medicine suggestions" are suggestions for recommending appropriate medicines based on the user's physical condition data.

[0600] "Information provision" is the process of transmitting the user's physical condition data and medication suggestions to doctors and other relevant parties.

[0601] "Data acquisition" is the process of obtaining the required information or data from an external source, such as an API.

[0602] A "trend or pattern" is a consistent change or specific behavior observed in a user's past physical condition data.

[0603] The present invention is a seamless system for efficiently meeting user needs through voice input. The system mainly includes five main functions: voice input, dialogue function, health management, health advice, and medicine suggestion and prescription.

[0604] 1. Voice Input

[0605] The device captures the user's voice using a built-in or external microphone, such as a smartphone or smart speaker. The captured voice data is compressed, encoded, and sent to a server as a data stream.

[0606] The server analyzes the received voice data and converts it into text using speech recognition algorithms such as Google Cloud Speech-to-Text or Amazon Transcribe, then sends the results back to the device, allowing the user to confirm that their voice input was handled correctly.

[0607] Examples:

[0608] User: "What's the weather like today?"

[0609] Example prompt sentence:

[0610] "The user has spoken to ask about today's weather. The server should convert this into text using the speech recognition API, obtain the weather information, and respond."

[0611] 2. Interactive features

[0612] When a user asks a question or wants to have a conversation, they say, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server uses a generative AI model (e.g., GPT-3.5) to analyze the intent of the question and retrieves the necessary data from appropriate sources. For example, based on information obtained from a weather information API, it generates an appropriate response and replies to the user via voice via the device.

[0613] Examples:

[0614] User: "What's the weather like today?"

[0615] Example prompt sentence:

[0616] "The user has asked about today's weather. Please provide an easy-to-understand answer based on the weather information you have obtained."

[0617] 3. Health Management

[0618] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required, and if necessary, generates basic health advice and provides it to the user via the device.

[0619] Examples:

[0620] User: "I have a bit of a headache this morning."

[0621] Example prompt sentence:

[0622] "A user reports how they felt this morning. Extract the information that they had a headache, store it in a database, and provide basic health advice based on that."

[0623] 4. Health Advice

[0624] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as "It is recommended that you exercise a little more."

[0625] Examples:

[0626] Server: "Looking at your recent health data, it seems like you're not getting enough exercise. I'd recommend you get a little more exercise."

[0627] Example prompt sentence:

[0628] "Based on the user's accumulated physical condition data, there is a tendency for lack of exercise. Based on that information, please provide appropriate health advice to the user."

[0629] 5. Medication suggestions and prescriptions

[0630] If a user asks, "I have a persistent headache. What should I do?", the device captures this speech, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. The server generates a preliminary medication suggestion based on this data and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[0631] Examples:

[0632] User: "I'm having a persistent headache. What should I do?"

[0633] Example prompt sentence:

[0634] "The user has reported a persistent headache and requested action. Please refer to their past physical condition data, generate a preliminary medication suggestion, and send it to the doctor for review. After the doctor's review, generate a formal prescription and notify the user."

[0635] In this way, the present invention is a system that seamlessly executes a series of processes, starting with the user's voice input, including dialogue functions, physical condition management, health advice, and drug suggestions and prescriptions. This allows users to use many functions using only their voice, providing convenience especially for elderly people and users who are unfamiliar with technology.

[0636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0637] Voice input

[0638] Step 1:

[0639] The user asks a question by voice, such as "What's the weather like today?" This voice becomes the input.

[0640] Step 2:

[0641] The device captures the user's voice using a built-in or external microphone, compresses and encodes the captured voice data as input, and outputs the encoded voice data.

[0642] Step 3:

[0643] The terminal transmits the encoded audio data to the server as a data stream, and the transmitted audio data becomes the input.

[0644] Step 4:

[0645] The server analyzes the received voice data using speech recognition algorithms such as Google Cloud Speech-to-Text and Amazon Transcribe. The voice data is converted into text and the text data is output.

[0646] Step 5:

[0647] The server returns the converted string to the terminal, and the returned string data becomes the input.

[0648] Step 6:

[0649] The device will then display the received text data to the user or provide audio feedback, allowing the user to confirm that their voice input was handled correctly.

[0650] Interactive features

[0651] Step 1:

[0652] The user asks aloud, "What's the weather like today?" This speech becomes the input.

[0653] Step 2:

[0654] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[0655] Step 3:

[0656] The server uses a generative AI model (e.g., GPT-3.5) to analyze the intent of the question. The string data is input, and the intent, such as "I want to know the weather information," is extracted.

[0657] Step 4:

[0658] The server retrieves the necessary data from a weather information API (e.g., OpenWeatherMap) based on the extracted intent. This data is input, and weather information is output.

[0659] Step 5:

[0660] The server generates an appropriate response message based on the weather information it has acquired. The response message is then output.

[0661] Step 6:

[0662] The server sends the generated response text to the terminal, which then becomes the input.

[0663] Step 7:

[0664] The terminal responds to the user by voice with a response message, providing the user with an easy-to-understand voice output.

[0665] Health management

[0666] Step 1:

[0667] The user reports by voice, "I have a slight headache this morning." This voice becomes the input.

[0668] Step 2:

[0669] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[0670] Step 3:

[0671] The server analyzes the speech recognition data and extracts important key information (e.g., "headache"). The input is text data, and the extracted key information is output.

[0672] Step 4:

[0673] The server stores the analysis results in a database, and the stored data becomes the input.

[0674] Step 5:

[0675] The server determines whether immediate action is required and generates basic health advice such as "drink lots of water and get plenty of rest." The health advice is output.

[0676] Step 6:

[0677] The server transmits the generated advice to the terminal, and the terminal provides the information to the user by voice. Voice advice is output.

[0678] Health Advice

[0679] Step 1:

[0680] The server analyzes the user's accumulated health data and extracts trends and patterns. The accumulated data is input, and trends and patterns are output.

[0681] Step 2:

[0682] Based on the analysis results, the server automatically generates individual health advice, such as "We recommend you exercise a little more." The health advice is output.

[0683] Step 3:

[0684] The server transmits the generated advice to the terminal, which then provides it to the user by voice. The voice advice is output.

[0685] Medication suggestions and prescriptions

[0686] Step 1:

[0687] The user asks a question by voice, "I have a persistent headache. What should I do?" This voice becomes the input.

[0688] Step 2:

[0689] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[0690] Step 3:

[0691] The server analyzes the user's physical condition data, referencing past data and current status as necessary. Physical condition data is input, and the analysis results are output.

[0692] Step 4:

[0693] The server generates preliminary medication suggestions based on the analysis results. The medication suggestions are output.

[0694] Step 5:

[0695] The server then sends the generated data to an online platform that provides preliminary drug recommendations to physicians.

[0696] Step 6:

[0697] After the doctor checks the data, a formal prescription is generated. The prescription is output.

[0698] Step 7:

[0699] The server sends the prescription to the terminal and notifies the user, who can then select the option to order and collect the medication online as needed.

[0700] (Application example 1)

[0701] 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."

[0702] Although there are systems that allow elderly people and those unfamiliar with technology to receive health management and health advice through voice input, these systems are limited to individual functions and lack multifaceted support. Furthermore, they lack functions for suggesting meals related to health and easy ordering, so comprehensive support is needed to help elderly people stay safe and healthy.

[0703] 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.

[0704] In this invention, the server includes means for capturing a user's voice through voice input, means for converting the captured voice data into a character string, means for analyzing the converted character string and generating an appropriate response, means for audibly communicating the generated response to the user, means for receiving health management information from the user, analyzing it, and storing it in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a doctor as needed based on the user's health data, and means for suggesting and ordering appropriate meals based on the user's health data and voice input. This enables a user to perform comprehensive health management using only voice input, and to suggest and order meals based on their physical condition.

[0705] "Voice input" is the means by which the user's voice is captured and transmitted to the system.

[0706] "Voice data" refers to data that stores captured user voice information in digital form.

[0707] The "means for converting into character string" is a process for analyzing the audio data and converting it into corresponding text data.

[0708] The "means for generating an appropriate response" is the process of creating a response to the user's question or request based on the parsed string.

[0709] "Physical condition management information" is information relating to the user's health condition and symptoms.

[0710] "Physical condition data" refers to measurement and report data relating to the user's health condition and physical condition.

[0711] "Health advice" is advice about health provided based on the user's physical condition data.

[0712] "Medicine suggestion" is the act of recommending appropriate medicine based on the user's physical condition data.

[0713] "Providing information to a doctor" refers to the act of transmitting the user's physical condition data to a doctor and providing information for appropriate treatment and prescription.

[0714] "Meal suggestion" is the act of recommending an appropriate meal menu based on the user's physical condition data and voice input.

[0715] "Meal Ordering" is the process of ordering suggested meals online.

[0716] A "natural language processing engine" is software for understanding and analyzing natural language.

[0717] The configuration and operation of a system for implementing the present invention will be described in detail below.

[0718] 1. System Configuration

[0719] The system mainly consists of the following components:

[0720] Voice input means: Devices with built-in microphones, such as smartphones, smart glasses, and head-mounted displays.

[0721] String conversion method: Software that converts voice data into strings using the Google Cloud Speech-to-Text API.

[0722] Natural language processing means: OpenAI's GPT-4 model is used to analyze the converted text and understand the user's intent.

[0723] Database: A database that uses Firebase to store and manage user physical condition data and voice data.

[0724] Food Delivery API: An API for suggesting and ordering meals using the Uber Eats API.

[0725] TTS Engine: A Text-to-Speech engine is used to generate responses that are then spoken to the user.

[0726] 2. Program Processing Overview

[0727] Voice Input and Recognition

[0728] When a user speaks a question or request, the device's built-in microphone captures the audio data, which is then sent to a server and converted into text using the Google Cloud Speech-to-Text API.

[0729] Natural Language Processing

[0730] The server receives the converted voice data and uses the OpenAI GPT-4 model to analyze the intent of the user's question, such as "What is your recommended meal today?", and generates appropriate meal suggestions.

[0731] Database Reference

[0732] The server accesses the Firebase database and references the user's physical condition data and allergy information, which allows it to provide health advice and dietary suggestions tailored to each individual user.

[0733] Food delivery API integration

[0734] Based on the analysis results and the user's physical condition data, the app will use the Uber Eats API to suggest appropriate meal options. For example, it will suggest specific menu items and provide a function to allow the user to proceed directly to the ordering process.

[0735] Response generation and communication

[0736] The server converts the generated responses and suggestions into speech using a TTS engine and transmits them to the user via the terminal.

[0737] 3. Examples of concrete examples and prompts

[0738] User: "What's your lunch recommendation today?"

[0739] Server side:

[0740] The app checks the user's physical condition data and suggests a "nutritious chicken salad" based on their recent lack of exercise.

[0741] Generates a voice response saying, "Uber Eats has a fresh chicken salad on the menu. Would you like to order it?"

[0742] Example prompt sentence:

[0743] User: "What's your recommendation today?"

[0744] Input to the AI ​​model:

[0745] Consider the user's profile and health data to suggest today's meal recommendations, including taking into account the user's allergies and dietary restrictions.

[0746] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0747] Step 1:

[0748] Voice input

[0749] Input: The user speaks a question or request (e.g., "What's the recommendation today?").

[0750] Processing: The device's microphone captures the user's voice and obtains the voice data.

[0751] Output: The acquired audio data is sent to the server as is.

[0752] Step 2:

[0753] Voice Recognition

[0754] Input: Captured audio data.

[0755] Processing: The server uses the Google Cloud Speech-to-Text API to convert the audio data into text. Specifically, a speech recognition engine analyzes the audio signal and generates corresponding text.

[0756] Output: Text data (e.g., "What's your recommendation today?").

[0757] Step 3:

[0758] Natural Language Processing

[0759] Input: Text data.

[0760] Processing: The server passes the text data to OpenAI's GPT-4 model, which analyzes the intent of the user's question or request. Specifically, the natural language processing engine understands the meaning of the sentence to generate appropriate meal suggestions and responses based on the input text.

[0761] Output: Analysis results (user intent and specific requirements).

[0762] Step 4:

[0763] Database Reference

[0764] Input: Analysis results and user ID.

[0765] Processing: The server accesses the Firebase database to retrieve the user's physical condition data and allergy information. Specifically, it queries the database using the user ID as a key to retrieve related health information.

[0766] Output: User's health data and allergy information.

[0767] Step 5:

[0768] Meal suggestions

[0769] Input: Analysis results, physical condition data, and allergy information.

[0770] Processing: The server uses the Uber Eats API to suggest appropriate meal options based on the analysis results and the user's health data. Specifically, it filters the meal options and selects the most suitable menu for the user.

[0771] Output: A suggested meal (e.g., "chicken salad").

[0772] Step 6:

[0773] Response Generation

[0774] Input: A suggested meal menu.

[0775] Processing: The server generates a response text (e.g., "Uber Eats has fresh chicken salad on the menu. Would you like to order it?"). Specifically, it uses a natural language generation engine to generate a message to be conveyed to the user.

[0776] Output: The response text.

[0777] Step 7:

[0778] Voice response

[0779] Input: Response text.

[0780] Processing: The server uses a text-to-speech engine to convert the response text into voice data, specifically outputting the generated text as a voice signal and sending it to the terminal.

[0781] Output: Audio data.

[0782] Step 8:

[0783] Communication to users

[0784] Input: Audio data.

[0785] Processing: The device plays the audio data and gives the response to the user by voice. Specifically, the device's speaker outputs the audio data so that the user can hear it.

[0786] Output: A spoken response to the user.

[0787] 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.

[0788] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions, as well as an emotion recognition engine that recognizes the user's emotions. The detailed configuration and operation of this system are described below.

[0789] 1. Voice Input and Emotion Recognition

[0790] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the voice data and converts it into text using a voice recognition algorithm, while simultaneously analyzing the user's emotions using an emotion recognition engine. This emotion recognition allows the system to distinguish emotions extracted from the user's voice (e.g., joy, sadness, anger, etc.) and respond accordingly.

[0791] 2. Interactivity and emotional response

[0792] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves data from the necessary sources (e.g., weather information API). Based on the retrieved information, the server generates a response, taking into account the results of the emotion recognition engine. For example, if the user asks a question in a sad voice, the server can add a touch of kindness or encouragement to the response. The generated response is sent to the device and replies to the user via voice.

