System

Abstract

An object of a system according to an embodiment is to enable an elderly person to easily access necessary information and services.SOLUTION: A system includes a voice input unit, an analysis unit, a guidance unit, and a connection unit. The voice input unit acquires voice. The analysis unit analyzes the voice acquired by the voice input unit. The guidance unit provides appropriate information based on the content analyzed by the analysis unit. The connection unit connects to a necessary service based on the information provided by the guide unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for elderly people to easily access the information and services they need.

[0005] The system according to the embodiment aims to enable elderly people to easily access necessary information and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, an analysis unit, a guidance unit, and a connection unit. The voice input unit acquires voice. The analysis unit analyzes the voice acquired by the voice input unit. The guidance unit provides appropriate information based on the content analyzed by the analysis unit. The connection unit connects to a required service based on the information provided by the guidance unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable elderly people to easily access necessary information and services. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A support app according to an embodiment of the present invention provides elderly people with information and services they need in their daily lives, improving their quality of life. This app combines generative AI and a speech-to-text tool. This allows elderly people to easily access information and services they need in their daily lives. For example, the support app can meet a variety of needs, such as providing guidance to appropriate hospitals, purchasing groceries, participating in events, conversation support, recording important information, and providing guidance on how to use PayPay. This improves the quality of life for elderly people, allowing them to live their daily lives with peace of mind.

[0029] A support app according to an embodiment includes a voice input unit, an analysis unit, a guidance unit, and a connection unit. The voice input unit acquires a user's voice. For example, the voice is acquired using a smartphone microphone. The voice input unit can also convert the voice into text data using voice recognition technology. For example, the voice input unit converts what the user says into text in real time. The analysis unit analyzes the voice acquired by the voice input unit. For example, the analysis unit converts the voice into text data using voice recognition technology and analyzes the text. The analysis unit can also understand the content of the voice and extract appropriate information using natural language processing technology. For example, if the user says, "My stomach hurts," the analysis unit analyzes the content and provides guidance to an appropriate hospital. The guidance unit provides appropriate information based on the content analyzed by the analysis unit. For example, the guidance unit provides guidance to the nearest hospital based on the user's symptoms. The guidance unit can also provide topics based on the user's interests. For example, if the user says, "Let's talk about recent news," the guidance unit provides the latest news. The connection unit connects to the necessary service based on the information provided by the guidance unit. For example, the connection unit automatically makes a call to the hospital that the user is guided to. The connection unit can also automatically order ingredients that the user wants to purchase from an online supermarket. For example, if the user says, "I want to eat curry," the connection unit orders the necessary ingredients from the online supermarket. In this way, the support app according to the embodiment can obtain information through voice from the elderly person and connect to the necessary service. For example, if the user says, "I have a stomachache," the app can guide the elderly to an appropriate hospital and automatically make a call. Also, if the user says, "I want to eat curry," the app can order the necessary ingredients from the online supermarket.

[0030] The analysis unit analyzes the tone and speed of the user's voice, determines the level of urgency, and can recommend the most appropriate hospital. For example, when a user says, "I have chest pain," the generation AI analyzes the tone and speed of the voice, and if it determines that the level of urgency is high, it can recommend the nearest emergency hospital. Also, when a user says, "I have a headache," the generation AI analyzes the tone and speed of the voice, and if it determines that the level of urgency is low, it can also recommend a general medical hospital. Also, when a user says, "I have a stomachache," the generation AI analyzes the tone and speed of the voice, and if it determines that the level of urgency is medium, it can also recommend an internal medicine hospital. This makes it possible to recommend the appropriate hospital according to the level of urgency.

[0031] The analysis unit can ask questions to the user to more accurately grasp the symptoms. For example, when the user says "I have a stomachache," the generation AI will ask "Where does it hurt?" to grasp the symptoms in detail. Also, when the user says "I have a headache," the analysis unit can ask "How long has the pain been going on?" to grasp the progression of the symptoms. Also, when the user says "I have a chest pain," the generation AI can ask "How strong is the pain?" to grasp the degree of pain. This allows the analysis unit to grasp the user's symptoms more accurately.

[0032] The analysis unit can refer to the user's past health data to provide more accurate hospital guidance. For example, when a user says, "I have a stomachache," the generation AI can refer to the past health data and provide guidance to hospitals that the user previously visited for similar symptoms. In addition, when a user says, "I have a headache," the generation AI can refer to the past health data and provide guidance to an appropriate hospital based on information about previously prescribed medications. In addition, when a user says, "I have chest pain," the generation AI can refer to the past health data and provide guidance to a specialized hospital if the user has a history of heart problems. This enables more accurate hospital guidance based on past health data.

[0033] The analysis unit can provide preventive measures and health advice by taking into account the user's lifestyle habits and dietary history. For example, when the user says "I have a stomachache," the generation AI can refer to the dietary history and provide advice on easy-to-digest meals. Also, when the user says "I have a headache," the analysis unit can provide stress management advice by referring to the user's lifestyle habits. Also, when the user says "I have a chest pain," the generation AI can provide moderate exercise advice by referring to the user's exercise habits. This makes it possible to provide preventive measures and health advice based on the user's lifestyle habits and dietary history.

[0034] The guidance unit can refer to the user's past meal history and suggest recipes that take nutritional balance into consideration. For example, when the user says, "I want to eat curry," the generation AI can refer to the user's past meal history and suggest a curry recipe that takes nutritional balance into consideration. Also, when the user says, "I want to eat pasta," the guidance unit can refer to the user's past meal history and suggest a pasta recipe that takes nutritional balance into consideration. Also, when the user says, "I want to eat salad," the generation AI can refer to the user's past meal history and suggest a salad recipe that takes nutritional balance into consideration. This makes it possible to suggest recipes that take nutritional balance into consideration based on the user's past meal history.

[0035] The guidance unit can automatically generate recipes that accommodate allergies. For example, when a user says, "I want to eat curry," the generation AI in the guidance unit references the allergy information and suggests a curry recipe that accommodates the allergy. Also, when a user says, "I want to eat pasta," the generation AI can reference the allergy information and suggest a pasta recipe that accommodates the allergy. Also, when a user says, "I want to eat salad," the generation AI can reference the allergy information and suggest a salad recipe that accommodates the allergy. This makes it possible to automatically generate recipes that accommodate allergies.

