system

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a request from a user using speech recognition and analyzing the request in natural language; means for obtaining appropriate information from a database or an external API based on profile information of the user and a history of past requests; means for providing the obtained information to the user in voice or text; and means for periodically generating information and event notifications for elderly users and notifying the users.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, as society ages, there is a growing need for efficient access to the wide range of information needed by the elderly. However, current systems and tools are often insufficiently customized based on the needs and interests of individual elderly people and are complicated to operate. Important information and event notifications are sometimes not provided or are delayed, disrupting the daily lives of the elderly. Therefore, it is necessary to provide a system that allows the elderly to easily obtain the information they need and lead smooth daily lives. [Means for solving the problem]

[0005] This invention provides a system for elderly users that uses voice recognition to receive requests from users and performs natural language analysis. Based on the user's profile information and past request history, the system retrieves appropriate information from a database or external API and provides the retrieved information to the user in voice or text format. It also has a function to periodically generate information and event notifications for elderly users and notify them. This allows users to easily obtain the information they need, and topics based on the user's interests and preferences can be suggested for specific information categories. Furthermore, the system filters the retrieved information and displays it in a format that is optimal for the user, thereby providing information that meets the needs of elderly users.

[0006] "Speech recognition" is a technology that analyzes speech and converts it into text form.

[0007] "Natural language analysis" is a technology for processing natural language text and understanding its meaning.

[0008] "Profile Information" is data that describes a user's personal information, preferences, interests, etc.

[0009] "Request history" is a record of past information requests and questions made by a user.

[0010] A "database" is a collection of data that systematically stores specific information and makes it easy to search and retrieve.

[0011] An "external API" is an interface that enables collaboration with other services and systems.

[0012] "Providing audio" means converting acquired information from text to audio and conveying it to the user.

[0013] "Providing text" refers to a method of displaying acquired information to the user in text format.

[0014] "Event notification" is a function that notifies users of important pre-set event information.

[0015] "Topic suggestions" means presenting relevant information based on a user's interests.

[0016] "Filtering" is the process of selecting necessary information from a vast amount of information based on specific conditions.

[0017] "Customization" means individually adjusting systems and information to suit the user's preferences. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and combines voice recognition technology, natural language analysis technology, database technology, and external API utilization technology. Specific embodiments of this system are described below.

[0040] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, consider the request "Tell me the nearest internal medicine hospital."

[0041] Voice Request Processing

[0042] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0043] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[0044] Request analysis and information acquisition

[0045] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[0046] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to receive a list of the nearest internal medicine hospitals.

[0047] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[0048] Providing information

[0049] Server: Sends filtered hospital information to the terminal.

[0050] Device: The received information is displayed to the user in voice and text format. For example, it may provide information such as, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[0051] Event notifications

[0052] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[0053] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[0054] Users: Check notifications and view details of events that interest them.

[0055] Topic Suggestion

[0056] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0057] On device: Suggested topics are presented to the user as audio or text.

[0058] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[0059] In this way, this system can improve the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together, allowing users to obtain the information they need easily and intuitively.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] User: Logs into the system. The login screen displays fields for entering ID and password.

[0063] Step 2:

[0064] Terminal: Sends the ID and password entered by the user to the server.

[0065] Step 3:

[0066] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[0067] Step 4:

[0068] On your device: Upon successful authentication, display a custom home screen based on the user's profile information. If it's your first time logging in, display an initial setup screen and ask for personal information and topics of interest.

[0069] Step 5:

[0070] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[0071] Step 6:

[0072] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[0073] Step 7:

[0074] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[0075] Step 8:

[0076] Server: Based on the analysis results, the server identifies the user's location and the type of hospital they prefer (e.g., internal medicine).

[0077] Step 9:

[0078] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[0079] Step 10:

[0080] Server: Filters the acquired hospital information and selects the most appropriate hospital information for the user.

[0081] Step 11:

[0082] Server: Sends the selected hospital information to the terminal.

[0083] Step 12:

[0084] Device: The received information is displayed to the user in voice and text format. For example, it may say, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[0085] Step 13:

[0086] Server: Based on user profile information, collects relevant event information and generates event notifications periodically.

[0087] Step 14:

[0088] Server: Sends generated event notifications to devices.

[0089] Step 15:

[0090] Device: Notify the user of the sent event notification with sound and popup.

[0091] Step 16:

[0092] Users: Check the notified event information and click or tap to view more information.

[0093] Step 17:

[0094] Server: Analyzes the user's past request history and periodically suggests topics that may be of interest to them.

[0095] Step 18:

[0096] On the device: Suggested topics are presented to the user in audio and text format.

[0097] Step 19:

[0098] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[0099] Step 20:

[0100] Terminal: Sends the user's add request to the server.

[0101] Step 21:

[0102] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[0103] Step 22:

[0104] Device: Displays the answers obtained to the user in audio and text format.

[0105] Through this series of processes, users can easily obtain the information they need through voice or text, and receive information and event notifications to assist with their daily lives.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] There is a need for systems that allow elderly people to easily and quickly obtain the information they need in their daily lives. Previous technologies have had problems such as low accuracy in speech recognition and natural language analysis, inefficient information retrieval, and a lack of suggestions for related events and topics. This has made it difficult for elderly people to obtain the information they want in an appropriate format.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis on the request, means for retrieving appropriate information from a storage device or an external data source based on the user's profile information and past request history, and means for providing the retrieved information to the user in voice or text format. This allows elderly people to efficiently obtain the information they need and have it provided in an appropriate format, thereby improving their quality of life. The server also includes means for receiving a user's voice request and converting it into text using a voice recognition engine, means for automatically filtering information and providing it in an optimal format, and means for suggesting topics that may be of interest, further improving the user experience.

[0111] "Speech recognition" is a technology that analyzes voice signals and converts them into text.

[0112] "Natural language analysis" is a technology that analyzes human language into a form that a computer can understand and extracts intent.

[0113] "User profile information" refers to data such as a user's personal information, settings, and past behavioral history.

[0114] "Request History" is a record of requests that a user has made to the system in the past.

[0115] "Storage device" is a general term for hardware and software used to store data.

[0116] "External data sources" refers to APIs and databases provided by third parties.

[0117] "Filtering" is the process of selecting information that matches specific conditions from the acquired information.

[0118] "Topic Suggestion" is a feature that provides themes and topics that may be of interest to users based on their past behavioral history.

[0119] "Event Notification" is a function that regularly provides users with events and information specifically aimed at seniors.

[0120] A "speech recognition engine" is a software component that recognizes speech and converts it into text.

[0121] A "natural language analysis engine" is a software component that analyzes text data and converts it into information that a computer can understand.

[0122] This invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and it combines voice recognition technology, natural language analysis technology, database and external API utilization technology. This system can be maximized by implementing it as follows.

[0123] First, a user logs into the system using a username and password. If the login is successful, a session is started and the user can start using the system.

[0124] Next, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest internal medicine hospital." If the user makes a request by voice, a speech recognition engine (e.g., Google's (registered trademark) speech recognition technology) is used to convert the voice signal into text. The converted text is sent to the server as request data, "the nearest internal medicine hospital."

[0125] The server analyzes the received request data using a natural language analysis engine (e.g., Azure® natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's profile information and desired information (e.g., internal medicine hospitals). The server then uses a database and external data sources (e.g., local hospital information APIs) to obtain the most appropriate information. For example, the server queries the hospital information API using the keywords "internal medicine" and "nearest" to receive a list of the nearest internal medicine hospitals.

[0126] The acquired information is filtered and selected in the most appropriate form for the user. The filtered information is sent from the server to the device, which then displays it to the user in voice and text format. For example, it may be provided in the form of "The nearest internal medicine hospital is XX Hospital, and its address is XX." After providing the information, the device prompts the user with an additional question: "Is there any other information you are looking for?"

[0127] The server also periodically collects relevant event information based on the user's profile information and generates event notifications tailored to the user, such as local health events and workshops for seniors. Event notifications are sent to the device and notify the user via audio and a pop-up display. The user can then view the notifications and view details of events of interest.

[0128] Additionally, the server analyzes the user's past request history and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts. These suggested topics are displayed on the device, allowing the user to ask further questions based on them. For example, they could ask, "What is the current flu outbreak situation?"

[0129] The components of the system work together to allow users to easily and intuitively obtain the information they need. For example, here is a prompt:

[0130] Example prompt: Receive a voice request saying, "What is the nearest internal medicine hospital?" and provide information about the appropriate internal medicine hospital.

[0131] In this way, this system can improve the quality of life of the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1: Log in

[0134] User: A user logs into the system by first entering a username and password.

[0135] Terminal: Processes the entered username and password and sends them to the server as an authentication request.

[0136] Server: Checks the received username and password against the database. If the check is successful, starts a session and returns a successful authentication message to the terminal. If the check is unsuccessful, an error message is returned.

[0137] Step 2: Accepting a voice request

[0138] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0139] Device: Receives audio through a microphone, captures it as audio data, and sends the captured audio data to a speech recognition engine.

[0140] Step 3: Voice Recognition

[0141] Terminal: The received voice data is analyzed by a voice recognition engine and converted into text. For example, the text may be "Please tell me the location of the nearest internal medicine hospital."

[0142] Terminal: The converted text is sent to the server as request data. The input data is the text converted from speech, and the output is the parsed request data.