[0793] 3. Physical and emotional health management

[0794] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and then sends it to the server. The server analyzes the report, extracts important keywords, and stores them in a database along with emotion recognition results. The server takes the user's emotional state into account when generating advice based on the analysis results. For example, if the user is feeling stressed, it can add advice on how to relax. The advice is provided to the user via voice via the device.

[0795] 4. Health advice and emotional support

[0796] The server analyzes the user's accumulated physical and emotional data to extract trends and patterns. Based on this, it automatically generates advice and provides it via voice through the device. For example, if a user has been feeling down recently, it can provide positive advice such as "Try taking a walk or doing some light exercise."

[0797] 5. Medication suggestions and prescriptions

[0798] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition and emotional data and sends the information to a doctor via an online platform. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. At the same time, appropriate support is provided based on the emotion recognition results (e.g., a reassuring message).

[0799] In this way, the present invention is a system that provides more user-friendly and effective support by combining an emotion recognition engine with a series of processes that start with voice input and include dialogue functions, physical condition management, health advice, and medication suggestions and prescriptions.

[0800] The processing flow will be explained below.

[0801] Processing steps for voice input and emotion recognition functions

[0802] Step 1:

[0803] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[0804] Step 2:

[0805] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[0806] Step 3:

[0807] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[0808] Step 4:

[0809] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[0810] Step 5:

[0811] In parallel with the voice recognition, the server uses an emotion recognition engine to determine the user's emotions from the voice data. For example, it analyzes the intensity and rhythm of the sound to estimate emotions such as joy, sadness, and anger.

[0812] Step 6:

[0813] The server returns the converted text data and the determined emotion data to the terminal.

[0814] Step 7:

[0815] The terminal displays the received text data and emotion data to the user and asks for confirmation of the input contents.

[0816] Interactivity and emotional response processing steps

[0817] Step 1:

[0818] The user uses voice input to ask a question, saying "What's the weather like today?"

[0819] Step 2:

[0820] The device captures the audio and sends the data to a server, just like the voice input and emotion recognition functions mentioned earlier.

[0821] Step 3:

[0822] The server analyzes the received text data using a natural language processing engine to understand the intent of the question.

[0823] Step 4:

[0824] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[0825] Step 5:

[0826] The server generates a response based on the acquired information, taking into account the results of the emotion recognition engine, for example, generating a gentle tone of response if the user is depressed.

[0827] Step 6:

[0828] The server transmits the generated response sentence and tone information based on the emotion to the terminal.

[0829] Step 7:

[0830] The terminal responds to the user with the received response sentence in a voice tone that corresponds to the emotion.

[0831] Physical condition management and emotional consideration processing steps

[0832] Step 1:

[0833] The user reports their physical condition by voice, saying, "I have a slight headache this morning."

[0834] Step 2:

[0835] The terminal captures the voice, converts it into a string of characters, and then transmits the voice data and emotion data to the server.

[0836] Step 3:

[0837] The server analyzes the received health report, extracts important keywords, and determines the appropriate response based on the emotion recognition results.

[0838] Step 4:

[0839] The server stores the analysis results and emotion recognition results in a health management database.

[0840] Step 5:

[0841] The server generates advice based on the reported physical condition and emotions. For example, if the user shows signs of anxiety, it adds advice such as "relax and take a deep breath."

[0842] Step 6:

[0843] The server transmits the generated advice to the terminal.

[0844] Step 7:

[0845] The terminal conveys the received advice to the user by voice.

[0846] Medication suggestion and prescription function processing steps

[0847] Step 1:

[0848] The user then voice-inputs a follow-up question about their condition, saying, "I'm having a persistent headache. What should I do?"

[0849] Step 2:

[0850] The device captures the audio, converts it into a string of characters, and sends it to the server.

[0851] Step 3:

[0852] The server analyzes the received character string data and emotion data and compares them with the user's physical condition data.

[0853] Step 4:

[0854] The server transmits the information through an online platform to provide the data to a physician as needed.

[0855] Step 5:

[0856] The doctor reviews the data and generates the necessary prescriptions.

[0857] Step 6:

[0858] The server receives the prescription generated by the doctor and provides the user with medication suggestions, taking into account their emotions. For example, if the user shows anxiety, the server adds a message saying, "Don't worry, we'll prepare your medication for you."

[0859] Step 7:

[0860] The terminal will notify the user that the prescription is ready and advise them on next steps (such as traveling to a pharmacy or online collection).

[0861] Example 2

[0862] 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."

[0863] Conventional voice input systems can convert the user's speech into text and provide information, but they have difficulty recognizing the user's emotional state and responding appropriately. In particular, there are limitations to providing appropriate advice and medication suggestions to users who are in poor health or experiencing psychological stress, and more user-friendly and effective support is needed.

[0864] 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.

[0865] In this invention, the server includes means for converting voice data into character strings, means for analyzing the converted character strings and voice data to recognize the user's emotions, and means for generating an appropriate response based on the emotion recognition result, thereby making it possible to provide responses and advice that take the user's emotions into consideration.

[0866] "Voice input" is a means of capturing the user's voice through a microphone.

[0867] "Captured audio data" refers to audio captured by a microphone and converted into digital form.

[0868] "Means for converting into character string" refers to a technology that analyzes audio data and converts it into text format.

[0869] The "analyzing means" is a process for processing the converted text and audio data to identify its content and sentiment.

[0870] "Means for recognizing emotions" refers to technology for determining a user's emotional state from voice or text.

[0871] A "means for generating a response" is an algorithm or engine for generating an appropriate response based on the analysis results.

[0872] "Physical condition management information" is information relating to the physical condition and mood reported by the user.

[0873] The "means of storing in a database" is a system that organizes and safely stores collected health management information and emotional data.

[0874] The "means for providing health advice" is a technology that generates and provides appropriate health-related advice based on the user's physical condition and emotional data.

[0875] "Professional information provision" is the process of using collected data to provide appropriate information to physicians and other professionals.

[0876] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions with an emotion recognition engine that recognizes the user's emotions. This system is implemented using a specific hardware and software configuration. The detailed configuration and operation of the system are described below.

[0877] Voice Input and Emotion Recognition

[0878] The device captures the user's voice using a built-in or external microphone. The captured voice data is converted into a digital format, compressed, and encoded. This data is then sent as a data stream to the server. The server uses a speech recognition algorithm (e.g., Google Speech-to-Text API) to analyze the voice data and convert it into a string of characters. It also uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to analyze and identify the user's emotional state. For example, if the user says "I have a headache," the system will recognize the emotion "sad" from the voice.

[0879] Interactivity and emotional response

[0880] When a user says, "What's the weather like today?", the device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine (e.g., OpenAI's GPT-3) to analyze the intent of the question. It then retrieves the necessary information from a weather information API database (e.g., OpenWeatherMap) and generates a response based on that information. It also takes into account the results of the emotion recognition engine, and if the user asks sadly, it creates a gentle response such as, "It's sunny today. I hope you have a nice day." The generated response is sent to the device, where it is replied to the user audibly using speech synthesis technology.

[0881] Physical condition management and emotional consideration

[0882] When a user reports their physical condition by saying, "I have a slight headache this morning," the device captures this voice, converts it into text, and sends it to the server. The server analyzes the report and extracts keywords such as "headache." It also stores the emotion recognition results in a database. Next, it creates advice that takes into account the user's stress level and emotional state. For example, it might generate advice such as, "I have a headache, so try drinking some tea and relaxing." This advice is provided to the user via the device.

[0883] Health advice and emotional support

[0884] The server analyzes the user's accumulated physical and emotional data to identify specific trends and patterns. Based on this data, it automatically generates health advice and provides it to the user via voice via the device. For example, if it is known that the user has recently been prone to stress, the server may provide advice such as "Try going for a walk to relax."

[0885] Medication suggestions and prescriptions

[0886] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to a server. The server analyzes the user's physical condition and emotional data and sends it to a specialist via an online platform (e.g., a medical information system). The specialist reviews this data and generates an official prescription. This information is returned to the server and notified to the user via the device. At the same time, a message to ease the user's anxiety (e.g., "Take some medicine and you'll feel better") is also provided.

[0887] Through these processes, the system provides comprehensive and emotionally sensitive support to the user.Examples of prompts for the generative AI model include, "I've been having a bad headache lately. What should I do?" and "What's the weather like today?"

[0888] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0889] Step 1:

[0890] The user makes a verbal request. For example, "What's the weather like today?" The input is the user's voice. The device's microphone captures this voice.

[0891] Step 2:

[0892] The captured audio data is converted to a digital format on the device, which then compresses and encodes the converted audio data and sends it as a data stream to the server. The input is digital audio data, and the output is compressed and encoded audio data.

[0893] Step 3:

[0894] The server analyzes the received audio data and converts it into a text string using a speech recognition algorithm, using the Google Speech-to-Text API. The input is compressed and encoded audio data, and the output is a text string.

[0895] Step 4:

[0896] The server analyzes the converted text string and voice data with an emotion recognition engine to identify the user's emotional state. For example, it uses IBM Watson Tone Analyzer. The input is the text string and voice data, and the output is the emotion analysis result (e.g., the user has the emotion "sad").

[0897] Step 5:

[0898] Based on the emotional state and the text string, the server uses a natural language processing engine to generate a response. OpenAI's GPT-3 can be used here. The input is the emotion analysis result and the text string, and the output is the generated response. For example, a response such as "It's sunny today. Cheer up!" is generated.

[0899] Step 6:

[0900] The generated response is sent from the server to the device, which then uses speech synthesis technology to convert the text response into speech. The input is a text response, and the output is a speech response.

[0901] Step 7:

[0902] The device then transmits the synthesized response to the user through the speaker. The final output is a voice response that the user hears. For example, "It's a sunny day today. Cheer up." The dialogue is completed.

[0903] Step 8:

[0904] When a user reports their physical condition (e.g., "I have a slight headache this morning"), their voice is captured and the data is sent to the server. This data is analyzed, and the physical condition data and emotion data are stored in a database. The input is the voice data about their physical condition, and the output is the analyzed physical condition and emotion data.

[0905] Step 9:

[0906] The server generates health advice based on the accumulated physical condition data and emotional data, and provides it to the user via audio via the terminal. The input is the accumulated data, and the output is audio advice. For example, advice such as "Drink tea to relax" is provided.

[0907] Step 10:

[0908] When a user requests a medication suggestion (e.g., "I have a persistent headache. What should I do?"), the device captures the voice and sends it to the server. The server analyzes the user's physical condition and emotional data and provides the information to the doctor through the online platform. Finally, the doctor's prescription information is returned to the device via the server and notified to the user. The input is the user's question and physical condition data, and the output is the prescription information and a message to soothe the emotions.

[0909] (Application example 2)

[0910] 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."

[0911] Conventional food delivery applications do not suggest meals that take into account the user's physical condition or emotional state, making it difficult to provide services that are appropriate for each user's individual health and psychological state. Furthermore, there is a lack of systems that combine voice input and emotion recognition, and no method is provided for users to easily report their physical and emotional state and receive appropriate support based on that information. This has led to the issue of low user satisfaction.

[0912] The specific processing by the specific 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 means for capturing the user's voice through voice input, means for converting the captured voice data into character strings, means for analyzing the converted character strings and generating an appropriate response, means for audibly conveying the generated response to the user, means for receiving, analyzing, and storing health management information from the user in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a specialist as needed based on the user's health data, means for analyzing the user's emotional state using an emotion recognition engine, means for adjusting responses and advice based on the analyzed emotional state, and means for making individual dietary suggestions based on the user's physical condition and emotional state. This enables personalized dietary suggestions and responses based on the user's physical condition and emotional state.

[0913] "Voice input" is the ability to capture a user's voice and obtain it as digital data for processing.

[0914] "Captured audio data" refers to audio information obtained through audio input and stored and processed as digital data.

[0915] "Means for converting into text" refers to the process or technology that analyzes audio data and converts it into corresponding text.

[0916] The "converted character string" refers to data resulting from converting voice data into character information.

[0917] The "means for generating an appropriate response" is a mechanism for generating an appropriate reply or information for the user based on the parsed string.

[0918] "Physical condition management information" is information about the health condition provided by the user, and is used as the basis for analyzing and managing the physical condition.

[0919] "Physical condition data" refers to data relating to the user's current or past health condition.

[0920] "Means for providing health advice" refers to a function that analyzes the user's physical condition data and provides health advice and suggestions based on the results.

[0921] "Medication suggestion" is the process of recommending appropriate medications based on the user's health condition.

[0922] The "means for providing information to experts" is a mechanism for collecting information about the user's health condition and providing it to experts such as doctors and pharmacists.

[0923] An "emotion recognition engine" is an algorithm or technology that analyzes a user's voice data and identifies their emotional state.

[0924] "Emotional state" refers to the emotional state that the user is currently feeling, and includes emotions such as joy, sadness, and anger.

[0925] A "means for tailoring responses or advice" is a technique or process for tailoring the content of generated responses or health advice based on the user's emotional state.

[0926] The "means for making meal suggestions" is a function that suggests an appropriate meal menu according to the user's physical condition and emotional state.

[0927] This invention is a system that combines voice input, emotion recognition, physical condition management, health advice, and meal suggestions. The configuration and operation of this system will be described below.

[0928] System configuration

[0929] This system mainly consists of the following hardware and software:

[0930] Hardware: Smartphone (with built-in microphone and speaker)

[0931] Software: Speech recognition engine, emotion recognition engine, recommender system, natural language processing engine, cloud database

[0932] System Operation

[0933] 1. Audio capture and transmission:

[0934] The device (smartphone) captures the user's voice input. The captured voice data is temporarily stored on the device. This voice data is compressed and sent to a cloud server as digital data.

[0935] 2. Speech and Emotion Recognition:

[0936] The server converts the received voice data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). At the same time, it analyzes the user's emotional state using an emotion recognition engine (e.g., Azure Cognitive Services Emotion API). The analyzed emotional state is output as a classification result, such as joy, sadness, or anger.

[0937] 3. Interaction processing and response generation:

[0938] The server stores the text data and emotion recognition results in a cloud database. It uses a natural language processing engine (e.g., GPT-4) to understand the user's intent from the analyzed text and generate an appropriate response. The response takes into account the emotion recognition results and adds elements of kindness and encouragement to the response. The generated response is returned to the device as audio and provided to the user.

[0939] 4. Physical fitness and health advice:

[0940] The server receives voice input from the user reporting their physical condition (e.g., "I have a slight headache today"). The server stores the physical condition data in a database and analyzes it to generate health advice. If the user is feeling stressed, it can also add advice on how to relax. Health advice based on the analysis results is provided to the user via the terminal as voice.