[0036] The guidance unit can suggest recipes that take into consideration seasonal ingredients. For example, when the user says, "I want to eat curry," the generation AI takes into consideration seasonal ingredients and suggests a curry recipe using seasonal vegetables. Also, when the user says, "I want to eat pasta," the generation AI can take into consideration seasonal ingredients and suggest a pasta recipe using seasonal seafood. Also, when the user says, "I want to eat salad," the generation AI can take into consideration seasonal ingredients and suggest a salad recipe using seasonal fruits. In this way, recipes that take into consideration seasonal ingredients can be suggested.

[0037] The guidance unit can provide an ingredient list that fits the user's budget. For example, when the user says, "I want to eat curry," the generation AI takes the budget into consideration and suggests a curry recipe that can be made at low cost. Also, when the user says, "I want to eat pasta," the generation AI can take the budget into consideration and suggest a pasta recipe that can be made at low cost. Also, when the user says, "I want to eat salad," the generation AI can take the budget into consideration and suggest a salad recipe that can be made at low cost. In this way, an ingredient list that fits the user's budget can be provided.

[0038] The guidance unit can refer to the user's past participation history and suggest events that match their preferences. For example, when the user says, "I want to participate in an event next Saturday," the generation AI in the guidance unit can refer to the past participation history and suggest events that match their preferences. Also, when the user says, "I want to participate in an event next Monday," the generation AI can refer to the past participation history and suggest events that match their preferences. Also, when the user says, "I want to participate in an event this weekend," the guidance unit can refer to the past participation history and suggest events that match their preferences. This makes it possible to suggest events that match their preferences based on their past participation history.

[0039] The guidance unit can provide access information that takes into account the user's means of transportation and physical strength. For example, when the user says, "I want to attend an event next Saturday," the generation AI considers the means of transportation and provides access information for public transportation. Also, when the user says, "I want to attend an event next Monday," the generation AI can consider the user's physical strength and suggest events that are within walking distance. Also, when the user says, "I want to attend an event this weekend," the generation AI can consider the user's means of transportation and provide information on taxi dispatch services. This makes it possible to provide access information that suits the user's means of transportation and physical strength.

[0040] The guidance unit can take into account the schedules of the user's friends and family and suggest events that they can attend together. For example, when the user says, "I want to attend an event next Saturday," the generation AI can take into account the schedules of their friends and suggest events that they can attend together. Also, when the user says, "I want to attend an event next Monday," the guidance unit can take into account the schedules of their family and suggest events that they can attend together. Also, when the user says, "I want to attend an event this weekend," the guidance unit can take into account the schedules of their friends and family and suggest events that they can attend together. This makes it possible to suggest events that they can attend together with their friends and family.

[0041] The guidance unit can suggest workshops and seminars related to the user's areas of interest. For example, when the user says, "I want to attend an event next Saturday," the generation AI takes the user's area of ​​interest into consideration and suggests related workshops. Also, when the user says, "I want to attend an event next Monday," the generation AI can take the user's area of ​​interest into consideration and suggest related seminars. Also, when the user says, "I want to attend an event this weekend," the generation AI can take the user's area of ​​interest into consideration and suggest related workshops and seminars. This makes it possible to suggest workshops and seminars related to the user's area of ​​interest.

[0042] The guidance unit can provide new topics based on the user's interests and concerns. For example, when the user says, "Let's talk about the latest news," the generation AI takes the user's interests into account and provides the latest news. Also, when the user says, "Let's talk about hobbies," the generation AI can take the user's interests into account and provide related topics. Also, when the user says, "Let's talk about travel," the generation AI can take the user's interests into account and provide topics related to travel. This makes it possible to provide new topics based on the user's interests and concerns.

[0043] The guidance unit can provide related topics by referring to the user's past conversation history. For example, when the user says, "Let's talk about the latest news," the generation AI can provide related news by referring to the past conversation history. Also, when the user says, "Let's talk about hobbies," the generation AI can provide related hobby topics by referring to the past conversation history. Also, when the user says, "Let's talk about travel," the generation AI can provide related travel topics by referring to the past conversation history. This allows related topics to be provided based on past conversation history.

[0044] The guidance unit can introduce communities and groups related to the user's hobbies and special skills. For example, when the user says, "Let's talk about hobbies," the generation AI can introduce communities related to hobbies. Also, when the user says, "Let's talk about special skills," the generation AI can introduce groups related to special skills. Also, when the user says, "Let's talk about travel," the generation AI can introduce communities related to travel. In this way, communities and groups related to hobbies and special skills can be introduced.

[0045] The guidance unit can suggest online events and webinars that match the user's interests. For example, when the user says, "Let's talk about hobbies," the generation AI can suggest online events related to hobbies. Also, when the user says, "Let's talk about special skills," the generation AI can suggest webinars related to special skills. Also, when the user says, "Let's talk about travel," the generation AI can suggest online events related to travel. This makes it possible to suggest online events and webinars that match the user's interests.

[0046] The guidance unit can automatically generate reminders that match the user's schedule. For example, when the user says to the guidance unit, "Remember the time to take my medicine tomorrow," the generation AI automatically generates a reminder that matches the schedule. Also, when the user says to the guidance unit, "Remember the meeting next week," the generation AI can automatically generate a reminder that matches the schedule. Also, when the user says to the guidance unit, "Remember the date of my next doctor's appointment," the generation AI can automatically generate a reminder that matches the schedule. In this way, reminders that match the user's schedule can be automatically generated.

[0047] The guidance unit can provide health advice based on the user's lifestyle habits. For example, when the user says, "I tend to get tired easily these days," the generation AI can refer to the lifestyle habits and provide appropriate health advice. Also, when the user says, "I'm concerned about the balance of my meals," the guidance unit can provide appropriate dietary advice by referring to the lifestyle habits. Also, when the user says, "I'm concerned about not getting enough exercise," the generation AI can provide appropriate exercise advice by referring to the lifestyle habits. This makes it possible to provide health advice based on lifestyle habits.