[0143] Step 4: Request Analysis

[0144] Server: Analyzes the text data using a natural language analysis engine to understand the user's intent. The input data is the converted text, and the analysis results contain the user's intent.

[0145] Server: Based on the analysis results, retrieves the user's profile information and desired information (e.g., internal medicine hospital) from the database. The output is data containing the user's intent and related information.

[0146] Step 5: Information Acquisition

[0147] Server: Based on the analysis results, it issues a search query to an external data source (e.g., a local hospital information API). For example, it searches for hospitals based on the criteria "internal medicine" and "current location."

[0148] Server: Receives the list of nearest internal medicine hospitals returned from the API. The input is the search query, and the output is the list of hospital information retrieved.

[0149] Step 6: Information filtering

[0150] Server: Filters the received list of hospitals and selects the best hospital for the user, for example, based on reputation, distance, and opening hours.

[0151] Server: Formats the filtered information and provides it to the user. The input is the hospital information list, and the output is the filtered information.

[0152] Step 7: Provide information

[0153] Server: Sends the formatted hospital information to the terminal. The input is the filtered information, and the output is the transmitted data.

[0154] Terminal: The received information is displayed to the user as voice and text. For example, it might output "The nearest internal medicine hospital is XX Hospital, and its address is XX." The input is the transmitted data, and the output is the displayed information.

[0155] Step 8: Related Event Notifications

[0156] Server: Based on the user's profile information, collects event information that the user may be interested in. The input is the profile information, and the output is a list of related events.

[0157] Server: Organizes event information and generates optimal event notifications for users.

[0158] Terminal: Receives event notifications sent from the server and notifies the user with sound and a popup. The input is the event notification data, and the output is the notification.

[0159] Users: Check notifications and view details about events that interest them.

[0160] Step 9: Topic Proposal

[0161] Server: Analyzes the user's past request history and suggests topics they may be interested in. The input is the request history, and the output is the suggested topics.

[0162] Terminal: Presents suggested topics to the user as speech or text. The input is the suggested topic, and the output is the displayed topic.

[0163] User: Ask follow-up questions based on the suggested topic, such as "What's the current flu outbreak like?"

[0164] (Application example 1)

[0165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0166] It is important for elderly people to easily and quickly obtain the information they need in their daily lives. However, conventional information provision systems are not specialized for the needs of elderly people, and obtaining the necessary information can require a great deal of time and effort. Furthermore, they lack the functionality to suggest meal delivery services that may be of particular interest to elderly people. Therefore, there is a need for a system that allows elderly people to easily use appropriate meal delivery services based on their preferences and history.

[0167] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0168] In this invention, the server includes means for receiving requests from users using voice recognition and performing natural language analysis of the requests, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the retrieved information to the user in voice or text, and means for proposing food delivery services based on the user's preferences and history and notifying the user of the proposed information in voice or text. This enables elderly people to quickly and easily obtain appropriate information that meets their needs, and in particular, receive suggestions regarding meal delivery services.

[0169] "Speech recognition" is a technology that converts voice data into text data.

[0170] "Natural language analysis" is a technology that converts the natural language used by humans on a daily basis into a format that is easy for computers to understand and analyzes its meaning.

[0171] "Profile Information" refers to information that includes a user's personal data, preferences, and past activity history.

[0172] An "external API" is a standardized interface for connecting with other services and databases.

[0173] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0174] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a designated location such as your home.

[0175] "Audio notifications" are a means of conveying information to the user in audio form.

[0176] A "text notification" is a means of conveying information to a user in written form.

[0177] "Event Notification" is a method of periodically notifying users of events and activities that are relevant to them.

[0178] The system of this invention is designed to enable elderly people to efficiently and easily obtain information necessary for daily life. The system combines voice recognition technology, natural language analysis technology, a database, an external API, and a food delivery service suggestion function. A specific embodiment of this system is described below.

[0179] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, the user can request "Tell me the nearest sushi restaurant."

[0180] Voice Request Processing

[0181] User: The user makes a request by voice.

[0182] Device: Converts the voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The converted text is sent to the server as request data for "nearest sushi restaurant."

[0183] Request analysis and information acquisition

[0184] Server: Analyzes the received request data using a natural language analysis engine (e.g., NLPProcessor) to understand the user's intent. Based on the analysis results, the user's location information and desired service (sushi) are identified.

[0185] Server: Uses a database and external APIs (e.g., Zomato API or Uber Eats API) to obtain the best sushi restaurant information. For example, make a query using the keywords "sushi" and "nearest" and receive a list of the nearest sushi restaurants.

[0186] Server: Filters the acquired sushi restaurant information and selects the most suitable service for the user.

[0187] Providing information

[0188] Server: Sends filtered service information to the terminal.

[0189] Device: The received information is displayed to the user in voice and text format. For example, it might provide information such as, "The nearest sushi restaurant is XX Sushi, and its address is XX."

[0190] Event notifications

[0191] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[0192] Terminal: Receives event notifications sent from the server and notifies the user in the form of sound and popup display.

[0193] Topic Suggestion

[0194] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0195] On the device: Suggested topics are presented to the user as audio and text.

[0196] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[0197] This system improves the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together to enable users to obtain the information they need easily and intuitively.

[0198] Prompt Sentence Examples

[0199] A user makes a voice request saying, "I'd like to order sushi." Use natural language analysis and a food delivery API to find the nearest recommended sushi restaurant and generate a response like this: "1. Sushi XX, address: XX. 2. Sushi △△, address: □□."

[0200] User location: Chuo-ku, Tokyo

[0201] Food preference: Sushi

[0202] Available APIs: Zomato, UberEats

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] A user logs in to the system. After logging in, they request specific information by voice or text. For example, the user might say, "Tell me the nearest sushi restaurant." The input is the user's voice data, and the output is digital voice data.

[0206] Step 2:

[0207] The device receives the user's voice data and converts it into text data using a speech recognition engine (for example, Google Speech-to-Text API). Specifically, the device sends the voice data to the speech recognition API and receives the text data "nearest sushi restaurant" as the conversion result. The input is digital voice data and the output is text data.

[0208] Step 3:

[0209] The server receives the request data sent from the device and analyzes it using a natural language analysis engine (e.g., NLPProcessor). Through this analysis, it understands the user's intent and converts a request such as "Tell me the nearest sushi restaurant" into a specific search query. The input is text data, and the output is the analyzed query data.

[0210] Step 4:

[0211] The server uses the user's location and preference information to send a search query to a database or external API (e.g., Zomato API or Uber Eats API). For example, a query is made using the keywords "sushi" and "nearest." The input is the query data, and the output is a list of services (sushi restaurants) provided.

[0212] Step 5:

[0213] The server filters the acquired service information and selects the sushi restaurant that best suits the user. Specifically, it selects the optimal service based on the user's past history and profile information. The input is the list data of sushi restaurants and the user profile data, and the output is the filtered sushi restaurant data.

[0214] Step 6:

[0215] The server sends the filtered sushi restaurant information to the terminal. It uses REST API as the transmission protocol to transfer data to the terminal. The input is the filtered sushi restaurant data, and the output is the data sent to the terminal.

[0216] Step 7:

[0217] The device receives the sushi restaurant information and displays it to the user in voice and text. It uses a voice output engine (Text-to-Speech TTS engine, such as Google Text-to-Speech API) to provide the information in voice, while simultaneously displaying text information on the screen. The input is the data sent to the device, and the output is voice and text information.

[0218] Step 8:

[0219] The server periodically collects relevant event information based on the user's profile information and generates optimal event notifications for the user, such as local health events or workshops for the elderly. The input is user profile data and event data, and the output is event notification data.

[0220] Step 9:

[0221] The terminal receives the event notification sent from the server and notifies the user. The notification is in the form of voice and a pop-up display. The input is the event notification data, and the output is voice and text notification.

[0222] Step 10:

[0223] The server analyzes the user's past request history and periodically suggests topics that the user may be interested in, such as the latest health news or weather forecasts. The input is the request history data, and the output is the suggested topic data.

[0224] Step 11:

[0225] The terminal displays the suggested topics to the user by voice and text. The input is the suggested topic data, and the output is the voice and text display.

[0226] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0227] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[0228] First, the user logs into the system. After logging in, the user can request specific information by voice or text, for example, "Tell me the nearest hospital that has an internal medicine department."

[0229] Voice Request Processing

[0230] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0231] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[0232] Request analysis and information acquisition

[0233] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[0234] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[0235] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[0236] Sentiment analysis and information provision

[0237] On the device: The emotion analysis engine uses the voice data collected during the voice request to analyze the user's emotions. For example, if the user is feeling anxious, that emotion will be analyzed.

[0238] Server: Receives emotional data from the sentiment analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, the server will provide reassuring explanations in a more friendly tone.

[0239] Server: Sends the adjusted information to the device.

[0240] Device: The received information is displayed to the user in voice and text. For example, it may say, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[0241] Event notifications

[0242] Server: Based on the user's profile information, the server periodically collects relevant event information and generates appropriate event notifications for the user, such as local health events or workshops for seniors.

[0243] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[0244] Users: Check notifications and view details of events that interest them.

[0245] Topic Suggestion

[0246] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0247] On device: Suggested topics are presented to the user as audio or text.