[0941] 5. Meal Suggestions:

[0942] It makes personalized meal suggestions based on the user's physical and emotional state. It uses a recommender system to select the meal menu that is best suited to the user's health condition. For example, if a user inputs, "I've been feeling stressed and tired lately," it will suggest meals that will help relieve fatigue. These suggestions are provided to the user via voice and text.

[0943] Specific examples

[0944] For example, if a user says, "I've been feeling stressed and tired lately," the process is as follows:

[0945] Example prompt sentence:

[0946] A user said, "I've been feeling stressed and tired lately." Please create an encouraging message and provide optimal meal suggestions for this user.

[0947] In response to this prompt, the system produces the following output:

[0948] Suggested menu: "Grilled Salmon Set Meal - A nutritionally balanced meal that will help you recover from fatigue."

[0949] Special message: "You seem a little tired today. Eat a balanced meal and relax. Take care!"

[0950] In this way, personalized meal suggestions and responses based on the user's physical condition and emotions are provided, which is expected to improve user satisfaction.

[0951] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0952] Step 1:

[0953] The device accepts voice input. The user speaks about their physical condition and emotions, and the device's microphone captures the voice. The captured voice data is saved as digital data. The input in this step is the user's voice, and the output is digital voice data.

[0954] Step 2:

[0955] The device sends the captured audio data to the cloud server, which then compresses the audio data and transfers it to the cloud server using data streaming technology. The input of this step is digital audio data, and the output is the audio data sent to the cloud server.

[0956] Step 3:

[0957] The server converts the received voice data into text using a speech recognition engine. The server then converts the voice data into text using a speech recognition engine such as Google Cloud Speech-to-Text. The input for this step is the voice data, and the output is the converted text.

[0958] Step 4:

[0959] The server inputs the converted string into an emotion recognition engine to analyze the user's emotional state. It uses the Azure Cognitive Services Emotion API to identify the emotional state (e.g., joy, sadness, anger, etc.). The input of this step is the converted string, and the output is the emotion recognition result.

[0960] Step 5:

[0961] The server saves the string data and emotion recognition results in a cloud database. It uses a database such as MySQL or MongoDB to save the analysis results. The input of this step is the string data and emotion recognition results, and the output is the analysis results saved in the database.

[0962] Step 6:

[0963] The server uses a natural language processing engine to analyze the user's intent and generate an appropriate response. A generative AI model such as GPT-4 is used to understand the user's intent from the input string and generate a response. The input for this step is string data, and the output is the generated response.

[0964] Step 7:

[0965] The server adjusts the response sentence taking into account the emotion recognition results. For example, if the user shows sadness, it adds elements of kindness or encouragement. The input of this step is the generated response sentence and the emotion recognition results, and the output is the adjusted response sentence.

[0966] Step 8:

[0967] The server converts the adjusted response sentence into speech and sends it to the terminal. A speech synthesis engine is used to convert the text into speech data and send that data to the terminal. The input of this step is the adjusted response sentence, and the output is the speech data sent to the terminal.

[0968] Step 9:

[0969] The terminal plays the received voice data and conveys the response to the user. The voice data is played using the terminal's speaker. The input of this step is the voice data, and the output is the voice response conveyed to the user.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] [Third embodiment]

[0974] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0975] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0976] 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).

[0977] 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.

[0978] 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.

[0979] 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).

[0980] 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.

[0981] 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.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] 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."

[0986] Specifically, the configuration and operation of a system for implementing the present invention will be described in detail below. The system mainly includes five main functions: voice input, dialogue function, health management, and medicine suggestion and prescription.

[0987] 1. Voice Input

[0988] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the received voice data and converts it into text using a speech recognition algorithm. The converted text is then sent back to the device, allowing the user to confirm that their voice input was handled correctly.

[0989] 2. Interactive features

[0990] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves the necessary data from an appropriate source (for example, a weather information API). Based on the retrieved information, the server generates a response to the user, sends it to the device, and responds to the user via voice.

[0991] 3. Health Management

[0992] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required and generates basic health advice as necessary. The advice is provided to the user via the device via voice.

[0993] 4. Health Advice

[0994] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as, "I recommend you exercise a little more."

[0995] 5. Medication suggestions and prescriptions

[0996] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. Based on this data, the server generates a preliminary medication suggestion and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[0997] As such, the present invention is a system that seamlessly executes a series of processes, starting with voice input, including dialogue, physical condition management, health advice, and medication suggestions and prescriptions. Users can access many functions using only their voice, increasing convenience and effectiveness, especially for elderly people and users who are unfamiliar with technology.

[0998] The processing flow will be explained below.

[0999] Processing steps for voice input function

[1000] Step 1:

[1001] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[1002] Step 2:

[1003] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[1004] Step 3:

[1005] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[1006] Step 4:

[1007] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[1008] Step 5:

[1009] The server returns the converted text data to the terminal.

[1010] Step 6:

[1011] The terminal displays the received text data to the user and asks for confirmation of the input contents.

[1012] Interactive behavior processing steps

[1013] Step 1:

[1014] The user uses voice input to ask a question or interact, for example, saying "What's the weather like today?"

[1015] Step 2:

[1016] The device captures the audio and converts it into a string, which is then sent to the server.

[1017] Step 3:

[1018] The server analyzes the received string data using a natural language processing engine to understand the intent of the question.

[1019] Step 4:

[1020] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[1021] Step 5:

[1022] The server generates a response based on the acquired information. The generated response is an appropriate response to the user's question.

[1023] Step 6:

[1024] The server sends a response to the terminal.

[1025] Step 7:

[1026] The terminal responds to the user by voice with the received response sentence.

[1027] Health management function processing steps

[1028] Step 1:

[1029] The user reports their physical condition by voice, for example, saying, "I have a slight headache this morning."

[1030] Step 2:

[1031] The device captures the audio and converts it into a string, which is then sent to the server.

[1032] Step 3:

[1033] The server analyzes the received string data and extracts important keywords (e.g., headache).

[1034] Step 4:

[1035] The server stores the extracted data in a health management database.

[1036] Step 5:

[1037] The server generates basic health advice based on the reported physical condition.

[1038] Step 6:

[1039] The server transmits the generated advice to the terminal.

[1040] Step 7:

[1041] The terminal conveys the received advice to the user by voice.

[1042] Medication suggestion and prescription function processing steps

[1043] Step 1:

[1044] The user then speaks a follow-up question about their condition, for example, "What should I do if my headache persists?"

[1045] Step 2:

[1046] The device captures the audio and converts it into a string, which is then sent to the server.

[1047] Step 3:

[1048] The server analyzes the received character string data and compares it with the user's physical condition data.

[1049] Step 4:

[1050] The server transmits the information through an online platform to provide the data to a physician as needed.

[1051] Step 5:

[1052] The doctor reviews the data and generates the necessary prescriptions.

[1053] Step 6:

[1054] The server receives the prescription generated by the doctor and communicates drug suggestions to the user.

[1055] Step 7:

[1056] The terminal will notify the user that the prescription is ready and advise on next steps.

[1057] Example 1

[1058] 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."

[1059] In modern society, it is difficult for elderly people and those unfamiliar with technology to efficiently obtain information and manage their health in daily life. While systems utilizing speech recognition and natural language processing exist, few systems seamlessly address a wide range of daily needs through these technologies. This creates a need for systems that allow users to easily and effectively gather information, manage their health, and receive medication recommendations through voice commands.

[1060] 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.

[1061] In this invention, the server includes means for capturing voice input at the terminal, compressing and encoding it, and transmitting it to the server, means for converting the voice data into text using a voice recognition algorithm at the server and returning the result to the terminal, and means for analyzing the intent of the user's question using a generative AI model and acquiring data from appropriate information sources. This enables users to efficiently acquire information and manage their health using voice, and to provide information to a doctor and receive medication recommendations as needed.

[1062] "Voice input" is a means by which a user provides information to a system using voice.

[1063] A "terminal" is a device that captures audio and transmits data to a server, such as a smartphone or smart speaker.

[1064] A "server" is a central computer system that analyzes voice data sent from a terminal and provides appropriate responses and services.

[1065] "Voice data" refers to data that is a digital recording of a user's voice input.

[1066] "Compression and encoding" refers to the process of reducing the size of captured audio data and converting it into a specific format for efficient transmission.

[1067] A "speech recognition algorithm" is a calculation method or program for converting voice data into a string of characters.

[1068] A "character string" is text data converted by a speech recognition algorithm.

[1069] A "natural language processing engine" is software that analyzes string data, understands its meaning, and generates appropriate responses.

[1070] A "generative AI model" is a type of artificial intelligence technology used to generate appropriate responses to user questions or requests.

[1071] "Physical condition management information" is data provided by a user regarding their own health condition.

[1072] A "database" is a system for storing and managing health management information and accumulated user data over a long period of time.

[1073] "Health advice" refers to suggestions and advice for improving and maintaining the user's health based on health management information.

[1074] "Medicine suggestions" are suggestions for recommending appropriate medicines based on the user's physical condition data.

[1075] "Information provision" is the process of transmitting the user's physical condition data and medication suggestions to doctors and other relevant parties.

[1076] "Data acquisition" is the process of obtaining the required information or data from an external source, such as an API.

[1077] A "trend or pattern" is a consistent change or specific behavior observed in a user's past physical condition data.

[1078] The present invention is a seamless system for efficiently meeting user needs through voice input. The system mainly includes five main functions: voice input, dialogue function, health management, health advice, and medicine suggestion and prescription.

[1079] 1. Voice Input

[1080] The device captures the user's voice using a built-in or external microphone, such as a smartphone or smart speaker. The captured voice data is compressed, encoded, and sent to a server as a data stream.

[1081] The server analyzes the received voice data and converts it into text using speech recognition algorithms such as Google Cloud Speech-to-Text or Amazon Transcribe, then sends the results back to the device, allowing the user to confirm that their voice input was handled correctly.

[1082] Examples:

[1083] User: "What's the weather like today?"

[1084] Example prompt sentence:

[1085] "The user has spoken to ask about today's weather. The server should convert this into text using the speech recognition API, obtain the weather information, and respond."

[1086] 2. Interactive features

[1087] When a user asks a question or wants to have a conversation, they say, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server uses a generative AI model (e.g., GPT-3.5) to analyze the intent of the question and retrieves the necessary data from appropriate sources. For example, based on information obtained from a weather information API, it generates an appropriate response and replies to the user via voice via the device.

[1088] Examples:

[1089] User: "What's the weather like today?"

[1090] Example prompt sentence:

[1091] "The user has asked about today's weather. Please provide an easy-to-understand answer based on the weather information you have obtained."

[1092] 3. Health Management

[1093] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required, and if necessary, generates basic health advice and provides it to the user via the device.

[1094] Examples:

[1095] User: "I have a bit of a headache this morning."

[1096] Example prompt sentence:

[1097] "A user reports how they felt this morning. Extract the information that they had a headache, store it in a database, and provide basic health advice based on that."

[1098] 4. Health Advice

[1099] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as "It is recommended that you exercise a little more."

[1100] Examples:

[1101] Server: "Looking at your recent health data, it seems like you're not getting enough exercise. I'd recommend you get a little more exercise."

[1102] Example prompt sentence:

[1103] "Based on the user's accumulated physical condition data, there is a tendency for lack of exercise. Based on that information, please provide appropriate health advice to the user."

[1104] 5. Medication suggestions and prescriptions

[1105] If a user asks, "I have a persistent headache. What should I do?", the device captures this speech, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. The server generates a preliminary medication suggestion based on this data and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[1106] Examples:

[1107] User: "I'm having a persistent headache. What should I do?"

[1108] Example prompt sentence:

[1109] "The user has reported a persistent headache and requested action. Please refer to their past physical condition data, generate a preliminary medication suggestion, and send it to the doctor for review. After the doctor's review, generate a formal prescription and notify the user."

[1110] In this way, the present invention is a system that seamlessly executes a series of processes, starting with the user's voice input, including dialogue functions, physical condition management, health advice, and drug suggestions and prescriptions. This allows users to use many functions using only their voice, providing convenience especially for elderly people and users who are unfamiliar with technology.

[1111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1112] Voice input

[1113] Step 1:

[1114] The user asks a question by voice, such as "What's the weather like today?" This voice becomes the input.

[1115] Step 2:

[1116] The device captures the user's voice using a built-in or external microphone, compresses and encodes the captured voice data as input, and outputs the encoded voice data.

[1117] Step 3:

[1118] The terminal transmits the encoded audio data to the server as a data stream, and the transmitted audio data becomes the input.

[1119] Step 4:

[1120] The server analyzes the received voice data using speech recognition algorithms such as Google Cloud Speech-to-Text and Amazon Transcribe. The voice data is converted into text and the text data is output.

[1121] Step 5:

[1122] The server returns the converted string to the terminal, and the returned string data becomes the input.

[1123] Step 6:

[1124] The device will then display the received text data to the user or provide audio feedback, allowing the user to confirm that their voice input was handled correctly.

[1125] Interactive features

[1126] Step 1:

[1127] The user asks aloud, "What's the weather like today?" This speech becomes the input.

[1128] Step 2:

[1129] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[1130] Step 3:

[1131] The server uses a generative AI model (e.g., GPT-3.5) to analyze the intent of the question. The string data is input, and the intent, such as "I want to know the weather information," is extracted.

[1132] Step 4:

[1133] The server retrieves the necessary data from a weather information API (e.g., OpenWeatherMap) based on the extracted intent. This data is input, and weather information is output.

[1134] Step 5:

[1135] The server generates an appropriate response message based on the weather information it has acquired. The response message is then output.

[1136] Step 6:

[1137] The server sends the generated response text to the terminal, which then becomes the input.

[1138] Step 7:

[1139] The terminal responds to the user by voice with a response message, providing the user with an easy-to-understand voice output.

[1140] Health management

[1141] Step 1:

[1142] The user reports by voice, "I have a slight headache this morning." This voice becomes the input.

[1143] Step 2:

[1144] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[1145] Step 3:

[1146] The server analyzes the speech recognition data and extracts important key information (e.g., "headache"). The input is text data, and the extracted key information is output.

[1147] Step 4:

[1148] The server stores the analysis results in a database, and the stored data becomes the input.

[1149] Step 5:

[1150] The server determines whether immediate action is required and generates basic health advice such as "drink lots of water and get plenty of rest." The health advice is output.

[1151] Step 6:

[1152] The server transmits the generated advice to the terminal, and the terminal provides the information to the user by voice. Voice advice is output.