[0048] The guidance unit can automatically generate a message to be shared with the user's family and friends. For example, when the user says, "Remember the time to take my medicine tomorrow," the guidance unit causes the generation AI to automatically generate a message to be shared with family. Also, when the user says, "Remember my meeting next week," the guidance unit can also automatically generate a message to be shared with friends. Also, when the user says, "Remember the date of my next appointment," the guidance unit can also automatically generate a message to be shared with family and friends. In this way, messages to be shared with family and friends can be automatically generated.

[0049] The guidance unit can provide new information related to the user's hobbies and interests. For example, when the user says, "Let's talk about recent gardening," the generation AI can provide the latest gardening information related to the hobbies. Also, when the user says, "I want to know new cooking recipes," the generation AI can provide the latest cooking recipes related to the interests. Also, when the user says, "I want to plan a trip," the generation AI can provide the latest travel information related to the interests. This makes it possible to provide new information related to hobbies and interests.

[0050] The guidance unit can refer to the user's past usage history and suggest the optimal way to use PayPay. For example, when the user says, "Tell me how to use PayPay," the generation AI in the guidance unit can refer to the past usage history and suggest the optimal way to use it. Also, when the user says, "Tell me how to use a coupon," the generation AI can refer to the past usage history and suggest the optimal way to use the coupon. Also, when the user says, "Tell me how to earn PayPay points," the generation AI can refer to the past usage history and suggest the optimal way to earn points. This makes it possible to suggest the optimal way to use PayPay based on the past usage history.

[0051] The guidance unit can analyze the user's purchase history and suggest the most advantageous coupon. For example, when the user says, "Tell me about PayPay coupons," the generation AI in the guidance unit analyzes the purchase history and suggests the most advantageous coupon. Also, when the user says, "Tell me which coupon is the most advantageous," the generation AI can analyze the purchase history and suggest the most advantageous coupon. Also, when the user says, "I want to use a PayPay coupon," the generation AI in the guidance unit can analyze the purchase history and suggest the most advantageous coupon. This allows the most advantageous coupon to be suggested based on the purchase history.

[0052] The guidance unit can provide benefit information tailored to the user's interests. For example, when a user says, "Tell me about PayPay benefits," the generation AI analyzes the user's interests and provides the most suitable benefit information. Also, when a user says, "Tell me which benefit is the best deal," the generation AI can analyze the user's interests and provide the most suitable benefit information. Also, when a user says, "I want to use PayPay benefits," the generation AI can analyze the user's interests and provide the most suitable benefit information. This makes it possible to provide benefit information tailored to the user's interests.

[0053] The guidance unit can suggest deals specific to the user's region. For example, when the user says, "Tell me about PayPay coupons," the generation AI in the guidance unit analyzes regional information and suggests the most suitable coupon. Also, when the user says, "Tell me which coupon is the best deal," the generation AI can analyze regional information and suggest the most suitable coupon. Also, when the user says, "I want to use a PayPay coupon," the generation AI in the guidance unit can analyze regional information and suggest the most suitable coupon. This makes it possible to suggest deals specific to the region.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can ask questions to the user to more accurately understand their symptoms. For example, when a user says, "I have a stomachache," the generation AI will ask, "Where does it hurt?" to understand the symptoms in detail. Also, when a user says, "I have a headache," the analysis unit can ask, "How long has the pain been going on for?" to understand the progression of the symptoms. Also, when a user says, "I have a chest pain," the analysis unit can ask, "How intense is the pain?" to understand the severity of the pain. This allows the user's symptoms to be understood more accurately.

[0056] The analysis unit can refer to the user's past health data to provide more accurate hospital guidance. For example, when a user says, "I have a stomachache," the generation AI will refer to the past health data and recommend hospitals where the user previously visited for similar symptoms. In addition, when a user says, "I have a headache," the analysis unit can refer to the past health data and recommend appropriate hospitals based on information about previously prescribed medications. In addition, when a user says, "I have chest pain," the analysis unit can refer to the past health data and recommend specialized hospitals if the user has a history of heart problems. This enables more accurate hospital guidance based on past health data.

[0057] The analysis unit can provide preventive measures and health advice by taking into account the user's lifestyle habits and dietary history. For example, when a user says, "I have a stomachache," the generation AI can refer to their dietary history and provide advice on easy-to-digest meals. In addition, when a user says, "I have a headache," the analysis unit can also refer to their lifestyle habits and provide advice on stress management. In addition, when a user says, "I have a chest pain," the analysis unit can also refer to their exercise habits and provide advice on moderate exercise. This makes it possible to provide preventive measures and health advice based on lifestyle habits and dietary history.

[0058] The guidance unit can refer to the user's past meal history and suggest recipes that take nutritional balance into consideration. For example, when a user says, "I want to eat curry," the generation AI can refer to the user's past meal history and suggest a curry recipe that takes nutritional balance into consideration. Also, when a user says, "I want to eat pasta," the guidance unit can refer to the user's past meal history and suggest a pasta recipe that takes nutritional balance into consideration. Also, when a user says, "I want to eat salad," the generation AI can refer to the user's past meal history and suggest a salad recipe that takes nutritional balance into consideration. This makes it possible to suggest recipes that take nutritional balance into consideration based on the user's past meal history.

[0059] The guidance unit can automatically generate recipes that accommodate allergies. For example, when a user says, "I want to eat curry," the generation AI references the allergy information and suggests a curry recipe that accommodates allergies. Also, when a user says, "I want to eat pasta," the guidance unit can have the generation AI reference the allergy information and suggest a pasta recipe that accommodates allergies. Also, when a user says, "I want to eat salad," the guidance unit can have the generation AI reference the allergy information and suggest a salad recipe that accommodates allergies. This makes it possible to automatically generate recipes that accommodate allergies.