[0248] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[0249] Use of Emotional Data

[0250] Server: The emotion data recognized by the emotion analysis engine is added to the user's profile information, allowing the emotion data to be reflected in future information and topic suggestions, providing a more personalized service.

[0251] In this way, this system analyzes the user's voice request, determines their emotions, and provides appropriate information, thereby meeting the needs of the elderly while also providing a service that takes their emotions into consideration.The various components of the system work together, allowing users to obtain the information they need with peace of mind.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] User: Logs into the system. The login screen displays fields for entering ID and password.

[0255] Step 2:

[0256] Terminal: Sends the ID and password entered by the user to the server.

[0257] Step 3:

[0258] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[0259] Step 4:

[0260] Device: Upon successful authentication, a custom home screen based on the user's profile information is displayed. If it is the first time the user is logged in, an initial setup screen is displayed, prompting the user to enter personal information and topics of interest, if necessary.

[0261] Step 5:

[0262] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[0263] Step 6:

[0264] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[0265] Step 7:

[0266] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[0267] Step 8:

[0268] Server: Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) desired.

[0269] Step 9:

[0270] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[0271] Step 10:

[0272] Server: Filters the acquired hospital information and selects the hospital information that is most appropriate for the user.

[0273] Step 11:

[0274] On the device: Using voice data collected simultaneously with the voice request, an emotion analysis engine analyzes the user's emotions, for example, determining whether the user is feeling anxious.

[0275] Step 12:

[0276] Server: Receives emotional data from sentiment analysis and adjusts the tone and content of the information provided. For example, if the user is feeling anxious, it generates a more friendly and reassuring explanation.

[0277] Step 13:

[0278] Server: Sends the adjusted information to the device.

[0279] Step 14:

[0280] Device: Presents the received information to the user via voice and text. For example, it might say, "The nearest internal medicine hospital is XX Hospital, its address is XX. This hospital has a good reputation and is safe."

[0281] Step 15:

[0282] Server: Collects event information of interest based on user profile information and generates event notifications periodically.

[0283] Step 16:

[0284] Server: Sends generated event notifications to devices.

[0285] Step 17:

[0286] Device: Notify users of event notifications with sound and popup.

[0287] Step 18:

[0288] Users: Check the event information they are notified about and click or tap to view details about the event they are interested in.

[0289] Step 19:

[0290] Server: Analyzes the user's past request history and suggests topics that may be of interest to them.

[0291] Step 20:

[0292] On the device: Suggested topics are presented to the user in audio and text format.

[0293] Step 21:

[0294] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[0295] Step 22:

[0296] Terminal: Sends the user's add request to the server.

[0297] Step 23:

[0298] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[0299] Step 24:

[0300] Device: Displays the answers obtained to the user in audio and text format.

[0301] This series of processes allows users to easily obtain the information they need through voice or text, and allows them to receive emotionally sensitive services with peace of mind.

[0302] Example 2

[0303] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0304] Elderly people face challenges in efficiently and easily obtaining the information they need in their daily lives. Furthermore, conventional information provision systems do not provide information tailored to the user's emotions, making it difficult for users to understand the information and feel reassured. Furthermore, the need for suggesting related information based on past request history and providing filtered, accurate information is also a problem.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0306] In this invention, the server includes: means for receiving a request from a user through speech recognition and converting the request into text; means for analyzing the converted text through natural language analysis to understand the user's intent; means for retrieving appropriate information from a database or external information source based on the user's profile information and past request history; means for filtering the retrieved information and providing it in a format optimal for the user; means for performing emotion analysis using the retrieved voice data; means for adjusting the tone and content of the information provided based on the analyzed emotion; and means for periodically generating information and event notifications for elderly users and notifying them of the information. This not only enables elderly users to efficiently and easily obtain the information they need, but also provides information that takes the user's emotions into consideration and gives them peace of mind. It also enables suggestions for related information and the provision of accurate information.

[0307] "Speech recognition" is a technology that analyzes voice signals and converts their contents into text data.

[0308] "Natural language analysis" is a technology that analyzes text data to understand its meaning and intent.

[0309] "Profile information" is data that records a user's basic information, past behavioral history, interests, etc.

[0310] An "external information source" is a resource used to obtain information, such as a database or API that exists outside the system.

[0311] "Filtering" is the process of selecting necessary information from acquired information based on specific conditions.

[0312] "Emotion analysis" is a technology that analyzes voice and text data to identify a user's emotional state.

[0313] "Tone" refers to the tone and attitude of the voice or writing used to provide information.

[0314] "Event Notifications" is a feature that notifies users of information about events and activities that may be of interest to them.

[0315] "Information provision" refers to the act of displaying or transmitting audio data necessary in response to a user's request.

[0316] "Related information" is topics and data suggested to you based on your interests and past requests.

[0317] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[0318] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest hospital that has an internal medicine department."

[0319] In the case of a voice request, the user makes a voice request such as "Tell me the nearest internal medicine hospital." The device receives the user's voice and converts it into text using a voice recognition engine (e.g., general voice recognition technology). The converted text is sent to the server as request data, "The nearest internal medicine hospital."

[0320] The server analyzes the received request data using a natural language analysis engine (e.g., general natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's location information and the type of hospital (internal medicine) desired. The server then uses a database and external API to obtain the most suitable hospital information. For example, it queries the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[0321] Next, the server filters the acquired hospital information and selects the most suitable hospital for the user. The device uses an emotion analysis engine (e.g., general emotion analysis technology) to analyze the user's emotions using the voice data collected during the voice request. The server receives the emotion data from the emotion analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, it will provide a more reassuring explanation in a more friendly tone. The adjusted information is sent to the device, which then displays the received information to the user in voice and text. Specifically, it provides information such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This is a safe hospital with a good reputation."

[0322] Furthermore, the system periodically collects relevant event information based on the user's profile information and generates event notifications appropriate for the user. For example, local health events or workshops for the elderly are targeted. The server updates this event information as needed, and the device provides notifications to the user in the form of audio and pop-up displays. The user can check the notifications and view details of events of interest.

[0323] The device also analyzes the user's past request history and periodically suggests topics that may be of interest to the user. For example, it suggests the latest health news or weather forecasts. The device displays the suggested topics to the user via voice or text, and the user can ask further questions based on the suggested topics. For example, a question could be, "What is the current flu epidemic situation?"

[0324] Emotional data recognized by the emotion analysis engine is added to the user's profile information and reflected in future information and topic suggestions, allowing for a more personalized service.

[0325] This system allows elderly people to obtain the information they need efficiently and easily, and provides information that is considerate of their emotions and gives them a sense of security. Below are some examples of prompts:

[0326] Example prompt:

[0327] User: "What's the nearest internal medicine hospital?"

[0328] Terminal: Receives voice and converts it into text (general voice recognition technology)

[0329] Server: Receives and analyzes the request text (general natural language analysis technology)

[0330] Server: Based on the analysis results, queries the local hospital information API and obtains the most appropriate hospital information.

[0331] Server: Analyzes the user's emotions and adjusts the tone of the information provided based on their emotions (general emotion analysis technology).

[0332] Device: Provides tailored information to the user via voice and text.

[0333] In this way, each component of the system works in cooperation with each other, allowing users to obtain the information they need with peace of mind.

[0334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0335] Step 1:

[0336] Input: A voice request from the user (e.g., "What is the nearest internal medicine hospital?")

[0337] How it works: The user makes a verbal request: "Tell me the nearest internal medicine hospital."

[0338] Output: User's voice data

[0339] Step 2:

[0340] Input: User's voice data

[0341] How it works: The device receives the user's speech and converts it into text using a speech recognition engine. Specifically, it sends the speech data to a speech recognition API such as Google Cloud Speech-to-Text and retrieves the text data in return.

[0342] Output: Text data (e.g., "What is the nearest hospital for internal medicine?")

[0343] Step 3:

[0344] Input: Text data

[0345] Operation: The terminal sends the converted text data to the server.

[0346] Output: Text data is sent to the server

[0347] Step 4:

[0348] Input: Text data (on the server)

[0349] How it works: The server analyzes the text data using a natural language analysis engine (e.g., Google Cloud Natural Language API) to understand the user's intent. Specifically, it sends the text data to the analysis engine's API and obtains the user's intent and request as the analysis result.

[0350] Output: User intent and request (e.g., "Hospital for internal medicine" "Nearest")

[0351] Step 5:

[0352] Input: User intent and request

[0353] Operation: The server obtains the user's location information and uses the hospital information API to obtain information on the nearest internal medicine hospital. Specifically, it queries the API using keywords such as "internal medicine" and "nearest" and obtains a list of internal medicine hospitals.

[0354] Output: List of nearest internal medicine hospitals

[0355] Step 6:

[0356] Input: List of nearest internal medicine hospitals

[0357] How it works: The server filters the hospital information it obtains and selects the hospital that best suits the user. Filtering criteria include distance, reputation, and opening hours.

[0358] Output: Information on the best hospital (e.g., "Hospital X, address XX, good reputation")

[0359] Step 7:

[0360] Input: User's voice data

[0361] How it works: The device sends the voice data collected during the voice request to an emotion analysis engine (e.g., IBM Watson® Tone Analyzer) to analyze the user's emotions. The analysis results include the user's feelings of anxiety, relief, or confusion.