[1153] Health Advice

[1154] Step 1:

[1155] The server analyzes the user's accumulated health data and extracts trends and patterns. The accumulated data is input, and trends and patterns are output.

[1156] Step 2:

[1157] Based on the analysis results, the server automatically generates individual health advice, such as "We recommend you exercise a little more." The health advice is output.

[1158] Step 3:

[1159] The server transmits the generated advice to the terminal, which then provides it to the user by voice. The voice advice is output.

[1160] Medication suggestions and prescriptions

[1161] Step 1:

[1162] The user asks a question by voice, "I have a persistent headache. What should I do?" This voice becomes the input.

[1163] Step 2:

[1164] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[1165] Step 3:

[1166] The server analyzes the user's physical condition data, referencing past data and current status as necessary. Physical condition data is input, and the analysis results are output.

[1167] Step 4:

[1168] The server generates preliminary medication suggestions based on the analysis results. The medication suggestions are output.

[1169] Step 5:

[1170] The server then sends the generated data to an online platform that provides preliminary drug recommendations to physicians.

[1171] Step 6:

[1172] After the doctor checks the data, a formal prescription is generated. The prescription is output.

[1173] Step 7:

[1174] The server sends the prescription to the terminal and notifies the user, who can then select the option to order and collect the medication online as needed.

[1175] (Application example 1)

[1176] 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."

[1177] Although there are systems that allow elderly people and those unfamiliar with technology to receive health management and health advice through voice input, these systems are limited to individual functions and lack multifaceted support. Furthermore, they lack functions for suggesting meals related to health and easy ordering, so comprehensive support is needed to help elderly people stay safe and healthy.

[1178] 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.

[1179] In this invention, the server includes means for capturing a user's voice through voice input, means for converting the captured voice data into a character string, means for analyzing the converted character string and generating an appropriate response, means for audibly communicating the generated response to the user, means for receiving health management information from the user, analyzing it, and storing it in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a doctor as needed based on the user's health data, and means for suggesting and ordering appropriate meals based on the user's health data and voice input. This enables a user to perform comprehensive health management using only voice input, and to suggest and order meals based on their physical condition.

[1180] "Voice input" is the means by which the user's voice is captured and transmitted to the system.

[1181] "Voice data" refers to data that stores captured user voice information in digital form.

[1182] The "means for converting into character string" is a process for analyzing the audio data and converting it into corresponding text data.

[1183] The "means for generating an appropriate response" is the process of creating a response to the user's question or request based on the parsed string.

[1184] "Physical condition management information" is information relating to the user's health condition and symptoms.

[1185] "Physical condition data" refers to measurement and report data relating to the user's health condition and physical condition.

[1186] "Health advice" is advice about health provided based on the user's physical condition data.

[1187] "Medicine suggestion" is the act of recommending appropriate medicine based on the user's physical condition data.

[1188] "Providing information to a doctor" refers to the act of transmitting the user's physical condition data to a doctor and providing information for appropriate treatment and prescription.

[1189] "Meal suggestion" is the act of recommending an appropriate meal menu based on the user's physical condition data and voice input.

[1190] "Meal Ordering" is the process of ordering suggested meals online.

[1191] A "natural language processing engine" is software for understanding and analyzing natural language.

[1192] The configuration and operation of a system for implementing the present invention will be described in detail below.

[1193] 1. System Configuration

[1194] The system mainly consists of the following components:

[1195] Voice input means: Devices with built-in microphones, such as smartphones, smart glasses, and head-mounted displays.

[1196] String conversion method: Software that converts voice data into strings using the Google Cloud Speech-to-Text API.

[1197] Natural language processing means: OpenAI's GPT-4 model is used to analyze the converted text and understand the user's intent.

[1198] Database: A database that uses Firebase to store and manage user physical condition data and voice data.

[1199] Food Delivery API: An API for suggesting and ordering meals using the Uber Eats API.

[1200] TTS Engine: A Text-to-Speech engine is used to generate responses that are then spoken to the user.

[1201] 2. Program Processing Overview

[1202] Voice Input and Recognition

[1203] When a user speaks a question or request, the device's built-in microphone captures the audio data, which is then sent to a server and converted into text using the Google Cloud Speech-to-Text API.

[1204] Natural Language Processing

[1205] The server receives the converted voice data and uses the OpenAI GPT-4 model to analyze the intent of the user's question, such as "What is your recommended meal today?", and generates appropriate meal suggestions.

[1206] Database Reference

[1207] The server accesses the Firebase database and references the user's physical condition data and allergy information, which allows it to provide health advice and dietary suggestions tailored to each individual user.

[1208] Food delivery API integration

[1209] Based on the analysis results and the user's physical condition data, the app will use the Uber Eats API to suggest appropriate meal options. For example, it will suggest specific menu items and provide a function to allow the user to proceed directly to the ordering process.

[1210] Response generation and communication

[1211] The server converts the generated responses and suggestions into speech using a TTS engine and transmits them to the user via the terminal.

[1212] 3. Examples of concrete examples and prompts

[1213] User: "What's your lunch recommendation today?"

[1214] Server side:

[1215] The app checks the user's physical condition data and suggests a "nutritious chicken salad" based on their recent lack of exercise.

[1216] Generates a voice response saying, "Uber Eats has a fresh chicken salad on the menu. Would you like to order it?"

[1217] Example prompt sentence:

[1218] User: "What's your recommendation today?"

[1219] Input to the AI ​​model:

[1220] Consider the user's profile and health data to suggest today's meal recommendations, including taking into account the user's allergies and dietary restrictions.

[1221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1222] Step 1:

[1223] Voice input

[1224] Input: The user speaks a question or request (e.g., "What's the recommendation today?").

[1225] Processing: The device's microphone captures the user's voice and obtains the voice data.

[1226] Output: The acquired audio data is sent to the server as is.

[1227] Step 2:

[1228] Voice Recognition

[1229] Input: Captured audio data.

[1230] Processing: The server uses the Google Cloud Speech-to-Text API to convert the audio data into text. Specifically, a speech recognition engine analyzes the audio signal and generates corresponding text.

[1231] Output: Text data (e.g., "What's your recommendation today?").

[1232] Step 3:

[1233] Natural Language Processing

[1234] Input: Text data.

[1235] Processing: The server passes the text data to OpenAI's GPT-4 model, which analyzes the intent of the user's question or request. Specifically, the natural language processing engine understands the meaning of the sentence to generate appropriate meal suggestions and responses based on the input text.

[1236] Output: Analysis results (user intent and specific requirements).

[1237] Step 4:

[1238] Database Reference

[1239] Input: Analysis results and user ID.

[1240] Processing: The server accesses the Firebase database to retrieve the user's physical condition data and allergy information. Specifically, it queries the database using the user ID as a key to retrieve related health information.

[1241] Output: User's health data and allergy information.

[1242] Step 5:

[1243] Meal suggestions

[1244] Input: Analysis results, physical condition data, and allergy information.

[1245] Processing: The server uses the Uber Eats API to suggest appropriate meal options based on the analysis results and the user's health data. Specifically, it filters the meal options and selects the most suitable menu for the user.

[1246] Output: A suggested meal (e.g., "chicken salad").

[1247] Step 6:

[1248] Response Generation

[1249] Input: A suggested meal menu.

[1250] Processing: The server generates a response text (e.g., "Uber Eats has fresh chicken salad on the menu. Would you like to order it?"). Specifically, it uses a natural language generation engine to generate a message to be conveyed to the user.

[1251] Output: The response text.

[1252] Step 7:

[1253] Voice response

[1254] Input: Response text.

[1255] Processing: The server uses a text-to-speech engine to convert the response text into voice data, specifically outputting the generated text as a voice signal and sending it to the terminal.

[1256] Output: Audio data.

[1257] Step 8:

[1258] Communication to users

[1259] Input: Audio data.

[1260] Processing: The device plays the audio data and gives the response to the user by voice. Specifically, the device's speaker outputs the audio data so that the user can hear it.

[1261] Output: A spoken response to the user.

[1262] 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.

[1263] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions, as well as an emotion recognition engine that recognizes the user's emotions. The detailed configuration and operation of this system are described below.

[1264] 1. Voice Input and Emotion Recognition

[1265] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the voice data and converts it into text using a voice recognition algorithm, while simultaneously analyzing the user's emotions using an emotion recognition engine. This emotion recognition allows the system to distinguish emotions extracted from the user's voice (e.g., joy, sadness, anger, etc.) and respond accordingly.

[1266] 2. Interactivity and emotional response

[1267] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves data from the necessary sources (e.g., weather information API). Based on the retrieved information, the server generates a response, taking into account the results of the emotion recognition engine. For example, if the user asks a question in a sad voice, the server can add a touch of kindness or encouragement to the response. The generated response is sent to the device and replies to the user via voice.

[1268] 3. Physical and emotional health management

[1269] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and then sends it to the server. The server analyzes the report, extracts important keywords, and stores them in a database along with emotion recognition results. The server takes the user's emotional state into account when generating advice based on the analysis results. For example, if the user is feeling stressed, it can add advice on how to relax. The advice is provided to the user via voice via the device.

[1270] 4. Health advice and emotional support

[1271] The server analyzes the user's accumulated physical and emotional data to extract trends and patterns. Based on this, it automatically generates advice and provides it via voice through the device. For example, if a user has been feeling down recently, it can provide positive advice such as "Try taking a walk or doing some light exercise."

[1272] 5. Medication suggestions and prescriptions

[1273] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition and emotional data and sends the information to a doctor via an online platform. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. At the same time, appropriate support is provided based on the emotion recognition results (e.g., a reassuring message).

[1274] In this way, the present invention is a system that provides more user-friendly and effective support by combining an emotion recognition engine with a series of processes that start with voice input and include dialogue functions, physical condition management, health advice, and medication suggestions and prescriptions.

[1275] The processing flow will be explained below.

[1276] Processing steps for voice input and emotion recognition functions

[1277] Step 1:

[1278] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[1279] Step 2:

[1280] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[1281] Step 3:

[1282] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[1283] Step 4:

[1284] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[1285] Step 5:

[1286] In parallel with the voice recognition, the server uses an emotion recognition engine to determine the user's emotions from the voice data. For example, it analyzes the intensity and rhythm of the sound to estimate emotions such as joy, sadness, and anger.

[1287] Step 6:

[1288] The server returns the converted text data and the determined emotion data to the terminal.

[1289] Step 7:

[1290] The terminal displays the received text data and emotion data to the user and asks for confirmation of the input contents.

[1291] Interactivity and emotional response processing steps

[1292] Step 1:

[1293] The user uses voice input to ask a question, saying "What's the weather like today?"

[1294] Step 2:

[1295] The device captures the audio and sends the data to a server, just like the voice input and emotion recognition functions mentioned earlier.

[1296] Step 3:

[1297] The server analyzes the received text data using a natural language processing engine to understand the intent of the question.

[1298] Step 4:

[1299] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[1300] Step 5:

[1301] The server generates a response based on the acquired information, taking into account the results of the emotion recognition engine, for example, generating a gentle tone of response if the user is depressed.

[1302] Step 6:

[1303] The server transmits the generated response sentence and tone information based on the emotion to the terminal.

[1304] Step 7:

[1305] The terminal responds to the user with the received response sentence in a voice tone that corresponds to the emotion.

[1306] Physical condition management and emotional consideration processing steps

[1307] Step 1:

[1308] The user reports their physical condition by voice, saying, "I have a slight headache this morning."

[1309] Step 2:

[1310] The terminal captures the voice, converts it into a string of characters, and then transmits the voice data and emotion data to the server.

[1311] Step 3:

[1312] The server analyzes the received health report, extracts important keywords, and determines the appropriate response based on the emotion recognition results.

[1313] Step 4:

[1314] The server stores the analysis results and emotion recognition results in a health management database.

[1315] Step 5:

[1316] The server generates advice based on the reported physical condition and emotions. For example, if the user shows signs of anxiety, it adds advice such as "relax and take a deep breath."

[1317] Step 6:

[1318] The server transmits the generated advice to the terminal.

[1319] Step 7:

[1320] The terminal conveys the received advice to the user by voice.

[1321] Medication suggestion and prescription function processing steps

[1322] Step 1:

[1323] The user then voice-inputs a follow-up question about their condition, saying, "I'm having a persistent headache. What should I do?"

[1324] Step 2:

[1325] The device captures the audio, converts it into a string of characters, and sends it to the server.

[1326] Step 3:

[1327] The server analyzes the received character string data and emotion data and compares them with the user's physical condition data.

[1328] Step 4:

[1329] The server transmits the information through an online platform to provide the data to a physician as needed.

[1330] Step 5:

[1331] The doctor reviews the data and generates the necessary prescriptions.

[1332] Step 6:

[1333] The server receives the prescription generated by the doctor and provides the user with medication suggestions, taking into account their emotions. For example, if the user shows anxiety, the server adds a message saying, "Don't worry, we'll prepare your medication for you."

[1334] Step 7:

[1335] The terminal will notify the user that the prescription is ready and advise them on next steps (such as traveling to a pharmacy or online collection).

[1336] Example 2

[1337] 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."

[1338] Conventional voice input systems can convert the user's speech into text and provide information, but they have difficulty recognizing the user's emotional state and responding appropriately. In particular, there are limitations to providing appropriate advice and medication suggestions to users who are in poor health or experiencing psychological stress, and more user-friendly and effective support is needed.

[1339] 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.

[1340] In this invention, the server includes means for converting voice data into character strings, means for analyzing the converted character strings and voice data to recognize the user's emotions, and means for generating an appropriate response based on the emotion recognition result, thereby making it possible to provide responses and advice that take the user's emotions into consideration.

[1341] "Voice input" is a means of capturing the user's voice through a microphone.

[1342] "Captured audio data" refers to audio captured by a microphone and converted into digital form.

[1343] "Means for converting into character string" refers to a technology that analyzes audio data and converts it into text format.

[1344] The "analyzing means" is a process for processing the converted text and audio data to identify its content and sentiment.

[1345] "Means for recognizing emotions" refers to technology for determining a user's emotional state from voice or text.

[1346] A "means for generating a response" is an algorithm or engine for generating an appropriate response based on the analysis results.

[1347] "Physical condition management information" is information relating to the physical condition and mood reported by the user.

[1348] The "means of storing in a database" is a system that organizes and safely stores collected health management information and emotional data.

[1349] The "means for providing health advice" is a technology that generates and provides appropriate health-related advice based on the user's physical condition and emotional data.