[0060] The guidance unit can suggest recipes that take into consideration seasonal ingredients. For example, when a user says, "I want to eat curry," the generation AI takes into consideration seasonal ingredients and suggests a curry recipe using seasonal vegetables. Also, when a user says, "I want to eat pasta," the guidance unit can suggest a pasta recipe using seasonal seafood, taking into consideration seasonal ingredients. Also, when a user says, "I want to eat salad," the generation AI can suggest a salad recipe using seasonal fruits, taking into consideration seasonal ingredients. In this way, recipes that take into consideration seasonal ingredients can be suggested.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The voice input unit acquires the user's voice. For example, the voice is acquired using a microphone on a smartphone. The voice input unit can also convert the voice into text data using voice recognition technology. For example, the voice input unit converts what the user says into text in real time. Step 2: The analysis unit analyzes the voice acquired by the voice input unit. For example, the analysis unit converts the voice into text data using voice recognition technology and analyzes the content. The analysis unit can also understand the content of the voice using natural language processing technology and extract appropriate information. For example, if the user says, "My stomach hurts," the analysis unit analyzes the content and guides the user to an appropriate hospital. Step 3: The guidance unit provides appropriate information based on the content analyzed by the analysis unit. For example, the guidance unit may guide the user to the nearest hospital based on the user's symptoms. The guidance unit may also provide topics based on the user's interests. For example, if the user says, "Let's talk about the latest news," the guidance unit may provide the latest news. Step 4: The connection unit connects to the necessary service based on the information provided by the guidance unit. For example, the connection unit automatically calls the hospital to which the user is directed. The connection unit can also automatically order ingredients that the user wants to purchase from an online supermarket. For example, if the user says, "I want to eat curry," the connection unit will order the necessary ingredients from the online supermarket.

[0063] (Example 2) A support app according to an embodiment of the present invention provides elderly people with information and services they need in their daily lives, improving their quality of life. This app combines generative AI and a speech-to-text tool. This allows elderly people to easily access information and services they need in their daily lives. For example, the support app can meet a variety of needs, such as providing guidance to appropriate hospitals, purchasing groceries, participating in events, conversation support, recording important information, and providing guidance on how to use PayPay. This improves the quality of life for elderly people, allowing them to live their daily lives with peace of mind.

[0064] A support app according to an embodiment includes a voice input unit, an analysis unit, a guidance unit, and a connection unit. The voice input unit acquires a user's voice. For example, the voice is acquired using a smartphone microphone. The voice input unit can also convert the voice into text data using voice recognition technology. For example, the voice input unit converts what the user says into text in real time. The analysis unit analyzes the voice acquired by the voice input unit. For example, the analysis unit converts the voice into text data using voice recognition technology and analyzes the text. The analysis unit can also understand the content of the voice and extract appropriate information using natural language processing technology. For example, if the user says, "My stomach hurts," the analysis unit analyzes the content and provides guidance to an appropriate hospital. The guidance unit provides appropriate information based on the content analyzed by the analysis unit. For example, the guidance unit provides guidance to the nearest hospital based on the user's symptoms. The guidance unit can also provide topics based on the user's interests. For example, if the user says, "Let's talk about recent news," the guidance unit provides the latest news. The connection unit connects to the necessary service based on the information provided by the guidance unit. For example, the connection unit automatically makes a call to the hospital that the user is guided to. The connection unit can also automatically order ingredients that the user wants to purchase from an online supermarket. For example, if the user says, "I want to eat curry," the connection unit orders the necessary ingredients from the online supermarket. In this way, the support app according to the embodiment can obtain information through voice from the elderly person and connect to the necessary service. For example, if the user says, "I have a stomachache," the app can guide the elderly to an appropriate hospital and automatically make a call. Also, if the user says, "I want to eat curry," the app can order the necessary ingredients from the online supermarket.

[0065] The analysis unit analyzes the tone and speed of the user's voice, determines the level of urgency, and can recommend the most appropriate hospital. For example, when a user says, "I have chest pain," the generation AI analyzes the tone and speed of the voice, and if it determines that the level of urgency is high, it can recommend the nearest emergency hospital. Also, when a user says, "I have a headache," the generation AI analyzes the tone and speed of the voice, and if it determines that the level of urgency is low, it can also recommend a general medical hospital. Also, when a user says, "I have a stomachache," the generation AI analyzes the tone and speed of the voice, and if it determines that the level of urgency is medium, it can also recommend an internal medicine hospital. This makes it possible to recommend the appropriate hospital according to the level of urgency.

[0066] The analysis unit can ask questions to the user to more accurately grasp the symptoms. For example, when the user says "I have a stomachache," the generation AI will ask "Where does it hurt?" to grasp the symptoms in detail. Also, when the user says "I have a headache," the analysis unit can ask "How long has the pain been going on?" to grasp the progression of the symptoms. Also, when the user says "I have a chest pain," the generation AI can ask "How strong is the pain?" to grasp the degree of pain. This allows the analysis unit to grasp the user's symptoms more accurately.

[0067] The analysis unit can use the emotion estimation function to provide relaxation music and advice to reduce the user's anxiety and stress. For example, when a user says, "My chest hurts," the analysis unit estimates that the generation AI is feeling anxious and plays relaxation music. Similarly, when a user says, "My head hurts," the analysis unit can estimate that the generation AI is feeling stressed and provide advice on deep breathing. Similarly, when a user says, "My stomach hurts," the analysis unit can estimate that the generation AI is feeling tense and suggest simple stretches to relax. This helps reduce the user's anxiety and stress.

[0068] The analysis unit can refer to the user's past health data to provide more accurate hospital guidance. For example, when a user says, "I have a stomachache," the generation AI can refer to the past health data and provide guidance to hospitals that the user previously visited for similar symptoms. In addition, when a user says, "I have a headache," the generation AI can refer to the past health data and provide guidance to an appropriate hospital based on information about previously prescribed medications. In addition, when a user says, "I have chest pain," the generation AI can refer to the past health data and provide guidance to a specialized hospital if the user has a history of heart problems. This enables more accurate hospital guidance based on past health data.

[0069] The analysis unit can provide preventive measures and health advice by taking into account the user's lifestyle habits and dietary history. For example, when the user says "I have a stomachache," the generation AI can refer to the dietary history and provide advice on easy-to-digest meals. Also, when the user says "I have a headache," the analysis unit can provide stress management advice by referring to the user's lifestyle habits. Also, when the user says "I have a chest pain," the generation AI can provide moderate exercise advice by referring to the user's exercise habits. This makes it possible to provide preventive measures and health advice based on the user's lifestyle habits and dietary history.