[0362] Output: User's emotion data (e.g., "anxiety")

[0363] Step 8:

[0364] Input: Optimal hospital information, user sentiment data

[0365] How it works: The server adjusts the tone and content of the information it provides based on the emotional data. For example, if a user feels anxious, it will provide information in a reassuring tone.

[0366] Output: Tailored information (e.g., "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe.")

[0367] Step 9:

[0368] Input: Adjusted information

[0369] How it works: The server sends the tailored information to the device, which then presents it to the user in voice and text.

[0370] Output: Information sent from the server to the device

[0371] Step 10:

[0372] Input: Information sent from the server to the device

[0373] Operation: The device displays the received information to the user in voice and text. The user receives information such as "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[0374] Output: Information provided to the user

[0375] (Application example 2)

[0376] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0377] It is important for elderly people to efficiently and easily obtain the information they need in their daily lives, but this is often technically difficult to achieve. Furthermore, information provision systems rarely respond in a way that takes into account the emotions and psychological state of elderly people, which can increase anxiety and stress. Furthermore, as the use of self-driving vehicles increases, there is a lack of support for elderly people to use self-driving vehicles safely. Therefore, there is a need for a system that allows elderly people to easily obtain information and provides support that takes their emotions into consideration.

[0378] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0379] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis of the request, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the information to the user in voice or text, and means for analyzing emotions using the voice data and adjusting the tone and content of the information provided based on the analysis results. This enables elderly people to obtain the information they need with peace of mind when using an autonomous vehicle and receive responses that take their emotions into consideration.

[0380] "Speech recognition" is a technology that analyzes speech as digital data and converts the content into text.

[0381] "Request" means a request for specific information or services sought by a User.

[0382] "Natural language analysis" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[0383] "Profile information" is data that compiles information about a user, such as personal information, usage history, and interests.

[0384] An "external API" is a program interface for connecting with external systems and services.

[0385] A "database" is a system that efficiently stores and manages large amounts of data and allows it to be quickly retrieved when needed.

[0386] "Voice provision" refers to the means of conveying acquired information to the user by voice.

[0387] "Text provision" refers to the means of conveying acquired information to users in text.

[0388] "Emotion analysis" is a technology that infers and analyzes a user's emotional state from data such as voice and text.

[0389] "Tone" refers to the manner and tone of speech used when providing information, and should be adjusted according to the user's emotions.

[0390] "Information for elderly users" refers to information on medical care, welfare, and lifestyle support that is particularly needed by the elderly.

[0391] "Event Notification" is a system that notifies users in advance of relevant events and important announcements.

[0392] "Filtering" is the process of narrowing down the information obtained to what is useful to the user.

[0393] "Personalized services" refer to information and services that are optimized for specific users based on their profile information and emotional data.

[0394] An "autonomous vehicle" is a vehicle equipped with technology that allows it to drive automatically without the involvement of a driver.

[0395] This invention aims to provide a system that allows elderly people to efficiently and easily obtain the information they need when using self-driving vehicles. Furthermore, by adjusting the tone and content of the information provided based on emotion analysis, it aims to create an environment where elderly people can use vehicles with peace of mind.

[0396] First, the user makes a request to the terminal in the vehicle through the voice recognition system. For example, they might say, "Tell me where the nearest hospital is." The terminal converts this voice data into a digital signal, which is then converted into text using a voice recognition engine (for example, Google's voice recognition API). The converted text is sent to the server as request data for "nearest hospital."

[0397] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Based on this analysis, it understands the user's intent and then references the user's profile information and past request history to retrieve appropriate information from a database or external API (e.g., a local hospital information API). The retrieved information could be a list of the nearest hospitals, their detailed addresses, ratings, etc.

[0398] Next, an emotion analysis engine (e.g., EmotionRecognizer) installed on the device is used to analyze the emotions felt by the user when they input their voice. Specifically, anxiety or tension is detected from the voice data. This emotion data is sent to the server, which then adjusts the tone and content of the information provided. For example, if the user is feeling anxious, the server will provide information in a friendly tone, such as, "Don't worry. The nearest hospital is ____. It has a good reputation and is a safe place."

[0399] The adjusted information is then provided to the user via the terminal in the form of voice and text, and the user can follow the voice prompts to find the nearest hospital.

[0400] Furthermore, the server periodically generates relevant event notifications based on the user's profile information and sends them to the device, such as local health events or workshops for seniors. The device then notifies the user of these notifications in the form of voice and pop-up displays.

[0401] As a concrete example, if an elderly person requests, "Tell me the nearest cafe," the server will analyze the request using a natural language analysis engine and retrieve the most suitable cafe information from an external API based on the profile information and past history. At the same time, if the emotion analysis engine detects anxiety from the voice data, the server will provide information in a reassuring tone, such as, "Don't worry, this is a cafe with a good reputation."

[0402] The following are examples of prompts to aid in implementation:

[0403] "You will create a program that responds to the user's voice request with appropriate information. This includes voice recognition, emotion analysis, and location information acquisition."

[0404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0405] Step 1:

[0406] The user inputs a voice request into the terminal, including specific information such as "Tell me where the nearest hospital is." The input voice is converted into digital data.

[0407] Step 2:

[0408] The device uses a speech recognition engine (for example, Google's speech recognition API) to convert the voice data into text data. The converted text data becomes request data such as "nearest hospital" and is sent to the server. Here, the voice data is converted into text data and sent as a result of processing, resulting in text data.

[0409] Step 3:

[0410] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Through analysis, it understands the details of the information the user is looking for (e.g., the category "hospitals" and the request "nearest"). During this analysis process, the text data is converted into data that indicates the user's intent.

[0411] Step 4:

[0412] The server references the user's profile information and past request history to retrieve the appropriate information. This may involve querying a database or an external API (e.g., a local hospital information API). Specifically, the server queries the database for the keywords "internal medicine" and "nearest," and retrieves the resulting list of nearby hospitals.

[0413] Step 5:

[0414] The server filters the obtained hospital list and selects the hospital information that is most suitable for the user. Here, it performs data calculations to determine the best option for the user using past profile information and request history.

[0415] Step 6:

[0416] The device analyzes the user's emotions based on the voice data using an emotion analysis engine (e.g., EmotionRecognizer). It identifies whether the user is feeling anxious or nervous when inputting voice data. The analyzed emotion data is sent to the server.

[0417] Step 7:

[0418] The server adjusts the tone and content of the information provided based on the emotion analysis results. For example, if the user is anxious, the server will provide information in a reassuring tone. In this procedure, the format of information provided is dynamically changed based on the emotion data.

[0419] Step 8:

[0420] The adjusted information is sent to the device in text and voice format. The device displays and outputs this information to the user, providing specific guidance such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[0421] Step 9:

[0422] The server periodically generates relevant event notifications based on the user's profile information and sends them to the device. For example, it periodically provides information about local health events and workshops for the elderly. This information is also notified to the user via voice and pop-up displays.

[0423] Step 10:

[0424] The device receives the event notification sent from the server and notifies the user. Detailed information about the notified event is also provided to the user, allowing the user to ask further questions or participate in the event.

[0425] 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.

[0426] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0427] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 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.

[0431] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0432] 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.

[0433] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0434] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0435] 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.

[0436] 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.

[0437] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0438] 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.

[0439] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0440] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0441] The present invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and combines voice recognition technology, natural language analysis technology, database technology, and external API utilization technology. Specific embodiments of this system are described below.

[0442] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, consider the request "Tell me the nearest internal medicine hospital."

[0443] Voice Request Processing

[0444] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0445] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[0446] Request analysis and information acquisition

[0447] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[0448] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to receive a list of the nearest internal medicine hospitals.

[0449] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[0450] Providing information

[0451] Server: Sends filtered hospital information to the terminal.

[0452] Device: The received information is displayed to the user in voice and text format. For example, it may provide information such as, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[0453] Event notifications

[0454] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[0455] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[0456] Users: Check notifications and view details of events that interest them.

[0457] Topic Suggestion

[0458] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0459] On device: Suggested topics are presented to the user as audio or text.

[0460] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[0461] In this way, this system can improve the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together, allowing users to obtain the information they need easily and intuitively.

[0462] The processing flow will be explained below.

[0463] Step 1:

[0464] User: Logs into the system. The login screen displays fields for entering ID and password.

[0465] Step 2:

[0466] Terminal: Sends the ID and password entered by the user to the server.

[0467] Step 3:

[0468] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[0469] Step 4:

[0470] On your device: Upon successful authentication, display a custom home screen based on the user's profile information. If it's your first time logging in, display an initial setup screen and ask for personal information and topics of interest.

[0471] Step 5:

[0472] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[0473] Step 6:

[0474] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[0475] Step 7:

[0476] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[0477] Step 8:

[0478] Server: Based on the analysis results, the server identifies the user's location and the type of hospital they prefer (e.g., internal medicine).

[0479] Step 9:

[0480] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[0481] Step 10:

[0482] Server: Filters the acquired hospital information and selects the most appropriate hospital information for the user.

[0483] Step 11:

[0484] Server: Sends the selected hospital information to the terminal.

[0485] Step 12:

[0486] Device: The received information is displayed to the user in voice and text format. For example, it may say, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[0487] Step 13:

[0488] Server: Based on user profile information, collects relevant event information and generates event notifications periodically.