[1350] "Professional information provision" is the process of using collected data to provide appropriate information to physicians and other professionals.

[1351] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions with an emotion recognition engine that recognizes the user's emotions. This system is implemented using a specific hardware and software configuration. The detailed configuration and operation of the system are described below.

[1352] Voice Input and Emotion Recognition

[1353] The device captures the user's voice using a built-in or external microphone. The captured voice data is converted into a digital format, compressed, and encoded. This data is then sent as a data stream to the server. The server uses a speech recognition algorithm (e.g., Google Speech-to-Text API) to analyze the voice data and convert it into a string of characters. It also uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to analyze and identify the user's emotional state. For example, if the user says "I have a headache," the system will recognize the emotion "sad" from the voice.

[1354] Interactivity and emotional response

[1355] When a user says, "What's the weather like today?", the device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine (e.g., OpenAI's GPT-3) to analyze the intent of the question. It then retrieves the necessary information from a weather information API database (e.g., OpenWeatherMap) and generates a response based on that information. It also takes into account the results of the emotion recognition engine, and if the user asks sadly, it creates a gentle response such as, "It's sunny today. I hope you have a nice day." The generated response is sent to the device, where it is replied to the user audibly using speech synthesis technology.

[1356] Physical condition management and emotional consideration

[1357] When a user reports their physical condition by saying, "I have a slight headache this morning," the device captures this voice, converts it into text, and sends it to the server. The server analyzes the report and extracts keywords such as "headache." It also stores the emotion recognition results in a database. Next, it creates advice that takes into account the user's stress level and emotional state. For example, it might generate advice such as, "I have a headache, so try drinking some tea and relaxing." This advice is provided to the user via the device.

[1358] Health advice and emotional support

[1359] The server analyzes the user's accumulated physical and emotional data to identify specific trends and patterns. Based on this data, it automatically generates health advice and provides it to the user via voice via the device. For example, if it is known that the user has recently been prone to stress, the server may provide advice such as "Try going for a walk to relax."

[1360] Medication suggestions and prescriptions

[1361] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to a server. The server analyzes the user's physical condition and emotional data and sends it to a specialist via an online platform (e.g., a medical information system). The specialist reviews this data and generates an official prescription. This information is returned to the server and notified to the user via the device. At the same time, a message to ease the user's anxiety (e.g., "Take some medicine and you'll feel better") is also provided.

[1362] Through these processes, the system provides comprehensive and emotionally sensitive support to the user.Examples of prompts for the generative AI model include, "I've been having a bad headache lately. What should I do?" and "What's the weather like today?"

[1363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1364] Step 1:

[1365] The user makes a verbal request. For example, "What's the weather like today?" The input is the user's voice. The device's microphone captures this voice.

[1366] Step 2:

[1367] The captured audio data is converted to a digital format on the device, which then compresses and encodes the converted audio data and sends it as a data stream to the server. The input is digital audio data, and the output is compressed and encoded audio data.

[1368] Step 3:

[1369] The server analyzes the received audio data and converts it into a text string using a speech recognition algorithm, using the Google Speech-to-Text API. The input is compressed and encoded audio data, and the output is a text string.

[1370] Step 4:

[1371] The server analyzes the converted text string and voice data with an emotion recognition engine to identify the user's emotional state. For example, it uses IBM Watson Tone Analyzer. The input is the text string and voice data, and the output is the emotion analysis result (e.g., the user has the emotion "sad").

[1372] Step 5:

[1373] Based on the emotional state and the text string, the server uses a natural language processing engine to generate a response. OpenAI's GPT-3 can be used here. The input is the emotion analysis result and the text string, and the output is the generated response. For example, a response such as "It's sunny today. Cheer up!" is generated.

[1374] Step 6:

[1375] The generated response is sent from the server to the device, which then uses speech synthesis technology to convert the text response into speech. The input is a text response, and the output is a speech response.

[1376] Step 7:

[1377] The device then transmits the synthesized response to the user through the speaker. The final output is a voice response that the user hears. For example, "It's a sunny day today. Cheer up." The dialogue is completed.

[1378] Step 8:

[1379] When a user reports their physical condition (e.g., "I have a slight headache this morning"), their voice is captured and the data is sent to the server. This data is analyzed, and the physical condition data and emotion data are stored in a database. The input is the voice data about their physical condition, and the output is the analyzed physical condition and emotion data.

[1380] Step 9:

[1381] The server generates health advice based on the accumulated physical condition data and emotional data, and provides it to the user via audio via the terminal. The input is the accumulated data, and the output is audio advice. For example, advice such as "Drink tea to relax" is provided.

[1382] Step 10:

[1383] When a user requests a medication suggestion (e.g., "I have a persistent headache. What should I do?"), the device captures the voice and sends it to the server. The server analyzes the user's physical condition and emotional data and provides the information to the doctor through the online platform. Finally, the doctor's prescription information is returned to the device via the server and notified to the user. The input is the user's question and physical condition data, and the output is the prescription information and a message to soothe the emotions.

[1384] (Application example 2)

[1385] 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."

[1386] Conventional food delivery applications do not suggest meals that take into account the user's physical condition or emotional state, making it difficult to provide services that are appropriate for each user's individual health and psychological state. Furthermore, there is a lack of systems that combine voice input and emotion recognition, and no method is provided for users to easily report their physical and emotional state and receive appropriate support based on that information. This has led to the issue of low user satisfaction.

[1387] The specific processing by the specific 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 means for capturing the user's voice through voice input, means for converting the captured voice data into character strings, means for analyzing the converted character strings and generating an appropriate response, means for audibly conveying the generated response to the user, means for receiving, analyzing, and storing health management information from the user in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a specialist as needed based on the user's health data, means for analyzing the user's emotional state using an emotion recognition engine, means for adjusting responses and advice based on the analyzed emotional state, and means for making individual dietary suggestions based on the user's physical condition and emotional state. This enables personalized dietary suggestions and responses based on the user's physical condition and emotional state.

[1388] "Voice input" is the ability to capture a user's voice and obtain it as digital data for processing.

[1389] "Captured audio data" refers to audio information obtained through audio input and stored and processed as digital data.

[1390] "Means for converting into text" refers to the process or technology that analyzes audio data and converts it into corresponding text.

[1391] The "converted character string" refers to data resulting from converting voice data into character information.

[1392] The "means for generating an appropriate response" is a mechanism for generating an appropriate reply or information for the user based on the parsed string.

[1393] "Physical condition management information" is information about the health condition provided by the user, and is used as the basis for analyzing and managing the physical condition.

[1394] "Physical condition data" refers to data relating to the user's current or past health condition.

[1395] "Means for providing health advice" refers to a function that analyzes the user's physical condition data and provides health advice and suggestions based on the results.

[1396] "Medication suggestion" is the process of recommending appropriate medications based on the user's health condition.

[1397] The "means for providing information to experts" is a mechanism for collecting information about the user's health condition and providing it to experts such as doctors and pharmacists.

[1398] An "emotion recognition engine" is an algorithm or technology that analyzes a user's voice data and identifies their emotional state.

[1399] "Emotional state" refers to the emotional state that the user is currently feeling, and includes emotions such as joy, sadness, and anger.

[1400] A "means for tailoring responses or advice" is a technique or process for tailoring the content of generated responses or health advice based on the user's emotional state.

[1401] The "means for making meal suggestions" is a function that suggests an appropriate meal menu according to the user's physical condition and emotional state.

[1402] This invention is a system that combines voice input, emotion recognition, physical condition management, health advice, and meal suggestions. The configuration and operation of this system will be described below.

[1403] System configuration

[1404] This system mainly consists of the following hardware and software:

[1405] Hardware: Smartphone (with built-in microphone and speaker)

[1406] Software: Speech recognition engine, emotion recognition engine, recommender system, natural language processing engine, cloud database

[1407] System Operation

[1408] 1. Audio capture and transmission:

[1409] The device (smartphone) captures the user's voice input. The captured voice data is temporarily stored on the device. This voice data is compressed and sent to a cloud server as digital data.

[1410] 2. Speech and Emotion Recognition:

[1411] The server converts the received voice data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). At the same time, it analyzes the user's emotional state using an emotion recognition engine (e.g., Azure Cognitive Services Emotion API). The analyzed emotional state is output as a classification result, such as joy, sadness, or anger.

[1412] 3. Interaction processing and response generation:

[1413] The server stores the text data and emotion recognition results in a cloud database. It uses a natural language processing engine (e.g., GPT-4) to understand the user's intent from the analyzed text and generate an appropriate response. The response takes into account the emotion recognition results and adds elements of kindness and encouragement to the response. The generated response is returned to the device as audio and provided to the user.

[1414] 4. Physical fitness and health advice:

[1415] The server receives voice input from the user reporting their physical condition (e.g., "I have a slight headache today"). The server stores the physical condition data in a database and analyzes it to generate health advice. If the user is feeling stressed, it can also add advice on how to relax. Health advice based on the analysis results is provided to the user via the terminal as voice.

[1416] 5. Meal Suggestions:

[1417] It makes personalized meal suggestions based on the user's physical and emotional state. It uses a recommender system to select the meal menu that is best suited to the user's health condition. For example, if a user inputs, "I've been feeling stressed and tired lately," it will suggest meals that will help relieve fatigue. These suggestions are provided to the user via voice and text.

[1418] Specific examples

[1419] For example, if a user says, "I've been feeling stressed and tired lately," the process is as follows:

[1420] Example prompt sentence:

[1421] A user said, "I've been feeling stressed and tired lately." Please create an encouraging message and provide optimal meal suggestions for this user.

[1422] In response to this prompt, the system produces the following output:

[1423] Suggested menu: "Grilled Salmon Set Meal - A nutritionally balanced meal that will help you recover from fatigue."

[1424] Special message: "You seem a little tired today. Eat a balanced meal and relax. Take care!"

[1425] In this way, personalized meal suggestions and responses based on the user's physical condition and emotions are provided, which is expected to improve user satisfaction.

[1426] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1427] Step 1:

[1428] The device accepts voice input. The user speaks about their physical condition and emotions, and the device's microphone captures the voice. The captured voice data is saved as digital data. The input in this step is the user's voice, and the output is digital voice data.

[1429] Step 2:

[1430] The device sends the captured audio data to the cloud server, which then compresses the audio data and transfers it to the cloud server using data streaming technology. The input of this step is digital audio data, and the output is the audio data sent to the cloud server.

[1431] Step 3:

[1432] The server converts the received voice data into text using a speech recognition engine. The server then converts the voice data into text using a speech recognition engine such as Google Cloud Speech-to-Text. The input for this step is the voice data, and the output is the converted text.

[1433] Step 4:

[1434] The server inputs the converted string into an emotion recognition engine to analyze the user's emotional state. It uses the Azure Cognitive Services Emotion API to identify the emotional state (e.g., joy, sadness, anger, etc.). The input of this step is the converted string, and the output is the emotion recognition result.

[1435] Step 5:

[1436] The server saves the string data and emotion recognition results in a cloud database. It uses a database such as MySQL or MongoDB to save the analysis results. The input of this step is the string data and emotion recognition results, and the output is the analysis results saved in the database.

[1437] Step 6:

[1438] The server uses a natural language processing engine to analyze the user's intent and generate an appropriate response. A generative AI model such as GPT-4 is used to understand the user's intent from the input string and generate a response. The input for this step is string data, and the output is the generated response.

[1439] Step 7:

[1440] The server adjusts the response sentence taking into account the emotion recognition results. For example, if the user shows sadness, it adds elements of kindness or encouragement. The input of this step is the generated response sentence and the emotion recognition results, and the output is the adjusted response sentence.

[1441] Step 8:

[1442] The server converts the adjusted response sentence into speech and sends it to the terminal. A speech synthesis engine is used to convert the text into speech data and send that data to the terminal. The input of this step is the adjusted response sentence, and the output is the speech data sent to the terminal.

[1443] Step 9:

[1444] The terminal plays the received voice data and conveys the response to the user. The voice data is played using the terminal's speaker. The input of this step is the voice data, and the output is the voice response conveyed to the user.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] [Fourth embodiment]

[1449] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1450] 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.

[1451] 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).

[1452] 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.

[1453] 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.

[1454] 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).

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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.

[1459] 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.

[1460] 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.

[1461] 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."

[1462] Specifically, the configuration and operation of a system for implementing the present invention will be described in detail below. The system mainly includes five main functions: voice input, dialogue function, health management, and medicine suggestion and prescription.

[1463] 1. Voice Input

[1464] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the received voice data and converts it into text using a speech recognition algorithm. The converted text is then sent back to the device, allowing the user to confirm that their voice input was handled correctly.

[1465] 2. Interactive features

[1466] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves the necessary data from an appropriate source (for example, a weather information API). Based on the retrieved information, the server generates a response to the user, sends it to the device, and responds to the user via voice.

[1467] 3. Health Management

[1468] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required and generates basic health advice as necessary. The advice is provided to the user via the device via voice.

[1469] 4. Health Advice

[1470] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as, "I recommend you exercise a little more."

[1471] 5. Medication suggestions and prescriptions

[1472] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. Based on this data, the server generates a preliminary medication suggestion and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[1473] As such, the present invention is a system that seamlessly executes a series of processes, starting with voice input, including dialogue, physical condition management, health advice, and medication suggestions and prescriptions. Users can access many functions using only their voice, increasing convenience and effectiveness, especially for elderly people and users who are unfamiliar with technology.

[1474] The processing flow will be explained below.

[1475] Processing steps for voice input function

[1476] Step 1:

[1477] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[1478] Step 2:

[1479] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[1480] Step 3:

[1481] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[1482] Step 4:

[1483] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[1484] Step 5:

[1485] The server returns the converted text data to the terminal.

[1486] Step 6:

[1487] The terminal displays the received text data to the user and asks for confirmation of the input contents.

[1488] Interactive behavior processing steps

[1489] Step 1:

[1490] The user uses voice input to ask a question or interact, for example, saying "What's the weather like today?"

[1491] Step 2:

[1492] The device captures the audio and converts it into a string, which is then sent to the server.

[1493] Step 3:

[1494] The server analyzes the received string data using a natural language processing engine to understand the intent of the question.

[1495] Step 4:

[1496] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[1497] Step 5:

[1498] The server generates a response based on the acquired information. The generated response is an appropriate response to the user's question.

[1499] Step 6:

[1500] The server sends a response to the terminal.