[0070] The analysis unit uses the emotion estimation function to analyze the emotions expressed when the user describes their symptoms and recommend appropriate counseling services. For example, when the user describes "I have a chest pain," the analysis unit estimates that the generating AI is feeling anxious and recommends counseling services. Furthermore, when the user describes "I have a headache," the analysis unit can estimate that the generating AI is feeling stressed and recommend stress management counseling. Furthermore, when the user describes "I have a stomachache," the analysis unit can estimate that the generating AI is feeling tense and recommend relaxation counseling. This allows the system to recommend appropriate counseling services based on the user's emotions.

[0071] The guidance unit can refer to the user's past meal history and suggest recipes that take nutritional balance into consideration. For example, when the user says, "I want to eat curry," the generation AI can refer to the user's past meal history and suggest a curry recipe that takes nutritional balance into consideration. Also, when the user says, "I want to eat pasta," the guidance unit can refer to the user's past meal history and suggest a pasta recipe that takes nutritional balance into consideration. Also, when the user says, "I want to eat salad," the generation AI can refer to the user's past meal history and suggest a salad recipe that takes nutritional balance into consideration. This makes it possible to suggest recipes that take nutritional balance into consideration based on the user's past meal history.

[0072] The guidance unit can automatically generate recipes that accommodate allergies. For example, when a user says, "I want to eat curry," the generation AI in the guidance unit references the allergy information and suggests a curry recipe that accommodates the allergy. Also, when a user says, "I want to eat pasta," the generation AI can reference the allergy information and suggest a pasta recipe that accommodates the allergy. Also, when a user says, "I want to eat salad," the generation AI can reference the allergy information and suggest a salad recipe that accommodates the allergy. This makes it possible to automatically generate recipes that accommodate allergies.

[0073] The guidance unit uses the emotion estimation function to suggest recipes that match the user's mood, increasing the enjoyment of meals. For example, when the user says, "I want to eat curry," the generation AI in the guidance unit analyzes the user's mood and suggests a spicy curry recipe. Also, when the user says, "I want to eat pasta," the generation AI in the guidance unit can analyze the user's mood and suggest a creamy pasta recipe. Also, when the user says, "I want to eat salad," the generation AI in the guidance unit can analyze the user's mood and suggest a fresh salad recipe. This allows the system to suggest recipes that match the user's mood, increasing the enjoyment of meals.

[0074] The guidance unit can suggest recipes that take into consideration seasonal ingredients. For example, when the user says, "I want to eat curry," the generation AI takes into consideration seasonal ingredients and suggests a curry recipe using seasonal vegetables. Also, when the user says, "I want to eat pasta," the generation AI can take into consideration seasonal ingredients and suggest a pasta recipe using seasonal seafood. Also, when the user says, "I want to eat salad," the generation AI can take into consideration seasonal ingredients and suggest a salad recipe using seasonal fruits. In this way, recipes that take into consideration seasonal ingredients can be suggested.

[0075] The guidance unit can provide an ingredient list that fits the user's budget. For example, when the user says, "I want to eat curry," the generation AI takes the budget into consideration and suggests a curry recipe that can be made at low cost. Also, when the user says, "I want to eat pasta," the generation AI can take the budget into consideration and suggest a pasta recipe that can be made at low cost. Also, when the user says, "I want to eat salad," the generation AI can take the budget into consideration and suggest a salad recipe that can be made at low cost. In this way, an ingredient list that fits the user's budget can be provided.

[0076] The guidance unit can use the emotion estimation function to suggest table decoration ideas to help the user enjoy their meal. For example, when the user says, "I want to eat curry," the generation AI analyzes the user's mood and suggests table decoration ideas that go well with the curry. Also, when the user says, "I want to eat pasta," the generation AI can analyze the user's mood and suggest table decoration ideas that go well with the pasta. Also, when the user says, "I want to eat salad," the generation AI can analyze the user's mood and suggest table decoration ideas that go well with the salad. In this way, the guidance unit can suggest decoration ideas to help the user enjoy their meal.

[0077] The guidance unit can refer to the user's past participation history and suggest events that match their preferences. For example, when the user says, "I want to participate in an event next Saturday," the generation AI in the guidance unit can refer to the past participation history and suggest events that match their preferences. Also, when the user says, "I want to participate in an event next Monday," the generation AI can refer to the past participation history and suggest events that match their preferences. Also, when the user says, "I want to participate in an event this weekend," the guidance unit can refer to the past participation history and suggest events that match their preferences. This makes it possible to suggest events that match their preferences based on their past participation history.

[0078] The guidance unit can provide access information that takes into account the user's means of transportation and physical strength. For example, when the user says, "I want to attend an event next Saturday," the generation AI considers the means of transportation and provides access information for public transportation. Also, when the user says, "I want to attend an event next Monday," the generation AI can consider the user's physical strength and suggest events that are within walking distance. Also, when the user says, "I want to attend an event this weekend," the generation AI can consider the user's means of transportation and provide information on taxi dispatch services. This makes it possible to provide access information that suits the user's means of transportation and physical strength.

[0079] The guidance unit can use the emotion estimation function to suggest events that match the user's mood and increase their willingness to participate. For example, when the user says, "I want to participate in an event next Saturday," the generation AI in the guidance unit can analyze the user's mood and suggest relaxing events. Also, when the user says, "I want to participate in an event next Monday," the generation AI in the guidance unit can analyze the user's mood and suggest active events. Also, when the user says, "I want to participate in an event this weekend," the generation AI can analyze the user's mood and suggest sociable events. This makes it possible to suggest events that match the user's mood and increase their willingness to participate.

[0080] The guidance unit can take into account the schedules of the user's friends and family and suggest events that they can attend together. For example, when the user says, "I want to attend an event next Saturday," the generation AI can take into account the schedules of their friends and suggest events that they can attend together. Also, when the user says, "I want to attend an event next Monday," the guidance unit can take into account the schedules of their family and suggest events that they can attend together. Also, when the user says, "I want to attend an event this weekend," the guidance unit can take into account the schedules of their friends and family and suggest events that they can attend together. This makes it possible to suggest events that they can attend together with their friends and family.