[0489] Step 14:

[0490] Server: Sends generated event notifications to devices.

[0491] Step 15:

[0492] Device: Notify the user of the sent event notification with sound and popup.

[0493] Step 16:

[0494] Users: Check the notified event information and click or tap to view more information.

[0495] Step 17:

[0496] Server: Analyzes the user's past request history and periodically suggests topics that may be of interest to them.

[0497] Step 18:

[0498] On the device: Suggested topics are presented to the user in audio and text format.

[0499] Step 19:

[0500] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[0501] Step 20:

[0502] Terminal: Sends the user's add request to the server.

[0503] Step 21:

[0504] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[0505] Step 22:

[0506] Device: Displays the answers obtained to the user in audio and text format.

[0507] Through this series of processes, users can easily obtain the information they need through voice or text, and receive information and event notifications to assist with their daily lives.

[0508] Example 1

[0509] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0510] There is a need for systems that allow elderly people to easily and quickly obtain the information they need in their daily lives. Previous technologies have had problems such as low accuracy in speech recognition and natural language analysis, inefficient information retrieval, and a lack of suggestions for related events and topics. This has made it difficult for elderly people to obtain the information they want in an appropriate format.

[0511] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0512] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis on the request, means for retrieving appropriate information from a storage device or an external data source based on the user's profile information and past request history, and means for providing the retrieved information to the user in voice or text format. This allows elderly people to efficiently obtain the information they need and have it provided in an appropriate format, thereby improving their quality of life. The server also includes means for receiving a user's voice request and converting it into text using a voice recognition engine, means for automatically filtering information and providing it in an optimal format, and means for suggesting topics that may be of interest, further improving the user experience.

[0513] "Speech recognition" is a technology that analyzes voice signals and converts them into text.

[0514] "Natural language analysis" is a technology that analyzes human language into a form that a computer can understand and extracts intent.

[0515] "User profile information" refers to data such as a user's personal information, settings, and past behavioral history.

[0516] "Request History" is a record of requests that a user has made to the system in the past.

[0517] "Storage device" is a general term for hardware and software used to store data.

[0518] "External data sources" refers to APIs and databases provided by third parties.

[0519] "Filtering" is the process of selecting information that matches specific conditions from the acquired information.

[0520] "Topic Suggestion" is a feature that provides themes and topics that may be of interest to users based on their past behavioral history.

[0521] "Event Notification" is a function that regularly provides users with events and information specifically aimed at seniors.

[0522] A "speech recognition engine" is a software component that recognizes speech and converts it into text.

[0523] A "natural language analysis engine" is a software component that analyzes text data and converts it into information that a computer can understand.

[0524] This invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and it combines voice recognition technology, natural language analysis technology, database and external API utilization technology. This system can be maximized by implementing it as follows.

[0525] First, a user logs into the system using a username and password. If the login is successful, a session is started and the user can start using the system.

[0526] Next, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest internal medicine hospital." When the user makes a request by voice, a speech recognition engine (e.g., Google's speech recognition technology) is used to convert the voice signal into text. The converted text is sent to the server as request data, "the nearest internal medicine hospital."

[0527] The server analyzes the received request data using a natural language analysis engine (e.g., Azure's natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's profile information and desired information (e.g., internal medicine hospitals). The server then uses a database and external data sources (e.g., local hospital information APIs) to obtain the most appropriate information. For example, it queries the hospital information API using the keywords "internal medicine" and "nearest" and receives a list of the nearest internal medicine hospitals.

[0528] The acquired information is filtered and selected in the most appropriate form for the user. The filtered information is sent from the server to the device, which then displays it to the user in voice and text format. For example, it may be provided in the form of "The nearest internal medicine hospital is XX Hospital, and its address is XX." After providing the information, the device prompts the user with an additional question: "Is there any other information you are looking for?"

[0529] The server also periodically collects relevant event information based on the user's profile information and generates event notifications tailored to the user, such as local health events and workshops for seniors. Event notifications are sent to the device and notify the user via audio and a pop-up display. The user can then view the notifications and view details of events of interest.

[0530] Additionally, the server analyzes the user's past request history and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts. These suggested topics are displayed on the device, allowing the user to ask further questions based on them. For example, they could ask, "What is the current flu outbreak situation?"

[0531] The components of the system work together to allow users to easily and intuitively obtain the information they need. For example, here is a prompt:

[0532] Example prompt: Receive a voice request saying, "What is the nearest internal medicine hospital?" and provide information about the appropriate internal medicine hospital.

[0533] In this way, this system can improve the quality of life of the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format.

[0534] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0535] Step 1: Log in

[0536] User: A user logs into the system by first entering a username and password.

[0537] Terminal: Processes the entered username and password and sends them to the server as an authentication request.

[0538] Server: Checks the received username and password against the database. If the check is successful, starts a session and returns a successful authentication message to the terminal. If the check is unsuccessful, an error message is returned.

[0539] Step 2: Accepting a voice request

[0540] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0541] Device: Receives audio through a microphone, captures it as audio data, and sends the captured audio data to a speech recognition engine.

[0542] Step 3: Voice Recognition

[0543] Terminal: The received voice data is analyzed by a voice recognition engine and converted into text. For example, the text may be "Please tell me the location of the nearest internal medicine hospital."

[0544] Terminal: The converted text is sent to the server as request data. The input data is the text converted from speech, and the output is the parsed request data.

[0545] Step 4: Request Analysis

[0546] Server: Analyzes the text data using a natural language analysis engine to understand the user's intent. The input data is the converted text, and the analysis results contain the user's intent.

[0547] Server: Based on the analysis results, retrieves the user's profile information and desired information (e.g., internal medicine hospital) from the database. The output is data containing the user's intent and related information.

[0548] Step 5: Information Acquisition

[0549] Server: Based on the analysis results, it issues a search query to an external data source (e.g., a local hospital information API). For example, it searches for hospitals based on the criteria "internal medicine" and "current location."

[0550] Server: Receives the list of nearest internal medicine hospitals returned from the API. The input is the search query, and the output is the list of hospital information retrieved.

[0551] Step 6: Information filtering

[0552] Server: Filters the received list of hospitals and selects the best hospital for the user, for example, based on reputation, distance, and opening hours.

[0553] Server: Formats the filtered information and provides it to the user. The input is the hospital information list, and the output is the filtered information.

[0554] Step 7: Provide information

[0555] Server: Sends the formatted hospital information to the terminal. The input is the filtered information, and the output is the transmitted data.

[0556] Terminal: The received information is displayed to the user as voice and text. For example, it might output "The nearest internal medicine hospital is XX Hospital, and its address is XX." The input is the transmitted data, and the output is the displayed information.

[0557] Step 8: Related Event Notifications

[0558] Server: Based on the user's profile information, collects event information that the user may be interested in. The input is the profile information, and the output is a list of related events.

[0559] Server: Organizes event information and generates optimal event notifications for users.

[0560] Terminal: Receives event notifications sent from the server and notifies the user with sound and a popup. The input is the event notification data, and the output is the notification.

[0561] Users: Check notifications and view details about events that interest them.

[0562] Step 9: Topic Proposal

[0563] Server: Analyzes the user's past request history and suggests topics they may be interested in. The input is the request history, and the output is the suggested topics.

[0564] Terminal: Presents suggested topics to the user as speech or text. The input is the suggested topic, and the output is the displayed topic.

[0565] User: Ask follow-up questions based on the suggested topic, such as "What's the current flu outbreak like?"

[0566] (Application example 1)

[0567] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0568] It is important for elderly people to easily and quickly obtain the information they need in their daily lives. However, conventional information provision systems are not specialized for the needs of elderly people, and obtaining the necessary information can require a great deal of time and effort. Furthermore, they lack the functionality to suggest meal delivery services that may be of particular interest to elderly people. Therefore, there is a need for a system that allows elderly people to easily use appropriate meal delivery services based on their preferences and history.

[0569] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0570] In this invention, the server includes means for receiving requests from users using voice recognition and performing natural language analysis of the requests, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the retrieved information to the user in voice or text, and means for proposing food delivery services based on the user's preferences and history and notifying the user of the proposed information in voice or text. This enables elderly people to quickly and easily obtain appropriate information that meets their needs, and in particular, receive suggestions regarding meal delivery services.

[0571] "Speech recognition" is a technology that converts voice data into text data.

[0572] "Natural language analysis" is a technology that converts the natural language used by humans on a daily basis into a format that is easy for computers to understand and analyzes its meaning.

[0573] "Profile Information" refers to information that includes a user's personal data, preferences, and past activity history.

[0574] An "external API" is a standardized interface for connecting with other services and databases.

[0575] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0576] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a designated location such as your home.

[0577] "Audio notifications" are a means of conveying information to the user in audio form.

[0578] A "text notification" is a means of conveying information to a user in written form.

[0579] "Event Notification" is a method of periodically notifying users of events and activities that are relevant to them.

[0580] The system of this invention is designed to enable elderly people to efficiently and easily obtain information necessary for daily life. The system combines voice recognition technology, natural language analysis technology, a database, an external API, and a food delivery service suggestion function. A specific embodiment of this system is described below.

[0581] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, the user can request "Tell me the nearest sushi restaurant."