[1501] Step 7:

[1502] The terminal responds to the user by voice with the received response sentence.

[1503] Health management function processing steps

[1504] Step 1:

[1505] The user reports their physical condition by voice, for example, saying, "I have a slight headache this morning."

[1506] Step 2:

[1507] The device captures the audio and converts it into a string, which is then sent to the server.

[1508] Step 3:

[1509] The server analyzes the received string data and extracts important keywords (e.g., headache).

[1510] Step 4:

[1511] The server stores the extracted data in a health management database.

[1512] Step 5:

[1513] The server generates basic health advice based on the reported physical condition.

[1514] Step 6:

[1515] The server transmits the generated advice to the terminal.

[1516] Step 7:

[1517] The terminal conveys the received advice to the user by voice.

[1518] Medication suggestion and prescription function processing steps

[1519] Step 1:

[1520] The user then speaks a follow-up question about their condition, for example, "What should I do if my headache persists?"

[1521] Step 2:

[1522] The device captures the audio and converts it into a string, which is then sent to the server.

[1523] Step 3:

[1524] The server analyzes the received character string data and compares it with the user's physical condition data.

[1525] Step 4:

[1526] The server transmits the information through an online platform to provide the data to a physician as needed.

[1527] Step 5:

[1528] The doctor reviews the data and generates the necessary prescriptions.

[1529] Step 6:

[1530] The server receives the prescription generated by the doctor and communicates drug suggestions to the user.

[1531] Step 7:

[1532] The terminal will notify the user that the prescription is ready and advise on next steps.

[1533] Example 1

[1534] 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."

[1535] In modern society, it is difficult for elderly people and those unfamiliar with technology to efficiently obtain information and manage their health in daily life. While systems utilizing speech recognition and natural language processing exist, few systems seamlessly address a wide range of daily needs through these technologies. This creates a need for systems that allow users to easily and effectively gather information, manage their health, and receive medication recommendations through voice commands.

[1536] 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.

[1537] In this invention, the server includes means for capturing voice input at the terminal, compressing and encoding it, and transmitting it to the server, means for converting the voice data into text using a voice recognition algorithm at the server and returning the result to the terminal, and means for analyzing the intent of the user's question using a generative AI model and acquiring data from appropriate information sources. This enables users to efficiently acquire information and manage their health using voice, and to provide information to a doctor and receive medication recommendations as needed.

[1538] "Voice input" is a means by which a user provides information to a system using voice.

[1539] A "terminal" is a device that captures audio and transmits data to a server, such as a smartphone or smart speaker.

[1540] A "server" is a central computer system that analyzes voice data sent from a terminal and provides appropriate responses and services.

[1541] "Voice data" refers to data that is a digital recording of a user's voice input.

[1542] "Compression and encoding" refers to the process of reducing the size of captured audio data and converting it into a specific format for efficient transmission.

[1543] A "speech recognition algorithm" is a calculation method or program for converting voice data into a string of characters.

[1544] A "character string" is text data converted by a speech recognition algorithm.

[1545] A "natural language processing engine" is software that analyzes string data, understands its meaning, and generates appropriate responses.

[1546] A "generative AI model" is a type of artificial intelligence technology used to generate appropriate responses to user questions or requests.

[1547] "Physical condition management information" is data provided by a user regarding their own health condition.

[1548] A "database" is a system for storing and managing health management information and accumulated user data over a long period of time.

[1549] "Health advice" refers to suggestions and advice for improving and maintaining the user's health based on health management information.

[1550] "Medicine suggestions" are suggestions for recommending appropriate medicines based on the user's physical condition data.

[1551] "Information provision" is the process of transmitting the user's physical condition data and medication suggestions to doctors and other relevant parties.

[1552] "Data acquisition" is the process of obtaining the required information or data from an external source, such as an API.

[1553] A "trend or pattern" is a consistent change or specific behavior observed in a user's past physical condition data.

[1554] The present invention is a seamless system for efficiently meeting user needs through voice input. The system mainly includes five main functions: voice input, dialogue function, health management, health advice, and medicine suggestion and prescription.

[1555] 1. Voice Input

[1556] The device captures the user's voice using a built-in or external microphone, such as a smartphone or smart speaker. The captured voice data is compressed, encoded, and sent to a server as a data stream.

[1557] The server analyzes the received voice data and converts it into text using speech recognition algorithms such as Google Cloud Speech-to-Text or Amazon Transcribe, then sends the results back to the device, allowing the user to confirm that their voice input was handled correctly.

[1558] Examples:

[1559] User: "What's the weather like today?"

[1560] Example prompt sentence:

[1561] "The user has spoken to ask about today's weather. The server should convert this into text using the speech recognition API, obtain the weather information, and respond."

[1562] 2. Interactive features

[1563] When a user asks a question or wants to have a conversation, they say, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server uses a generative AI model (e.g., GPT-3.5) to analyze the intent of the question and retrieves the necessary data from appropriate sources. For example, based on information obtained from a weather information API, it generates an appropriate response and replies to the user via voice via the device.

[1564] Examples:

[1565] User: "What's the weather like today?"

[1566] Example prompt sentence:

[1567] "The user has asked about today's weather. Please provide an easy-to-understand answer based on the weather information you have obtained."

[1568] 3. Health Management

[1569] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and sends it to the server. The server analyzes the report, extracts important key information (e.g., "headache"), and stores it in a database. Based on the analysis, the server determines whether immediate action is required, and if necessary, generates basic health advice and provides it to the user via the device.

[1570] Examples:

[1571] User: "I have a bit of a headache this morning."

[1572] Example prompt sentence:

[1573] "A user reports how they felt this morning. Extract the information that they had a headache, store it in a database, and provide basic health advice based on that."

[1574] 4. Health Advice

[1575] The server analyzes the user's accumulated physical condition data and extracts trends and patterns. Based on this, it automatically generates personalized health advice for the user and provides it via voice through the device. For example, if the user has not been exercising much recently, the server will provide advice such as "It is recommended that you exercise a little more."

[1576] Examples:

[1577] Server: "Looking at your recent health data, it seems like you're not getting enough exercise. I'd recommend you get a little more exercise."

[1578] Example prompt sentence:

[1579] "Based on the user's accumulated physical condition data, there is a tendency for lack of exercise. Based on that information, please provide appropriate health advice to the user."

[1580] 5. Medication suggestions and prescriptions

[1581] If a user asks, "I have a persistent headache. What should I do?", the device captures this speech, converts it into text, and sends it to the server. The server analyzes the user's physical condition data, referencing past data and current condition as necessary. The server generates a preliminary medication suggestion based on this data and sends it to an online platform to provide to a doctor. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. In some cases, the option to order and collect medication online is also provided.

[1582] Examples:

[1583] User: "I'm having a persistent headache. What should I do?"

[1584] Example prompt sentence:

[1585] "The user has reported a persistent headache and requested action. Please refer to their past physical condition data, generate a preliminary medication suggestion, and send it to the doctor for review. After the doctor's review, generate a formal prescription and notify the user."

[1586] In this way, the present invention is a system that seamlessly executes a series of processes, starting with the user's voice input, including dialogue functions, physical condition management, health advice, and drug suggestions and prescriptions. This allows users to use many functions using only their voice, providing convenience especially for elderly people and users who are unfamiliar with technology.

[1587] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1588] Voice input

[1589] Step 1:

[1590] The user asks a question by voice, such as "What's the weather like today?" This voice becomes the input.

[1591] Step 2:

[1592] The device captures the user's voice using a built-in or external microphone, compresses and encodes the captured voice data as input, and outputs the encoded voice data.

[1593] Step 3:

[1594] The terminal transmits the encoded audio data to the server as a data stream, and the transmitted audio data becomes the input.

[1595] Step 4:

[1596] The server analyzes the received voice data using speech recognition algorithms such as Google Cloud Speech-to-Text and Amazon Transcribe. The voice data is converted into text and the text data is output.

[1597] Step 5:

[1598] The server returns the converted string to the terminal, and the returned string data becomes the input.

[1599] Step 6:

[1600] The device will then display the received text data to the user or provide audio feedback, allowing the user to confirm that their voice input was handled correctly.

[1601] Interactive features

[1602] Step 1:

[1603] The user asks aloud, "What's the weather like today?" This speech becomes the input.

[1604] Step 2:

[1605] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[1606] Step 3:

[1607] The server uses a generative AI model (e.g., GPT-3.5) to analyze the intent of the question. The string data is input, and the intent, such as "I want to know the weather information," is extracted.

[1608] Step 4:

[1609] The server retrieves the necessary data from a weather information API (e.g., OpenWeatherMap) based on the extracted intent. This data is input, and weather information is output.

[1610] Step 5:

[1611] The server generates an appropriate response message based on the weather information it has acquired. The response message is then output.

[1612] Step 6:

[1613] The server sends the generated response text to the terminal, which then becomes the input.

[1614] Step 7:

[1615] The terminal responds to the user by voice with a response message, providing the user with an easy-to-understand voice output.

[1616] Health management

[1617] Step 1:

[1618] The user reports by voice, "I have a slight headache this morning." This voice becomes the input.

[1619] Step 2:

[1620] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[1621] Step 3:

[1622] The server analyzes the speech recognition data and extracts important key information (e.g., "headache"). The input is text data, and the extracted key information is output.

[1623] Step 4:

[1624] The server stores the analysis results in a database, and the stored data becomes the input.

[1625] Step 5:

[1626] The server determines whether immediate action is required and generates basic health advice such as "drink lots of water and get plenty of rest." The health advice is output.

[1627] Step 6:

[1628] The server transmits the generated advice to the terminal, and the terminal provides the information to the user by voice. Voice advice is output.

[1629] Health Advice

[1630] Step 1:

[1631] The server analyzes the user's accumulated health data and extracts trends and patterns. The accumulated data is input, and trends and patterns are output.

[1632] Step 2:

[1633] Based on the analysis results, the server automatically generates individual health advice, such as "We recommend you exercise a little more." The health advice is output.

[1634] Step 3:

[1635] The server transmits the generated advice to the terminal, which then provides it to the user by voice. The voice advice is output.

[1636] Medication suggestions and prescriptions

[1637] Step 1:

[1638] The user asks a question by voice, "I have a persistent headache. What should I do?" This voice becomes the input.

[1639] Step 2:

[1640] The device captures the audio, converts it into a string, and sends it to the server. The string data sent becomes the input.

[1641] Step 3:

[1642] The server analyzes the user's physical condition data, referencing past data and current status as necessary. Physical condition data is input, and the analysis results are output.

[1643] Step 4:

[1644] The server generates preliminary medication suggestions based on the analysis results. The medication suggestions are output.

[1645] Step 5:

[1646] The server then sends the generated data to an online platform that provides preliminary drug recommendations to physicians.

[1647] Step 6:

[1648] After the doctor checks the data, a formal prescription is generated. The prescription is output.

[1649] Step 7:

[1650] The server sends the prescription to the terminal and notifies the user, who can then select the option to order and collect the medication online as needed.

[1651] (Application example 1)

[1652] 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."

[1653] Although there are systems that allow elderly people and those unfamiliar with technology to receive health management and health advice through voice input, these systems are limited to individual functions and lack multifaceted support. Furthermore, they lack functions for suggesting meals related to health and easy ordering, so comprehensive support is needed to help elderly people stay safe and healthy.

[1654] 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.

[1655] In this invention, the server includes means for capturing a user's voice through voice input, means for converting the captured voice data into a character string, means for analyzing the converted character string and generating an appropriate response, means for audibly communicating the generated response to the user, means for receiving health management information from the user, analyzing it, and storing it in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a doctor as needed based on the user's health data, and means for suggesting and ordering appropriate meals based on the user's health data and voice input. This enables a user to perform comprehensive health management using only voice input, and to suggest and order meals based on their physical condition.

[1656] "Voice input" is the means by which the user's voice is captured and transmitted to the system.

[1657] "Voice data" refers to data that stores captured user voice information in digital form.

[1658] The "means for converting into character string" is a process for analyzing the audio data and converting it into corresponding text data.

[1659] The "means for generating an appropriate response" is the process of creating a response to the user's question or request based on the parsed string.

[1660] "Physical condition management information" is information relating to the user's health condition and symptoms.

[1661] "Physical condition data" refers to measurement and report data relating to the user's health condition and physical condition.

[1662] "Health advice" is advice about health provided based on the user's physical condition data.

[1663] "Medicine suggestion" is the act of recommending appropriate medicine based on the user's physical condition data.

[1664] "Providing information to a doctor" refers to the act of transmitting the user's physical condition data to a doctor and providing information for appropriate treatment and prescription.

[1665] "Meal suggestion" is the act of recommending an appropriate meal menu based on the user's physical condition data and voice input.

[1666] "Meal Ordering" is the process of ordering suggested meals online.

[1667] A "natural language processing engine" is software for understanding and analyzing natural language.

[1668] The configuration and operation of a system for implementing the present invention will be described in detail below.

[1669] 1. System Configuration

[1670] The system mainly consists of the following components:

[1671] Voice input means: Devices with built-in microphones, such as smartphones, smart glasses, and head-mounted displays.

[1672] String conversion method: Software that converts voice data into strings using the Google Cloud Speech-to-Text API.

[1673] Natural language processing means: OpenAI's GPT-4 model is used to analyze the converted text and understand the user's intent.

[1674] Database: A database that uses Firebase to store and manage user physical condition data and voice data.

[1675] Food Delivery API: An API for suggesting and ordering meals using the Uber Eats API.

[1676] TTS Engine: A Text-to-Speech engine is used to generate responses that are then spoken to the user.

[1677] 2. Program Processing Overview

[1678] Voice Input and Recognition

[1679] When a user speaks a question or request, the device's built-in microphone captures the audio data, which is then sent to a server and converted into text using the Google Cloud Speech-to-Text API.

[1680] Natural Language Processing

[1681] The server receives the converted voice data and uses the OpenAI GPT-4 model to analyze the intent of the user's question, such as "What is your recommended meal today?", and generates appropriate meal suggestions.

[1682] Database Reference

[1683] The server accesses the Firebase database and references the user's physical condition data and allergy information, which allows it to provide health advice and dietary suggestions tailored to each individual user.

[1684] Food delivery API integration

[1685] Based on the analysis results and the user's physical condition data, the app will use the Uber Eats API to suggest appropriate meal options. For example, it will suggest specific menu items and provide a function to allow the user to proceed directly to the ordering process.

[1686] Response generation and communication

[1687] The server converts the generated responses and suggestions into speech using a TTS engine and transmits them to the user via the terminal.