[0081] The guidance unit can suggest workshops and seminars related to the user's areas of interest. For example, when the user says, "I want to attend an event next Saturday," the generation AI takes the user's area of ​​interest into consideration and suggests related workshops. Also, when the user says, "I want to attend an event next Monday," the generation AI can take the user's area of ​​interest into consideration and suggest related seminars. Also, when the user says, "I want to attend an event this weekend," the generation AI can take the user's area of ​​interest into consideration and suggest related workshops and seminars. This makes it possible to suggest workshops and seminars related to the user's area of ​​interest.

[0082] The guidance unit can use the emotion estimation function to provide support information to reduce anxiety when the user participates in an event. For example, when the user says, "I want to participate in an event next Saturday," the guidance unit can infer that the generation AI is feeling anxious and provide preparation information before participating. Also, when the user says, "I want to participate in an event next Monday," the guidance unit can infer that the generation AI is feeling anxious and provide detailed information about the event. Also, when the user says, "I want to participate in an event this weekend," the guidance unit can infer that the generation AI is feeling anxious and provide advice on how to relax. In this way, support information can be provided to reduce anxiety when participating in an event.

[0083] The guidance unit can provide new topics based on the user's interests and concerns. For example, when the user says, "Let's talk about the latest news," the generation AI takes the user's interests into account and provides the latest news. Also, when the user says, "Let's talk about hobbies," the generation AI can take the user's interests into account and provide related topics. Also, when the user says, "Let's talk about travel," the generation AI can take the user's interests into account and provide topics related to travel. This makes it possible to provide new topics based on the user's interests and concerns.

[0084] The guidance unit can provide related topics by referring to the user's past conversation history. For example, when the user says, "Let's talk about the latest news," the generation AI can provide related news by referring to the past conversation history. Also, when the user says, "Let's talk about hobbies," the generation AI can provide related hobby topics by referring to the past conversation history. Also, when the user says, "Let's talk about travel," the generation AI can provide related travel topics by referring to the past conversation history. This allows related topics to be provided based on past conversation history.

[0085] The guidance unit can use the emotion estimation function to provide relaxing topics that match the user's mood. For example, when the user says, "Let's talk about the latest news," the generation AI analyzes the user's mood and provides relaxing news. Also, when the user says, "Let's talk about hobbies," the generation AI can analyze the user's mood and provide relaxing hobby topics. Also, when the user says, "Let's talk about travel," the generation AI can analyze the user's mood and provide relaxing travel topics. This allows the guidance unit to provide relaxing topics that match the user's mood.

[0086] The guidance unit can introduce communities and groups related to the user's hobbies and special skills. For example, when the user says, "Let's talk about hobbies," the generation AI can introduce communities related to hobbies. Also, when the user says, "Let's talk about special skills," the generation AI can introduce groups related to special skills. Also, when the user says, "Let's talk about travel," the generation AI can introduce communities related to travel. In this way, communities and groups related to hobbies and special skills can be introduced.

[0087] The guidance unit can suggest online events and webinars that match the user's interests. For example, when the user says, "Let's talk about hobbies," the generation AI can suggest online events related to hobbies. Also, when the user says, "Let's talk about special skills," the generation AI can suggest webinars related to special skills. Also, when the user says, "Let's talk about travel," the generation AI can suggest online events related to travel. This makes it possible to suggest online events and webinars that match the user's interests.

[0088] The guidance unit can use the emotion estimation function to suggest music and video content for the user to enjoy the conversation. For example, when the user says, "Let's talk about hobbies," the generation AI analyzes the user's mood and suggests music related to the hobbies. Also, when the user says, "Let's talk about special skills," the generation AI can analyze the user's mood and suggest video content related to the special skills. Also, when the user says, "Let's talk about travel," the generation AI can analyze the user's mood and suggest music and video content related to travel. This makes it possible to suggest music and video content for the user to enjoy the conversation.

[0089] The guidance unit can automatically generate reminders that match the user's schedule. For example, when the user says to the guidance unit, "Remember the time to take my medicine tomorrow," the generation AI automatically generates a reminder that matches the schedule. Also, when the user says to the guidance unit, "Remember the meeting next week," the generation AI can automatically generate a reminder that matches the schedule. Also, when the user says to the guidance unit, "Remember the date of my next doctor's appointment," the generation AI can automatically generate a reminder that matches the schedule. In this way, reminders that match the user's schedule can be automatically generated.

[0090] The guidance unit can provide health advice based on the user's lifestyle habits. For example, when the user says, "I tend to get tired easily these days," the generation AI can refer to the lifestyle habits and provide appropriate health advice. Also, when the user says, "I'm concerned about the balance of my meals," the guidance unit can provide appropriate dietary advice by referring to the lifestyle habits. Also, when the user says, "I'm concerned about not getting enough exercise," the generation AI can provide appropriate exercise advice by referring to the lifestyle habits. This makes it possible to provide health advice based on lifestyle habits.

[0091] The guidance unit can use the emotion estimation function to analyze the emotion of the content recorded by the user and provide positive feedback. For example, when the user says, "Remember the time to take my medicine tomorrow," the generation AI in the guidance unit can analyze the emotion and provide positive feedback. Also, when the user says, "Remember my meeting next week," the generation AI can analyze the emotion and provide positive feedback. Also, when the user says, "Remember the date of my next appointment," the generation AI can analyze the emotion and provide positive feedback. In this way, the guidance unit can analyze the emotion of the recorded content and provide positive feedback.

[0092] The guidance unit can automatically generate a message to be shared with the user's family and friends. For example, when the user says, "Remember the time to take my medicine tomorrow," the guidance unit causes the generation AI to automatically generate a message to be shared with family. Also, when the user says, "Remember my meeting next week," the guidance unit can also automatically generate a message to be shared with friends. Also, when the user says, "Remember the date of my next appointment," the guidance unit can also automatically generate a message to be shared with family and friends. In this way, messages to be shared with family and friends can be automatically generated.