[0582] Voice Request Processing

[0583] User: The user makes a request by voice.

[0584] Device: Converts the voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The converted text is sent to the server as request data for "nearest sushi restaurant."

[0585] Request analysis and information acquisition

[0586] Server: Analyzes the received request data using a natural language analysis engine (e.g., NLPProcessor) to understand the user's intent. Based on the analysis results, the user's location information and desired service (sushi) are identified.

[0587] Server: Uses a database and external APIs (e.g., Zomato API or Uber Eats API) to obtain the best sushi restaurant information. For example, make a query using the keywords "sushi" and "nearest" and receive a list of the nearest sushi restaurants.

[0588] Server: Filters the acquired sushi restaurant information and selects the most suitable service for the user.

[0589] Providing information

[0590] Server: Sends filtered service information to the terminal.

[0591] Device: The received information is displayed to the user in voice and text format. For example, it might provide information such as, "The nearest sushi restaurant is XX Sushi, and its address is XX."

[0592] Event notifications

[0593] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[0594] Terminal: Receives event notifications sent from the server and notifies the user in the form of sound and popup display.

[0595] Topic Suggestion

[0596] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0597] On the device: Suggested topics are presented to the user as audio and text.

[0598] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[0599] This system improves the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together to enable users to obtain the information they need easily and intuitively.

[0600] Prompt Sentence Examples

[0601] A user makes a voice request saying, "I'd like to order sushi." Use natural language analysis and a food delivery API to find the nearest recommended sushi restaurant and generate a response like this: "1. Sushi XX, address: XX. 2. Sushi △△, address: □□."

[0602] User location: Chuo-ku, Tokyo

[0603] Food preference: Sushi

[0604] Available APIs: Zomato, UberEats

[0605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0606] Step 1:

[0607] A user logs in to the system. After logging in, they request specific information by voice or text. For example, the user might say, "Tell me the nearest sushi restaurant." The input is the user's voice data, and the output is digital voice data.

[0608] Step 2:

[0609] The device receives the user's voice data and converts it into text data using a speech recognition engine (for example, Google Speech-to-Text API). Specifically, the device sends the voice data to the speech recognition API and receives the text data "nearest sushi restaurant" as the conversion result. The input is digital voice data and the output is text data.

[0610] Step 3:

[0611] The server receives the request data sent from the device and analyzes it using a natural language analysis engine (e.g., NLPProcessor). Through this analysis, it understands the user's intent and converts a request such as "Tell me the nearest sushi restaurant" into a specific search query. The input is text data, and the output is the analyzed query data.

[0612] Step 4:

[0613] The server uses the user's location and preference information to send a search query to a database or external API (e.g., Zomato API or Uber Eats API). For example, a query is made using the keywords "sushi" and "nearest." The input is the query data, and the output is a list of services (sushi restaurants) provided.

[0614] Step 5:

[0615] The server filters the acquired service information and selects the sushi restaurant that best suits the user. Specifically, it selects the optimal service based on the user's past history and profile information. The input is the list data of sushi restaurants and the user profile data, and the output is the filtered sushi restaurant data.

[0616] Step 6:

[0617] The server sends the filtered sushi restaurant information to the terminal. It uses REST API as the transmission protocol to transfer data to the terminal. The input is the filtered sushi restaurant data, and the output is the data sent to the terminal.

[0618] Step 7:

[0619] The device receives the sushi restaurant information and displays it to the user in voice and text. It uses a voice output engine (Text-to-Speech TTS engine, such as Google Text-to-Speech API) to provide the information in voice, while simultaneously displaying text information on the screen. The input is the data sent to the device, and the output is voice and text information.

[0620] Step 8:

[0621] The server periodically collects relevant event information based on the user's profile information and generates optimal event notifications for the user, such as local health events or workshops for the elderly. The input is user profile data and event data, and the output is event notification data.

[0622] Step 9:

[0623] The terminal receives the event notification sent from the server and notifies the user. The notification is in the form of voice and a pop-up display. The input is the event notification data, and the output is voice and text notification.

[0624] Step 10:

[0625] The server analyzes the user's past request history and periodically suggests topics that the user may be interested in, such as the latest health news or weather forecasts. The input is the request history data, and the output is the suggested topic data.

[0626] Step 11:

[0627] The terminal displays the suggested topics to the user by voice and text. The input is the suggested topic data, and the output is the voice and text display.

[0628] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0629] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[0630] First, the user logs into the system. After logging in, the user can request specific information by voice or text, for example, "Tell me the nearest hospital that has an internal medicine department."

[0631] Voice Request Processing

[0632] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0633] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[0634] Request analysis and information acquisition

[0635] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[0636] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[0637] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[0638] Sentiment analysis and information provision

[0639] On the device: The emotion analysis engine uses the voice data collected during the voice request to analyze the user's emotions. For example, if the user is feeling anxious, that emotion will be analyzed.

[0640] Server: Receives emotional data from the sentiment analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, the server will provide reassuring explanations in a more friendly tone.

[0641] Server: Sends the adjusted information to the device.

[0642] Device: The received information is displayed to the user in voice and text. For example, it may say, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[0643] Event notifications

[0644] Server: Based on the user's profile information, the server periodically collects relevant event information and generates appropriate event notifications for the user, such as local health events or workshops for seniors.

[0645] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[0646] Users: Check notifications and view details of events that interest them.

[0647] Topic Suggestion

[0648] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0649] On device: Suggested topics are presented to the user as audio or text.

[0650] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[0651] Use of Emotional Data

[0652] Server: The emotion data recognized by the emotion analysis engine is added to the user's profile information, allowing the emotion data to be reflected in future information and topic suggestions, providing a more personalized service.

[0653] In this way, this system analyzes the user's voice request, determines their emotions, and provides appropriate information, thereby meeting the needs of the elderly while also providing a service that takes their emotions into consideration.The various components of the system work together, allowing users to obtain the information they need with peace of mind.

[0654] The processing flow will be explained below.

[0655] Step 1:

[0656] User: Logs into the system. The login screen displays fields for entering ID and password.

[0657] Step 2:

[0658] Terminal: Sends the ID and password entered by the user to the server.

[0659] Step 3:

[0660] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[0661] Step 4:

[0662] Device: Upon successful authentication, a custom home screen based on the user's profile information is displayed. If it is the first time the user is logged in, an initial setup screen is displayed, prompting the user to enter personal information and topics of interest, if necessary.

[0663] Step 5:

[0664] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[0665] Step 6:

[0666] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[0667] Step 7:

[0668] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[0669] Step 8:

[0670] Server: Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) desired.

[0671] Step 9:

[0672] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[0673] Step 10:

[0674] Server: Filters the acquired hospital information and selects the hospital information that is most appropriate for the user.

[0675] Step 11:

[0676] On the device: Using voice data collected simultaneously with the voice request, an emotion analysis engine analyzes the user's emotions, for example, determining whether the user is feeling anxious.

[0677] Step 12:

[0678] Server: Receives emotional data from sentiment analysis and adjusts the tone and content of the information provided. For example, if the user is feeling anxious, it generates a more friendly and reassuring explanation.

[0679] Step 13:

[0680] Server: Sends the adjusted information to the device.

[0681] Step 14:

[0682] Device: Presents the received information to the user via voice and text. For example, it might say, "The nearest internal medicine hospital is XX Hospital, its address is XX. This hospital has a good reputation and is safe."

[0683] Step 15:

[0684] Server: Collects event information of interest based on user profile information and generates event notifications periodically.

[0685] Step 16:

[0686] Server: Sends generated event notifications to devices.

[0687] Step 17:

[0688] Device: Notify users of event notifications with sound and popup.

[0689] Step 18:

[0690] Users: Check the event information they are notified about and click or tap to view details about the event they are interested in.

[0691] Step 19:

[0692] Server: Analyzes the user's past request history and suggests topics that may be of interest to them.

[0693] Step 20:

[0694] On the device: Suggested topics are presented to the user in audio and text format.

[0695] Step 21:

[0696] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[0697] Step 22:

[0698] Terminal: Sends the user's add request to the server.

[0699] Step 23:

[0700] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[0701] Step 24:

[0702] Device: Displays the answers obtained to the user in audio and text format.

[0703] This series of processes allows users to easily obtain the information they need through voice or text, and allows them to receive emotionally sensitive services with peace of mind.

[0704] Example 2

[0705] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0706] Elderly people face challenges in efficiently and easily obtaining the information they need in their daily lives. Furthermore, conventional information provision systems do not provide information tailored to the user's emotions, making it difficult for users to understand the information and feel reassured. Furthermore, the need for suggesting related information based on past request history and providing filtered, accurate information is also a problem.

[0707] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0708] In this invention, the server includes: means for receiving a request from a user through speech recognition and converting the request into text; means for analyzing the converted text through natural language analysis to understand the user's intent; means for retrieving appropriate information from a database or external information source based on the user's profile information and past request history; means for filtering the retrieved information and providing it in a format optimal for the user; means for performing emotion analysis using the retrieved voice data; means for adjusting the tone and content of the information provided based on the analyzed emotion; and means for periodically generating information and event notifications for elderly users and notifying them of the information. This not only enables elderly users to efficiently and easily obtain the information they need, but also provides information that takes the user's emotions into consideration and gives them peace of mind. It also enables suggestions for related information and the provision of accurate information.