[1688] 3. Examples of concrete examples and prompts

[1689] User: "What's your lunch recommendation today?"

[1690] Server side:

[1691] The app checks the user's physical condition data and suggests a "nutritious chicken salad" based on their recent lack of exercise.

[1692] Generates a voice response saying, "Uber Eats has a fresh chicken salad on the menu. Would you like to order it?"

[1693] Example prompt sentence:

[1694] User: "What's your recommendation today?"

[1695] Input to the AI ​​model:

[1696] Consider the user's profile and health data to suggest today's meal recommendations, including taking into account the user's allergies and dietary restrictions.

[1697] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1698] Step 1:

[1699] Voice input

[1700] Input: The user speaks a question or request (e.g., "What's the recommendation today?").

[1701] Processing: The device's microphone captures the user's voice and obtains the voice data.

[1702] Output: The acquired audio data is sent to the server as is.

[1703] Step 2:

[1704] Voice Recognition

[1705] Input: Captured audio data.

[1706] Processing: The server uses the Google Cloud Speech-to-Text API to convert the audio data into text. Specifically, a speech recognition engine analyzes the audio signal and generates corresponding text.

[1707] Output: Text data (e.g., "What's your recommendation today?").

[1708] Step 3:

[1709] Natural Language Processing

[1710] Input: Text data.

[1711] Processing: The server passes the text data to OpenAI's GPT-4 model, which analyzes the intent of the user's question or request. Specifically, the natural language processing engine understands the meaning of the sentence to generate appropriate meal suggestions and responses based on the input text.

[1712] Output: Analysis results (user intent and specific requirements).

[1713] Step 4:

[1714] Database Reference

[1715] Input: Analysis results and user ID.

[1716] Processing: The server accesses the Firebase database to retrieve the user's physical condition data and allergy information. Specifically, it queries the database using the user ID as a key to retrieve related health information.

[1717] Output: User's health data and allergy information.

[1718] Step 5:

[1719] Meal suggestions

[1720] Input: Analysis results, physical condition data, and allergy information.

[1721] Processing: The server uses the Uber Eats API to suggest appropriate meal options based on the analysis results and the user's health data. Specifically, it filters the meal options and selects the most suitable menu for the user.

[1722] Output: A suggested meal (e.g., "chicken salad").

[1723] Step 6:

[1724] Response Generation

[1725] Input: A suggested meal menu.

[1726] Processing: The server generates a response text (e.g., "Uber Eats has fresh chicken salad on the menu. Would you like to order it?"). Specifically, it uses a natural language generation engine to generate a message to be conveyed to the user.

[1727] Output: The response text.

[1728] Step 7:

[1729] Voice response

[1730] Input: Response text.

[1731] Processing: The server uses a text-to-speech engine to convert the response text into voice data, specifically outputting the generated text as a voice signal and sending it to the terminal.

[1732] Output: Audio data.

[1733] Step 8:

[1734] Communication to users

[1735] Input: Audio data.

[1736] Processing: The device plays the audio data and gives the response to the user by voice. Specifically, the device's speaker outputs the audio data so that the user can hear it.

[1737] Output: A spoken response to the user.

[1738] 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.

[1739] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions, as well as an emotion recognition engine that recognizes the user's emotions. The detailed configuration and operation of this system are described below.

[1740] 1. Voice Input and Emotion Recognition

[1741] The device captures the user's voice using a built-in or external microphone. The captured voice data is compressed, encoded, and sent as a data stream to the server. The server analyzes the voice data and converts it into text using a voice recognition algorithm, while simultaneously analyzing the user's emotions using an emotion recognition engine. This emotion recognition allows the system to distinguish emotions extracted from the user's voice (e.g., joy, sadness, anger, etc.) and respond accordingly.

[1742] 2. Interactivity and emotional response

[1743] When a user asks a question or wants to have a conversation, they might say something like, "What's the weather like today?" The device captures this speech, converts it into text, and then sends it to the server. The server then uses a natural language processing engine to analyze the intent of the question and retrieves data from the necessary sources (e.g., weather information API). Based on the retrieved information, the server generates a response, taking into account the results of the emotion recognition engine. For example, if the user asks a question in a sad voice, the server can add a touch of kindness or encouragement to the response. The generated response is sent to the device and replies to the user via voice.

[1744] 3. Physical and emotional health management

[1745] The user reports their physical condition by voice input, such as "I have a slight headache this morning." The device captures the voice, converts it into text, and then sends it to the server. The server analyzes the report, extracts important keywords, and stores them in a database along with emotion recognition results. The server takes the user's emotional state into account when generating advice based on the analysis results. For example, if the user is feeling stressed, it can add advice on how to relax. The advice is provided to the user via voice via the device.

[1746] 4. Health advice and emotional support

[1747] The server analyzes the user's accumulated physical and emotional data to extract trends and patterns. Based on this, it automatically generates advice and provides it via voice through the device. For example, if a user has been feeling down recently, it can provide positive advice such as "Try taking a walk or doing some light exercise."

[1748] 5. Medication suggestions and prescriptions

[1749] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to the server. The server analyzes the user's physical condition and emotional data and sends the information to a doctor via an online platform. After the doctor reviews the data, a formal prescription is generated and notified to the user via the device. At the same time, appropriate support is provided based on the emotion recognition results (e.g., a reassuring message).

[1750] In this way, the present invention is a system that provides more user-friendly and effective support by combining an emotion recognition engine with a series of processes that start with voice input and include dialogue functions, physical condition management, health advice, and medication suggestions and prescriptions.

[1751] The processing flow will be explained below.

[1752] Processing steps for voice input and emotion recognition functions

[1753] Step 1:

[1754] The terminal captures the user's voice through the microphone, and at this time, the terminal starts the operation of the voice input device and captures the voice signal as digital data.

[1755] Step 2:

[1756] The device compresses the captured audio data and processes it for noise reduction, a process that conserves audio data and improves quality.

[1757] Step 3:

[1758] The device sends the processed audio data to the server using the appropriate communication protocol (e.g. HTTP / HTTPS).

[1759] Step 4:

[1760] The server analyzes the received voice data and applies a speech recognition algorithm, which converts the voice data into text data.

[1761] Step 5:

[1762] In parallel with the voice recognition, the server uses an emotion recognition engine to determine the user's emotions from the voice data. For example, it analyzes the intensity and rhythm of the sound to estimate emotions such as joy, sadness, and anger.

[1763] Step 6:

[1764] The server returns the converted text data and the determined emotion data to the terminal.

[1765] Step 7:

[1766] The terminal displays the received text data and emotion data to the user and asks for confirmation of the input contents.

[1767] Interactivity and emotional response processing steps

[1768] Step 1:

[1769] The user uses voice input to ask a question, saying "What's the weather like today?"

[1770] Step 2:

[1771] The device captures the audio and sends the data to a server, just like the voice input and emotion recognition functions mentioned earlier.

[1772] Step 3:

[1773] The server analyzes the received text data using a natural language processing engine to understand the intent of the question.

[1774] Step 4:

[1775] The server accesses an external data source (e.g., a weather information API) to obtain the required information.

[1776] Step 5:

[1777] The server generates a response based on the acquired information, taking into account the results of the emotion recognition engine, for example, generating a gentle tone of response if the user is depressed.

[1778] Step 6:

[1779] The server transmits the generated response sentence and tone information based on the emotion to the terminal.

[1780] Step 7:

[1781] The terminal responds to the user with the received response sentence in a voice tone that corresponds to the emotion.

[1782] Physical condition management and emotional consideration processing steps

[1783] Step 1:

[1784] The user reports their physical condition by voice, saying, "I have a slight headache this morning."

[1785] Step 2:

[1786] The terminal captures the voice, converts it into a string of characters, and then transmits the voice data and emotion data to the server.

[1787] Step 3:

[1788] The server analyzes the received health report, extracts important keywords, and determines the appropriate response based on the emotion recognition results.

[1789] Step 4:

[1790] The server stores the analysis results and emotion recognition results in a health management database.

[1791] Step 5:

[1792] The server generates advice based on the reported physical condition and emotions. For example, if the user shows signs of anxiety, it adds advice such as "relax and take a deep breath."

[1793] Step 6:

[1794] The server transmits the generated advice to the terminal.

[1795] Step 7:

[1796] The terminal conveys the received advice to the user by voice.

[1797] Medication suggestion and prescription function processing steps

[1798] Step 1:

[1799] The user then voice-inputs a follow-up question about their condition, saying, "I'm having a persistent headache. What should I do?"

[1800] Step 2:

[1801] The device captures the audio, converts it into a string of characters, and sends it to the server.

[1802] Step 3:

[1803] The server analyzes the received character string data and emotion data and compares them with the user's physical condition data.

[1804] Step 4:

[1805] The server transmits the information through an online platform to provide the data to a physician as needed.

[1806] Step 5:

[1807] The doctor reviews the data and generates the necessary prescriptions.

[1808] Step 6:

[1809] The server receives the prescription generated by the doctor and provides the user with medication suggestions, taking into account their emotions. For example, if the user shows anxiety, the server adds a message saying, "Don't worry, we'll prepare your medication for you."

[1810] Step 7:

[1811] The terminal will notify the user that the prescription is ready and advise them on next steps (such as traveling to a pharmacy or online collection).

[1812] Example 2

[1813] 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."

[1814] Conventional voice input systems can convert the user's speech into text and provide information, but they have difficulty recognizing the user's emotional state and responding appropriately. In particular, there are limitations to providing appropriate advice and medication suggestions to users who are in poor health or experiencing psychological stress, and more user-friendly and effective support is needed.

[1815] 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.

[1816] In this invention, the server includes means for converting voice data into character strings, means for analyzing the converted character strings and voice data to recognize the user's emotions, and means for generating an appropriate response based on the emotion recognition result, thereby making it possible to provide responses and advice that take the user's emotions into consideration.

[1817] "Voice input" is a means of capturing the user's voice through a microphone.

[1818] "Captured audio data" refers to audio captured by a microphone and converted into digital form.

[1819] "Means for converting into character string" refers to a technology that analyzes audio data and converts it into text format.

[1820] The "analyzing means" is a process for processing the converted text and audio data to identify its content and sentiment.

[1821] "Means for recognizing emotions" refers to technology for determining a user's emotional state from voice or text.

[1822] A "means for generating a response" is an algorithm or engine for generating an appropriate response based on the analysis results.

[1823] "Physical condition management information" is information relating to the physical condition and mood reported by the user.

[1824] The "means of storing in a database" is a system that organizes and safely stores collected health management information and emotional data.

[1825] The "means for providing health advice" is a technology that generates and provides appropriate health-related advice based on the user's physical condition and emotional data.

[1826] "Professional information provision" is the process of using collected data to provide appropriate information to physicians and other professionals.

[1827] The present invention is a system that combines voice input, dialogue functions, physical condition management, health advice, and medication suggestions with an emotion recognition engine that recognizes the user's emotions. This system is implemented using a specific hardware and software configuration. The detailed configuration and operation of the system are described below.

[1828] Voice Input and Emotion Recognition

[1829] The device captures the user's voice using a built-in or external microphone. The captured voice data is converted into a digital format, compressed, and encoded. This data is then sent as a data stream to the server. The server uses a speech recognition algorithm (e.g., Google Speech-to-Text API) to analyze the voice data and convert it into a string of characters. It also uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to analyze and identify the user's emotional state. For example, if the user says "I have a headache," the system will recognize the emotion "sad" from the voice.

[1830] Interactivity and emotional response

[1831] When a user says, "What's the weather like today?", the device captures this speech, converts it into text, and sends it to the server. The server then uses a natural language processing engine (e.g., OpenAI's GPT-3) to analyze the intent of the question. It then retrieves the necessary information from a weather information API database (e.g., OpenWeatherMap) and generates a response based on that information. It also takes into account the results of the emotion recognition engine, and if the user asks sadly, it creates a gentle response such as, "It's sunny today. I hope you have a nice day." The generated response is sent to the device, where it is replied to the user audibly using speech synthesis technology.

[1832] Physical condition management and emotional consideration

[1833] When a user reports their physical condition by saying, "I have a slight headache this morning," the device captures this voice, converts it into text, and sends it to the server. The server analyzes the report and extracts keywords such as "headache." It also stores the emotion recognition results in a database. Next, it creates advice that takes into account the user's stress level and emotional state. For example, it might generate advice such as, "I have a headache, so try drinking some tea and relaxing." This advice is provided to the user via the device.

[1834] Health advice and emotional support

[1835] The server analyzes the user's accumulated physical and emotional data to identify specific trends and patterns. Based on this data, it automatically generates health advice and provides it to the user via voice via the device. For example, if it is known that the user has recently been prone to stress, the server may provide advice such as "Try going for a walk to relax."

[1836] Medication suggestions and prescriptions

[1837] When a user asks, "I have a persistent headache. What should I do?", the device captures the voice, converts it into text, and sends it to a server. The server analyzes the user's physical condition and emotional data and sends it to a specialist via an online platform (e.g., a medical information system). The specialist reviews this data and generates an official prescription. This information is returned to the server and notified to the user via the device. At the same time, a message to ease the user's anxiety (e.g., "Take some medicine and you'll feel better") is also provided.

[1838] Through these processes, the system provides comprehensive and emotionally sensitive support to the user.Examples of prompts for the generative AI model include, "I've been having a bad headache lately. What should I do?" and "What's the weather like today?"

[1839] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1840] Step 1:

[1841] The user makes a verbal request. For example, "What's the weather like today?" The input is the user's voice. The device's microphone captures this voice.

[1842] Step 2:

[1843] The captured audio data is converted to a digital format on the device, which then compresses and encodes the converted audio data and sends it as a data stream to the server. The input is digital audio data, and the output is compressed and encoded audio data.

[1844] Step 3:

[1845] The server analyzes the received audio data and converts it into a text string using a speech recognition algorithm, using the Google Speech-to-Text API. The input is compressed and encoded audio data, and the output is a text string.

[1846] Step 4:

[1847] The server analyzes the converted text string and voice data with an emotion recognition engine to identify the user's emotional state. For example, it uses IBM Watson Tone Analyzer. The input is the text string and voice data, and the output is the emotion analysis result (e.g., the user has the emotion "sad").

[1848] Step 5:

[1849] Based on the emotional state and the text string, the server uses a natural language processing engine to generate a response. OpenAI's GPT-3 can be used here. The input is the emotion analysis result and the text string, and the output is the generated response. For example, a response such as "It's sunny today. Cheer up!" is generated.