[0093] The guidance unit can provide new information related to the user's hobbies and interests. For example, when the user says, "Let's talk about recent gardening," the generation AI can provide the latest gardening information related to the hobbies. Also, when the user says, "I want to know new cooking recipes," the generation AI can provide the latest cooking recipes related to the interests. Also, when the user says, "I want to plan a trip," the generation AI can provide the latest travel information related to the interests. This makes it possible to provide new information related to hobbies and interests.

[0094] The guidance unit can use the emotion estimation function to provide emotional support based on the content recorded by the user. For example, when the user says, "I've been feeling tired lately," the generation AI can analyze the emotion and provide advice on how to relax. Also, when the user says, "I'm concerned about the balance of my meals," the generation AI can analyze the emotion and provide positive dietary advice. Also, when the user says, "I'm concerned about not getting enough exercise," the generation AI can analyze the emotion and provide exercise advice to increase motivation. This allows emotional support to be provided based on the content recorded.

[0095] The guidance unit can refer to the user's past usage history and suggest the optimal way to use PayPay. For example, when the user says, "Tell me how to use PayPay," the generation AI in the guidance unit can refer to the past usage history and suggest the optimal way to use it. Also, when the user says, "Tell me how to use a coupon," the generation AI can refer to the past usage history and suggest the optimal way to use the coupon. Also, when the user says, "Tell me how to earn PayPay points," the generation AI can refer to the past usage history and suggest the optimal way to earn points. This makes it possible to suggest the optimal way to use PayPay based on the past usage history.

[0096] The guidance unit can analyze the user's purchase history and suggest the most advantageous coupon. For example, when the user says, "Tell me about PayPay coupons," the generation AI in the guidance unit analyzes the purchase history and suggests the most advantageous coupon. Also, when the user says, "Tell me which coupon is the most advantageous," the generation AI can analyze the purchase history and suggest the most advantageous coupon. Also, when the user says, "I want to use a PayPay coupon," the generation AI in the guidance unit can analyze the purchase history and suggest the most advantageous coupon. This allows the most advantageous coupon to be suggested based on the purchase history.

[0097] The guidance unit can use the emotion estimation function to provide advice to increase the user's satisfaction when using coupons. For example, when the user says, "Tell me about PayPay coupons," the generation AI in the guidance unit analyzes the user's emotions and provides advice to increase satisfaction. Also, when the user says, "Tell me which coupon is the best deal," the generation AI can analyze the user's emotions and provide advice to increase satisfaction. Also, when the user says, "I want to use a PayPay coupon," the generation AI can analyze the user's emotions and provide advice to increase satisfaction. This makes it possible to provide advice to increase satisfaction when using coupons.

[0098] The guidance unit can provide benefit information tailored to the user's interests. For example, when a user says, "Tell me about PayPay benefits," the generation AI analyzes the user's interests and provides the most suitable benefit information. Also, when a user says, "Tell me which benefit is the best deal," the generation AI can analyze the user's interests and provide the most suitable benefit information. Also, when a user says, "I want to use PayPay benefits," the generation AI can analyze the user's interests and provide the most suitable benefit information. This makes it possible to provide benefit information tailored to the user's interests.

[0099] The guidance unit can suggest deals specific to the user's region. For example, when the user says, "Tell me about PayPay coupons," the generation AI in the guidance unit analyzes regional information and suggests the most suitable coupon. Also, when the user says, "Tell me which coupon is the best deal," the generation AI can analyze regional information and suggest the most suitable coupon. Also, when the user says, "I want to use a PayPay coupon," the generation AI in the guidance unit can analyze regional information and suggest the most suitable coupon. This makes it possible to suggest deals specific to the region.

[0100] The guidance unit can use the emotion estimation function to provide support information to reduce anxiety when a user uses a coupon. For example, when a user says, "Tell me about PayPay coupons," the generation AI in the guidance unit analyzes the user's emotions and provides support information to reduce anxiety. Also, when a user says, "Tell me which coupon is the best deal," the generation AI can analyze the user's emotions and provide support information to reduce anxiety. Also, when a user says, "I want to use a PayPay coupon," the generation AI can analyze the user's emotions and provide support information to reduce anxiety. This makes it possible to provide support information to reduce anxiety when using coupons.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analysis unit analyzes the tone and speed of the user's voice, determines the level of urgency, and can guide the user to the most appropriate hospital. For example, when a user says, "I have chest pain," the generation AI analyzes the tone and speed of the voice, and if it determines that the level of urgency is high, it can guide the user to the nearest emergency hospital. Also, when a user says, "I have a headache," the analysis unit analyzes the tone and speed of the voice, and if it determines that the level of urgency is low, it can guide the user to a general medical hospital. Also, when a user says, "I have a stomachache," the analysis unit analyzes the tone and speed of the voice, and if it determines that the level of urgency is medium, it can guide the user to an internal medicine hospital. This makes it possible to provide appropriate hospital guidance according to the level of urgency.

[0103] The analysis unit can ask questions to the user to more accurately understand their symptoms. For example, when a user says, "I have a stomachache," the generation AI will ask, "Where does it hurt?" to understand the symptoms in detail. Also, when a user says, "I have a headache," the analysis unit can ask, "How long has the pain been going on for?" to understand the progression of the symptoms. Also, when a user says, "I have a chest pain," the analysis unit can ask, "How intense is the pain?" to understand the severity of the pain. This allows the user's symptoms to be understood more accurately.

[0104] The analysis unit can use the emotion estimation function to provide relaxation music and advice to reduce the user's anxiety and stress. For example, when a user says "I have a chest pain," the generation AI can infer that the user is feeling anxious and play relaxation music. Similarly, when a user says "I have a headache," the analysis unit can infer that the generation AI is feeling stressed and provide advice on deep breathing. Similarly, when a user says "I have a stomachache," the analysis unit can infer that the generation AI is feeling tense and suggest simple stretches to relax. This can reduce the user's anxiety and stress.