[0709] "Speech recognition" is a technology that analyzes voice signals and converts their contents into text data.

[0710] "Natural language analysis" is a technology that analyzes text data to understand its meaning and intent.

[0711] "Profile information" is data that records a user's basic information, past behavioral history, interests, etc.

[0712] An "external information source" is a resource used to obtain information, such as a database or API that exists outside the system.

[0713] "Filtering" is the process of selecting necessary information from acquired information based on specific conditions.

[0714] "Emotion analysis" is a technology that analyzes voice and text data to identify a user's emotional state.

[0715] "Tone" refers to the tone and attitude of the voice or writing used to provide information.

[0716] "Event Notifications" is a feature that notifies users of information about events and activities that may be of interest to them.

[0717] "Information provision" refers to the act of displaying or transmitting audio data necessary in response to a user's request.

[0718] "Related information" is topics and data suggested to you based on your interests and past requests.

[0719] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[0720] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest hospital that has an internal medicine department."

[0721] In the case of a voice request, the user makes a voice request such as "Tell me the nearest internal medicine hospital." The device receives the user's voice and converts it into text using a voice recognition engine (e.g., general voice recognition technology). The converted text is sent to the server as request data, "The nearest internal medicine hospital."

[0722] The server analyzes the received request data using a natural language analysis engine (e.g., general natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's location information and the type of hospital (internal medicine) desired. The server then uses a database and external API to obtain the most suitable hospital information. For example, it queries the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[0723] Next, the server filters the acquired hospital information and selects the most suitable hospital for the user. The device uses an emotion analysis engine (e.g., general emotion analysis technology) to analyze the user's emotions using the voice data collected during the voice request. The server receives the emotion data from the emotion analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, it will provide a more reassuring explanation in a more friendly tone. The adjusted information is sent to the device, which then displays the received information to the user in voice and text. Specifically, it provides information such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This is a safe hospital with a good reputation."

[0724] Furthermore, the system periodically collects relevant event information based on the user's profile information and generates event notifications appropriate for the user. For example, local health events or workshops for the elderly are targeted. The server updates this event information as needed, and the device provides notifications to the user in the form of audio and pop-up displays. The user can check the notifications and view details of events of interest.

[0725] The device also analyzes the user's past request history and periodically suggests topics that may be of interest to the user. For example, it suggests the latest health news or weather forecasts. The device displays the suggested topics to the user via voice or text, and the user can ask further questions based on the suggested topics. For example, a question could be, "What is the current flu epidemic situation?"

[0726] Emotional data recognized by the emotion analysis engine is added to the user's profile information and reflected in future information and topic suggestions, allowing for a more personalized service.

[0727] This system allows elderly people to obtain the information they need efficiently and easily, and provides information that is considerate of their emotions and gives them a sense of security. Below are some examples of prompts:

[0728] Example prompt:

[0729] User: "What's the nearest internal medicine hospital?"

[0730] Terminal: Receives voice and converts it into text (general voice recognition technology)

[0731] Server: Receives and analyzes the request text (general natural language analysis technology)

[0732] Server: Based on the analysis results, queries the local hospital information API and obtains the most appropriate hospital information.

[0733] Server: Analyzes the user's emotions and adjusts the tone of the information provided based on their emotions (general emotion analysis technology).

[0734] Device: Provides tailored information to the user via voice and text.

[0735] In this way, each component of the system works in cooperation with each other, allowing users to obtain the information they need with peace of mind.

[0736] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0737] Step 1:

[0738] Input: A voice request from the user (e.g., "What is the nearest internal medicine hospital?")

[0739] How it works: The user makes a verbal request: "Tell me the nearest internal medicine hospital."

[0740] Output: User's voice data

[0741] Step 2:

[0742] Input: User's voice data

[0743] How it works: The device receives the user's speech and converts it into text using a speech recognition engine. Specifically, it sends the speech data to a speech recognition API such as Google Cloud Speech-to-Text and retrieves the text data in return.

[0744] Output: Text data (e.g., "What is the nearest hospital for internal medicine?")

[0745] Step 3:

[0746] Input: Text data

[0747] Operation: The terminal sends the converted text data to the server.

[0748] Output: Text data is sent to the server

[0749] Step 4:

[0750] Input: Text data (on the server)

[0751] How it works: The server analyzes the text data using a natural language analysis engine (e.g., Google Cloud Natural Language API) to understand the user's intent. Specifically, it sends the text data to the analysis engine's API and obtains the user's intent and request as the analysis result.

[0752] Output: User intent and request (e.g., "Hospital for internal medicine" "Nearest")

[0753] Step 5:

[0754] Input: User intent and request

[0755] Operation: The server obtains the user's location information and uses the hospital information API to obtain information on the nearest internal medicine hospital. Specifically, it queries the API using keywords such as "internal medicine" and "nearest" and obtains a list of internal medicine hospitals.

[0756] Output: List of nearest internal medicine hospitals

[0757] Step 6:

[0758] Input: List of nearest internal medicine hospitals

[0759] How it works: The server filters the hospital information it obtains and selects the hospital that best suits the user. Filtering criteria include distance, reputation, and opening hours.

[0760] Output: Information on the best hospital (e.g., "Hospital X, address XX, good reputation")

[0761] Step 7:

[0762] Input: User's voice data

[0763] How it works: The device sends the voice data collected during a voice request to an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. The analysis results include the user's feelings of anxiety, relief, or confusion.

[0764] Output: User's emotion data (e.g., "anxiety")

[0765] Step 8:

[0766] Input: Optimal hospital information, user sentiment data

[0767] How it works: The server adjusts the tone and content of the information it provides based on the emotional data. For example, if a user feels anxious, it will provide information in a reassuring tone.

[0768] Output: Tailored information (e.g., "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe.")

[0769] Step 9:

[0770] Input: Adjusted information

[0771] How it works: The server sends the tailored information to the device, which then presents it to the user in voice and text.

[0772] Output: Information sent from the server to the device

[0773] Step 10:

[0774] Input: Information sent from the server to the device

[0775] Operation: The device displays the received information to the user in voice and text. The user receives information such as "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[0776] Output: Information provided to the user

[0777] (Application example 2)

[0778] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0779] It is important for elderly people to efficiently and easily obtain the information they need in their daily lives, but this is often technically difficult to achieve. Furthermore, information provision systems rarely respond in a way that takes into account the emotions and psychological state of elderly people, which can increase anxiety and stress. Furthermore, as the use of self-driving vehicles increases, there is a lack of support for elderly people to use self-driving vehicles safely. Therefore, there is a need for a system that allows elderly people to easily obtain information and provides support that takes their emotions into consideration.

[0780] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0781] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis of the request, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the information to the user in voice or text, and means for analyzing emotions using the voice data and adjusting the tone and content of the information provided based on the analysis results. This enables elderly people to obtain the information they need with peace of mind when using an autonomous vehicle and receive responses that take their emotions into consideration.

[0782] "Speech recognition" is a technology that analyzes speech as digital data and converts the content into text.

[0783] "Request" means a request for specific information or services sought by a User.

[0784] "Natural language analysis" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[0785] "Profile information" is data that compiles information about a user, such as personal information, usage history, and interests.

[0786] An "external API" is a program interface for connecting with external systems and services.

[0787] A "database" is a system that efficiently stores and manages large amounts of data and allows it to be quickly retrieved when needed.

[0788] "Voice provision" refers to the means of conveying acquired information to the user by voice.

[0789] "Text provision" refers to the means of conveying acquired information to users in text.

[0790] "Emotion analysis" is a technology that infers and analyzes a user's emotional state from data such as voice and text.

[0791] "Tone" refers to the manner and tone of speech used when providing information, and should be adjusted according to the user's emotions.

[0792] "Information for elderly users" refers to information on medical care, welfare, and lifestyle support that is particularly needed by the elderly.

[0793] "Event Notification" is a system that notifies users in advance of relevant events and important announcements.

[0794] "Filtering" is the process of narrowing down the information obtained to what is useful to the user.

[0795] "Personalized services" refer to information and services that are optimized for specific users based on their profile information and emotional data.

[0796] An "autonomous vehicle" is a vehicle equipped with technology that allows it to drive automatically without the involvement of a driver.

[0797] This invention aims to provide a system that allows elderly people to efficiently and easily obtain the information they need when using self-driving vehicles. Furthermore, by adjusting the tone and content of the information provided based on emotion analysis, it aims to create an environment where elderly people can use vehicles with peace of mind.

[0798] First, the user makes a request to the terminal in the vehicle through the voice recognition system. For example, they might say, "Tell me where the nearest hospital is." The terminal converts this voice data into a digital signal, which is then converted into text using a voice recognition engine (for example, Google's voice recognition API). The converted text is sent to the server as request data for "nearest hospital."

[0799] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Based on this analysis, it understands the user's intent and then references the user's profile information and past request history to retrieve appropriate information from a database or external API (e.g., a local hospital information API). The retrieved information could be a list of the nearest hospitals, their detailed addresses, ratings, etc.

[0800] Next, an emotion analysis engine (e.g., EmotionRecognizer) installed on the device is used to analyze the emotions felt by the user when they input their voice. Specifically, anxiety or tension is detected from the voice data. This emotion data is sent to the server, which then adjusts the tone and content of the information provided. For example, if the user is feeling anxious, the server will provide information in a friendly tone, such as, "Don't worry. The nearest hospital is ____. It has a good reputation and is a safe place."