[1850] Step 6:

[1851] The generated response is sent from the server to the device, which then uses speech synthesis technology to convert the text response into speech. The input is a text response, and the output is a speech response.

[1852] Step 7:

[1853] The device then transmits the synthesized response to the user through the speaker. The final output is a voice response that the user hears. For example, "It's a sunny day today. Cheer up." The dialogue is completed.

[1854] Step 8:

[1855] When a user reports their physical condition (e.g., "I have a slight headache this morning"), their voice is captured and the data is sent to the server. This data is analyzed, and the physical condition data and emotion data are stored in a database. The input is the voice data about their physical condition, and the output is the analyzed physical condition and emotion data.

[1856] Step 9:

[1857] The server generates health advice based on the accumulated physical condition data and emotional data, and provides it to the user via audio via the terminal. The input is the accumulated data, and the output is audio advice. For example, advice such as "Drink tea to relax" is provided.

[1858] Step 10:

[1859] When a user requests a medication suggestion (e.g., "I have a persistent headache. What should I do?"), the device captures the voice and sends it to the server. The server analyzes the user's physical condition and emotional data and provides the information to the doctor through the online platform. Finally, the doctor's prescription information is returned to the device via the server and notified to the user. The input is the user's question and physical condition data, and the output is the prescription information and a message to soothe the emotions.

[1860] (Application example 2)

[1861] 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."

[1862] Conventional food delivery applications do not suggest meals that take into account the user's physical condition or emotional state, making it difficult to provide services that are appropriate for each user's individual health and psychological state. Furthermore, there is a lack of systems that combine voice input and emotion recognition, and no method is provided for users to easily report their physical and emotional state and receive appropriate support based on that information. This has led to the issue of low user satisfaction.

[1863] The specific processing by the specific 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 means for capturing the user's voice through voice input, means for converting the captured voice data into character strings, means for analyzing the converted character strings and generating an appropriate response, means for audibly conveying the generated response to the user, means for receiving, analyzing, and storing health management information from the user in a database, means for providing health advice based on the health data, means for suggesting medication and providing information to a specialist as needed based on the user's health data, means for analyzing the user's emotional state using an emotion recognition engine, means for adjusting responses and advice based on the analyzed emotional state, and means for making individual dietary suggestions based on the user's physical condition and emotional state. This enables personalized dietary suggestions and responses based on the user's physical condition and emotional state.

[1864] "Voice input" is the ability to capture a user's voice and obtain it as digital data for processing.

[1865] "Captured audio data" refers to audio information obtained through audio input and stored and processed as digital data.

[1866] "Means for converting into text" refers to the process or technology that analyzes audio data and converts it into corresponding text.

[1867] The "converted character string" refers to data resulting from converting voice data into character information.

[1868] The "means for generating an appropriate response" is a mechanism for generating an appropriate reply or information for the user based on the parsed string.

[1869] "Physical condition management information" is information about the health condition provided by the user, and is used as the basis for analyzing and managing the physical condition.

[1870] "Physical condition data" refers to data relating to the user's current or past health condition.

[1871] "Means for providing health advice" refers to a function that analyzes the user's physical condition data and provides health advice and suggestions based on the results.

[1872] "Medication suggestion" is the process of recommending appropriate medications based on the user's health condition.

[1873] The "means for providing information to experts" is a mechanism for collecting information about the user's health condition and providing it to experts such as doctors and pharmacists.

[1874] An "emotion recognition engine" is an algorithm or technology that analyzes a user's voice data and identifies their emotional state.

[1875] "Emotional state" refers to the emotional state that the user is currently feeling, and includes emotions such as joy, sadness, and anger.

[1876] A "means for tailoring responses or advice" is a technique or process for tailoring the content of generated responses or health advice based on the user's emotional state.

[1877] The "means for making meal suggestions" is a function that suggests an appropriate meal menu according to the user's physical condition and emotional state.

[1878] This invention is a system that combines voice input, emotion recognition, physical condition management, health advice, and meal suggestions. The configuration and operation of this system will be described below.

[1879] System configuration

[1880] This system mainly consists of the following hardware and software:

[1881] Hardware: Smartphone (with built-in microphone and speaker)

[1882] Software: Speech recognition engine, emotion recognition engine, recommender system, natural language processing engine, cloud database

[1883] System Operation

[1884] 1. Audio capture and transmission:

[1885] The device (smartphone) captures the user's voice input. The captured voice data is temporarily stored on the device. This voice data is compressed and sent to a cloud server as digital data.

[1886] 2. Speech and Emotion Recognition:

[1887] The server converts the received voice data into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text). At the same time, it analyzes the user's emotional state using an emotion recognition engine (e.g., Azure Cognitive Services Emotion API). The analyzed emotional state is output as a classification result, such as joy, sadness, or anger.

[1888] 3. Interaction processing and response generation:

[1889] The server stores the text data and emotion recognition results in a cloud database. It uses a natural language processing engine (e.g., GPT-4) to understand the user's intent from the analyzed text and generate an appropriate response. The response takes into account the emotion recognition results and adds elements of kindness and encouragement to the response. The generated response is returned to the device as audio and provided to the user.

[1890] 4. Physical fitness and health advice:

[1891] The server receives voice input from the user reporting their physical condition (e.g., "I have a slight headache today"). The server stores the physical condition data in a database and analyzes it to generate health advice. If the user is feeling stressed, it can also add advice on how to relax. Health advice based on the analysis results is provided to the user via the terminal as voice.

[1892] 5. Meal Suggestions:

[1893] It makes personalized meal suggestions based on the user's physical and emotional state. It uses a recommender system to select the meal menu that is best suited to the user's health condition. For example, if a user inputs, "I've been feeling stressed and tired lately," it will suggest meals that will help relieve fatigue. These suggestions are provided to the user via voice and text.

[1894] Specific examples

[1895] For example, if a user says, "I've been feeling stressed and tired lately," the process is as follows:

[1896] Example prompt sentence:

[1897] A user said, "I've been feeling stressed and tired lately." Please create an encouraging message and provide optimal meal suggestions for this user.

[1898] In response to this prompt, the system produces the following output:

[1899] Suggested menu: "Grilled Salmon Set Meal - A nutritionally balanced meal that will help you recover from fatigue."

[1900] Special message: "You seem a little tired today. Eat a balanced meal and relax. Take care!"

[1901] In this way, personalized meal suggestions and responses based on the user's physical condition and emotions are provided, which is expected to improve user satisfaction.

[1902] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1903] Step 1:

[1904] The device accepts voice input. The user speaks about their physical condition and emotions, and the device's microphone captures the voice. The captured voice data is saved as digital data. The input in this step is the user's voice, and the output is digital voice data.

[1905] Step 2:

[1906] The device sends the captured audio data to the cloud server, which then compresses the audio data and transfers it to the cloud server using data streaming technology. The input of this step is digital audio data, and the output is the audio data sent to the cloud server.

[1907] Step 3:

[1908] The server converts the received voice data into text using a speech recognition engine. The server then converts the voice data into text using a speech recognition engine such as Google Cloud Speech-to-Text. The input for this step is the voice data, and the output is the converted text.

[1909] Step 4:

[1910] The server inputs the converted string into an emotion recognition engine to analyze the user's emotional state. It uses the Azure Cognitive Services Emotion API to identify the emotional state (e.g., joy, sadness, anger, etc.). The input of this step is the converted string, and the output is the emotion recognition result.

[1911] Step 5:

[1912] The server saves the string data and emotion recognition results in a cloud database. It uses a database such as MySQL or MongoDB to save the analysis results. The input of this step is the string data and emotion recognition results, and the output is the analysis results saved in the database.

[1913] Step 6:

[1914] The server uses a natural language processing engine to analyze the user's intent and generate an appropriate response. A generative AI model such as GPT-4 is used to understand the user's intent from the input string and generate a response. The input for this step is string data, and the output is the generated response.

[1915] Step 7:

[1916] The server adjusts the response sentence taking into account the emotion recognition results. For example, if the user shows sadness, it adds elements of kindness or encouragement. The input of this step is the generated response sentence and the emotion recognition results, and the output is the adjusted response sentence.

[1917] Step 8:

[1918] The server converts the adjusted response sentence into speech and sends it to the terminal. A speech synthesis engine is used to convert the text into speech data and send that data to the terminal. The input of this step is the adjusted response sentence, and the output is the speech data sent to the terminal.

[1919] Step 9:

[1920] The terminal plays the received voice data and conveys the response to the user. The voice data is played using the terminal's speaker. The input of this step is the voice data, and the output is the voice response conveyed to the user.

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] 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.

[1927] 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).

[1928] 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.

[1929] 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."

[1930] 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.

[1931] 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).

[1932] 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.

[1933] 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.

[1934] 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.

[1935] 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.

[1936] 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.

[1937] 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.

[1938] 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.

[1939] 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.

[1940] 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.

[1941] 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.

[1942] The following is further disclosed regarding the above embodiment.

[1943] (Claim 1)

[1944] means for capturing a user's voice through voice input;

[1945] a means for converting the captured audio data into a string;

[1946] a means for parsing the converted string and generating an appropriate response;

[1947] means for audibly communicating the generated response to the user;

[1948] A means for receiving health management information from a user, analyzing the information, and storing the information in a database;

[1949] A means for providing health advice based on physical condition data;

[1950] A means for suggesting medicines and providing information to doctors as needed based on the user's physical condition data;

[1951] A system including:

[1952] (Claim 2)

[1953] 10. The system of claim 1, wherein the means for analyzing the voice data uses a natural language processing engine.

[1954] (Claim 3)

[1955] 2. The system according to claim 1, wherein the means for generating health advice based on said physical condition management information provides advice by referring to the user's past physical condition data.

[1956] "Example 1"

[1957] (Claim 1)

[1958] means for capturing a user's voice through voice input;

[1959] a means for converting the captured audio data into a string;

[1960] a means for parsing the converted string and generating an appropriate response;

[1961] means for audibly communicating the generated response to the user;

[1962] A means for receiving health management information from a user, analyzing the information, and storing the information in a database;

[1963] A means for providing health advice based on physical condition data;

[1964] A means for suggesting medicines and providing information to doctors as needed based on the user's physical condition data;

[1965] means for capturing audio input at the device, compressing and encoding the audio input, and transmitting the audio input to a server;

[1966] A means for converting the voice data into a string of characters using a voice recognition algorithm on the server and returning the result to the terminal;

[1967] A means of analyzing the intent of a user's question using a generative AI model and retrieving data from appropriate sources;

[1968] A means for analyzing the user's accumulated physical condition data and extracting trends and patterns;

[1969] A system including:

[1970] (Claim 2)

[1971] 10. The system of claim 1, wherein the means for analyzing the speech data uses a natural language processing engine and a generative AI model.

[1972] (Claim 3)

[1973] 2. The system according to claim 1, wherein the means for generating health advice based on said physical condition management information refers to the user's past physical condition data and provides advice based on trends and patterns.

[1974] "Application Example 1"

[1975] (Claim 1)

[1976] means for capturing a user's voice through voice input;

[1977] a means for converting the captured audio data into a string;

[1978] a means for parsing the converted string and generating an appropriate response;

[1979] means for audibly communicating the generated response to the user;

[1980] A means for receiving health management information from a user, analyzing the information, and storing the information in a database;

[1981] A means for providing health advice based on physical condition data;

[1982] A means for suggesting medicines and providing information to doctors as needed based on the user's physical condition data;

[1983] a means for making appropriate meal suggestions and ordering meals based on the user's physical condition data and voice input;

[1984] A system including:

[1985] (Claim 2)

[1986] 10. The system of claim 1, wherein the means for analyzing the voice data uses a natural language processing engine.

[1987] (Claim 3)

[1988] 2. The system according to claim 1, wherein the means for generating health advice and dietary suggestions based on said physical condition management information provides advice by referring to the user's past physical condition data.

[1989] "Example 2: Combining Emotion Engines"

[1990] (Claim 1)

[1991] means for capturing a user's voice through voice input;

[1992] a means for converting the captured audio data into a string;

[1993] means for analyzing the converted character string and voice data and recognizing the user's emotion;

[1994] means for generating an appropriate response based on the emotion recognition result;

[1995] means for audibly communicating the generated response to the user;

[1996] A means for receiving health management information from a user, analyzing the information, and storing the information in a database;

[1997] means for providing health advice based on the physical condition data and the emotional data;

[1998] A means for suggesting medicine and providing information to specialists as needed based on the user's physical condition data and emotional data;

[1999] A system including:

[2000] (Claim 2)

[2001] 2. The system according to claim 1, wherein the analyzing means uses a natural language processing engine and an emotion recognition engine.

[2002] (Claim 3)

[2003] 2. The system according to claim 1, wherein the means for generating health advice based on the physical condition and emotion data provides advice by referring to past data of the user.

[2004] "Application example 2 when combining emotion engines"

[2005] (Claim 1)

[2006] means for capturing a user's voice through voice input;

[2007] a means for converting the captured audio data into a string;

[2008] a means for parsing the converted string and generating an appropriate response;

[2009] means for audibly communicating the generated response to the user;

[2010] A means for receiving health management information from a user, analyzing the information, and storing the information in a database;

[2011] A means for providing health advice based on physical condition data;

[2012] A means for suggesting medicines and providing information to specialists as needed based on the user's physical condition data;

[2013] means for analyzing the emotional state of a user using an emotion recognition engine;

[2014] means for tailoring responses or advice based on the analyzed emotional state;

[2015] A means for providing personalized dietary suggestions according to physical and emotional states;

[2016] A system including:

[2017] (Claim 2)

[2018] 10. The system of claim 1, wherein the means for analyzing the voice data uses a natural language processing engine.

[2019] (Claim 3)

[2020] 2. The system according to claim 1, wherein the means for generating health advice based on said physical condition management information provides advice by referring to the user's past physical condition data. [Explanation of symbols]

[2021] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for capturing a user's voice through voice input; a means for converting the captured audio data into a string; a means for parsing the converted string and generating an appropriate response; means for audibly communicating the generated response to the user; A means for receiving health management information from a user, analyzing the information, and storing the information in a database; A means for providing health advice based on physical condition data; A means for suggesting medicines and providing information to doctors as needed based on the user's physical condition data; A system including:

2. 2. The system of claim 1, wherein the means for analyzing the speech data uses a natural language processing engine.

3. 2. The system according to claim 1, wherein the means for generating health advice based on said physical condition management information provides advice by referring to the user's past physical condition data.

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