[0105] The analysis unit can refer to the user's past health data to provide more accurate hospital guidance. For example, when a user says, "I have a stomachache," the generation AI will refer to the past health data and recommend hospitals where the user previously visited for similar symptoms. In addition, when a user says, "I have a headache," the analysis unit can refer to the past health data and recommend appropriate hospitals based on information about previously prescribed medications. In addition, when a user says, "I have chest pain," the analysis unit can refer to the past health data and recommend specialized hospitals if the user has a history of heart problems. This enables more accurate hospital guidance based on past health data.

[0106] The analysis unit can provide preventive measures and health advice by taking into account the user's lifestyle habits and dietary history. For example, when a user says, "I have a stomachache," the generation AI can refer to their dietary history and provide advice on easy-to-digest meals. In addition, when a user says, "I have a headache," the analysis unit can also refer to their lifestyle habits and provide advice on stress management. In addition, when a user says, "I have a chest pain," the analysis unit can also refer to their exercise habits and provide advice on moderate exercise. This makes it possible to provide preventive measures and health advice based on lifestyle habits and dietary history.

[0107] The analysis unit uses the emotion estimation function to analyze the emotions expressed when the user describes their symptoms and recommend appropriate counseling services. For example, when a user says, "I have a chest pain," the generation AI can estimate that the user is feeling anxious and recommend counseling services. In addition, when a user says, "I have a headache," the analysis unit can estimate that the generation AI is feeling stressed and recommend stress management counseling. In addition, when a user says, "I have a stomachache," the analysis unit can estimate that the generation AI is feeling tense and recommend relaxation counseling. This allows the system to recommend appropriate counseling services based on the user's emotions.

[0108] The guidance unit can refer to the user's past meal history and suggest recipes that take nutritional balance into consideration. For example, when a user says, "I want to eat curry," the generation AI can refer to the user's past meal history and suggest a curry recipe that takes nutritional balance into consideration. Also, when a user says, "I want to eat pasta," the guidance unit can refer to the user's past meal history and suggest a pasta recipe that takes nutritional balance into consideration. Also, when a user says, "I want to eat salad," the generation AI can refer to the user's past meal history and suggest a salad recipe that takes nutritional balance into consideration. This makes it possible to suggest recipes that take nutritional balance into consideration based on the user's past meal history.

[0109] The guidance unit can automatically generate recipes that accommodate allergies. For example, when a user says, "I want to eat curry," the generation AI references the allergy information and suggests a curry recipe that accommodates allergies. Also, when a user says, "I want to eat pasta," the guidance unit can have the generation AI reference the allergy information and suggest a pasta recipe that accommodates allergies. Also, when a user says, "I want to eat salad," the guidance unit can have the generation AI reference the allergy information and suggest a salad recipe that accommodates allergies. This makes it possible to automatically generate recipes that accommodate allergies.

[0110] The guidance unit uses the emotion estimation function to suggest recipes that match the user's mood, increasing the enjoyment of meals. For example, when a user says, "I want to eat curry," the generation AI analyzes their mood and suggests a spicy curry recipe. Also, when a user says, "I want to eat pasta," the guidance unit can analyze their mood and suggest a creamy pasta recipe. Also, when a user says, "I want to eat salad," the generation AI can analyze their mood and suggest a fresh salad recipe. This allows the guidance unit to suggest recipes that match the user's mood, increasing the enjoyment of meals.

[0111] The guidance unit can suggest recipes that take into consideration seasonal ingredients. For example, when a user says, "I want to eat curry," the generation AI takes into consideration seasonal ingredients and suggests a curry recipe using seasonal vegetables. Also, when a user says, "I want to eat pasta," the guidance unit can suggest a pasta recipe using seasonal seafood, taking into consideration seasonal ingredients. Also, when a user says, "I want to eat salad," the generation AI can suggest a salad recipe using seasonal fruits, taking into consideration seasonal ingredients. In this way, recipes that take into consideration seasonal ingredients can be suggested.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The voice input unit acquires the user's voice. For example, the voice is acquired using a microphone on a smartphone. The voice input unit can also convert the voice into text data using voice recognition technology. For example, the voice input unit converts what the user says into text in real time. Step 2: The analysis unit analyzes the voice acquired by the voice input unit. For example, the analysis unit converts the voice into text data using voice recognition technology and analyzes the content. The analysis unit can also understand the content of the voice using natural language processing technology and extract appropriate information. For example, if the user says, "My stomach hurts," the analysis unit analyzes the content and guides the user to an appropriate hospital. Step 3: The guidance unit provides appropriate information based on the content analyzed by the analysis unit. For example, the guidance unit may guide the user to the nearest hospital based on the user's symptoms. The guidance unit may also provide topics based on the user's interests. For example, if the user says, "Let's talk about the latest news," the guidance unit may provide the latest news. Step 4: The connection unit connects to the necessary service based on the information provided by the guidance unit. For example, the connection unit automatically calls the hospital to which the user is directed. The connection unit can also automatically order ingredients that the user wants to purchase from an online supermarket. For example, if the user says, "I want to eat curry," the connection unit will order the necessary ingredients from the online supermarket.

[0114] 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.

[0115] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0135] 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.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0150] 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.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] 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.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0155] 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.

[0156] 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.

[0157] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] 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.

[0164] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0165] 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.

[0166] 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).

[0167] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0175] The hardware resource that executes the specific process 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 process may be a single processor.

[0176] 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.

[0177] 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.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] 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.

[0180] 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. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice input unit for acquiring voice; an analysis unit that analyzes the voice acquired by the voice input unit; a guide unit that provides appropriate information based on the content analyzed by the analysis unit; a connection unit that connects to a required service based on the information provided by the guidance unit. A system characterized by:

2. The analysis unit Analyzes the tone and speed of the user's voice to determine the urgency of the call and guides them to the most appropriate hospital.

2. The system of claim 1.

3. The analysis unit Asking questions to the user to better understand the symptoms 2. The system of claim 1.

4. The analysis unit Provides relaxation music and advice to reduce anxiety and stress 2. The system of claim 1.

5. The analysis unit Referencing the user's past health data to provide more accurate hospital guidance 2. The system of claim 1.

6. The analysis unit Taking into account the user's lifestyle and dietary history, the system provides preventative measures and health advice 2. The system of claim 1.

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