[0801] The adjusted information is then provided to the user via the terminal in the form of voice and text, and the user can follow the voice prompts to find the nearest hospital.

[0802] Furthermore, the server periodically generates relevant event notifications based on the user's profile information and sends them to the device, such as local health events or workshops for seniors. The device then notifies the user of these notifications in the form of voice and pop-up displays.

[0803] As a concrete example, if an elderly person requests, "Tell me the nearest cafe," the server will analyze the request using a natural language analysis engine and retrieve the most suitable cafe information from an external API based on the profile information and past history. At the same time, if the emotion analysis engine detects anxiety from the voice data, the server will provide information in a reassuring tone, such as, "Don't worry, this is a cafe with a good reputation."

[0804] The following are examples of prompts to aid in implementation:

[0805] "You will create a program that responds to the user's voice request with appropriate information. This includes voice recognition, emotion analysis, and location information acquisition."

[0806] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0807] Step 1:

[0808] The user inputs a voice request into the terminal, including specific information such as "Tell me where the nearest hospital is." The input voice is converted into digital data.

[0809] Step 2:

[0810] The device uses a speech recognition engine (for example, Google's speech recognition API) to convert the voice data into text data. The converted text data becomes request data such as "nearest hospital" and is sent to the server. Here, the voice data is converted into text data and sent as a result of processing, resulting in text data.

[0811] Step 3:

[0812] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Through analysis, it understands the details of the information the user is looking for (e.g., the category "hospitals" and the request "nearest"). During this analysis process, the text data is converted into data that indicates the user's intent.

[0813] Step 4:

[0814] The server references the user's profile information and past request history to retrieve the appropriate information. This may involve querying a database or an external API (e.g., a local hospital information API). Specifically, the server queries the database for the keywords "internal medicine" and "nearest," and retrieves the resulting list of nearby hospitals.

[0815] Step 5:

[0816] The server filters the obtained hospital list and selects the hospital information that is most suitable for the user. Here, it performs data calculations to determine the best option for the user using past profile information and request history.

[0817] Step 6:

[0818] The device analyzes the user's emotions based on the voice data using an emotion analysis engine (e.g., EmotionRecognizer). It identifies whether the user is feeling anxious or nervous when inputting voice data. The analyzed emotion data is sent to the server.

[0819] Step 7:

[0820] The server adjusts the tone and content of the information provided based on the emotion analysis results. For example, if the user is anxious, the server will provide information in a reassuring tone. In this procedure, the format of information provided is dynamically changed based on the emotion data.

[0821] Step 8:

[0822] The adjusted information is sent to the device in text and voice format. The device displays and outputs this information to the user, providing specific guidance such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[0823] Step 9:

[0824] The server periodically generates relevant event notifications based on the user's profile information and sends them to the device. For example, it periodically provides information about local health events and workshops for the elderly. This information is also notified to the user via voice and pop-up displays.

[0825] Step 10:

[0826] The device receives the event notification sent from the server and notifies the user. Detailed information about the notified event is also provided to the user, allowing the user to ask further questions or participate in the event.

[0827] 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.

[0828] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0829] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0830] [Third embodiment]

[0831] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0832] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0833] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0834] 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.

[0835] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0836] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0837] 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.

[0838] 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.

[0839] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0840] 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.

[0841] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0842] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0843] The present invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and combines voice recognition technology, natural language analysis technology, database technology, and external API utilization technology. Specific embodiments of this system are described below.

[0844] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, consider the request "Tell me the nearest internal medicine hospital."

[0845] Voice Request Processing

[0846] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0847] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[0848] Request analysis and information acquisition

[0849] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[0850] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to receive a list of the nearest internal medicine hospitals.

[0851] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[0852] Providing information

[0853] Server: Sends filtered hospital information to the terminal.

[0854] Device: The received information is displayed to the user in voice and text format. For example, it may provide information such as, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[0855] Event notifications

[0856] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[0857] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[0858] Users: Check notifications and view details of events that interest them.

[0859] Topic Suggestion

[0860] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0861] On device: Suggested topics are presented to the user as audio or text.

[0862] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[0863] In this way, this system can improve the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together, allowing users to obtain the information they need easily and intuitively.

[0864] The processing flow will be explained below.

[0865] Step 1:

[0866] User: Logs into the system. The login screen displays fields for entering ID and password.

[0867] Step 2:

[0868] Terminal: Sends the ID and password entered by the user to the server.

[0869] Step 3:

[0870] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[0871] Step 4:

[0872] On your device: Upon successful authentication, display a custom home screen based on the user's profile information. If it's your first time logging in, display an initial setup screen and ask for personal information and topics of interest.

[0873] Step 5:

[0874] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[0875] Step 6:

[0876] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[0877] Step 7:

[0878] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[0879] Step 8:

[0880] Server: Based on the analysis results, the server identifies the user's location and the type of hospital they prefer (e.g., internal medicine).

[0881] Step 9:

[0882] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[0883] Step 10:

[0884] Server: Filters the acquired hospital information and selects the most appropriate hospital information for the user.

[0885] Step 11:

[0886] Server: Sends the selected hospital information to the terminal.

[0887] Step 12:

[0888] Device: The received information is displayed to the user in voice and text format. For example, it may say, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[0889] Step 13:

[0890] Server: Based on user profile information, collects relevant event information and generates event notifications periodically.

[0891] Step 14:

[0892] Server: Sends generated event notifications to devices.

[0893] Step 15:

[0894] Device: Notify the user of the sent event notification with sound and popup.

[0895] Step 16:

[0896] Users: Check the notified event information and click or tap to view more information.

[0897] Step 17:

[0898] Server: Analyzes the user's past request history and periodically suggests topics that may be of interest to them.

[0899] Step 18:

[0900] On the device: Suggested topics are presented to the user in audio and text format.

[0901] Step 19:

[0902] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[0903] Step 20:

[0904] Terminal: Sends the user's add request to the server.

[0905] Step 21:

[0906] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[0907] Step 22:

[0908] Device: Displays the answers obtained to the user in audio and text format.

[0909] Through this series of processes, users can easily obtain the information they need through voice or text, and receive information and event notifications to assist with their daily lives.

[0910] Example 1

[0911] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0912] There is a need for systems that allow elderly people to easily and quickly obtain the information they need in their daily lives. Previous technologies have had problems such as low accuracy in speech recognition and natural language analysis, inefficient information retrieval, and a lack of suggestions for related events and topics. This has made it difficult for elderly people to obtain the information they want in an appropriate format.

[0913] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0914] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis on the request, means for retrieving appropriate information from a storage device or an external data source based on the user's profile information and past request history, and means for providing the retrieved information to the user in voice or text format. This allows elderly people to efficiently obtain the information they need and have it provided in an appropriate format, thereby improving their quality of life. The server also includes means for receiving a user's voice request and converting it into text using a voice recognition engine, means for automatically filtering information and providing it in an optimal format, and means for suggesting topics that may be of interest, further improving the user experience.

[0915] "Speech recognition" is a technology that analyzes voice signals and converts them into text.

[0916] "Natural language analysis" is a technology that analyzes human language into a form that a computer can understand and extracts intent.

[0917] "User profile information" refers to data such as a user's personal information, settings, and past behavioral history.

[0918] "Request History" is a record of requests that a user has made to the system in the past.

[0919] "Storage device" is a general term for hardware and software used to store data.

[0920] "External data sources" refers to APIs and databases provided by third parties.

[0921] "Filtering" is the process of selecting information that matches specific conditions from the acquired information.

[0922] "Topic Suggestion" is a feature that provides themes and topics that may be of interest to users based on their past behavioral history.

[0923] "Event Notification" is a function that regularly provides users with events and information specifically aimed at seniors.

[0924] A "speech recognition engine" is a software component that recognizes speech and converts it into text.

[0925] A "natural language analysis engine" is a software component that analyzes text data and converts it into information that a computer can understand.

[0926] This invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and it combines voice recognition technology, natural language analysis technology, database and external API utilization technology. This system can be maximized by implementing it as follows.

[0927] First, a user logs into the system using a username and password. If the login is successful, a session is started and the user can start using the system.

[0928] Next, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest internal medicine hospital." When the user makes a request by voice, a speech recognition engine (e.g., Google's speech recognition technology) is used to convert the voice signal into text. The converted text is sent to the server as request data, "the nearest internal medicine hospital."

[0929] The server analyzes the received request data using a natural language analysis engine (e.g., Azure's natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's profile information and desired information (e.g., internal medicine hospitals). The server then uses a database and external data sources (e.g., local hospital information APIs) to obtain the most appropriate information. For example, it queries the hospital information API using the keywords "internal medicine" and "nearest" and receives a list of the nearest internal medicine hospitals.

[0930] The acquired information is filtered and selected in the most appropriate form for the user. The filtered information is sent from the server to the device, which then displays it to the user in voice and text format. For example, it may be provided in the form of "The nearest internal medicine hospital is XX Hospital, and its address is XX." After providing the information, the device prompts the user with an additional question: "Is there any other information you are looking for?"

[0931] The server also periodically collects relevant event information based on the user's profile information and generates event notifications tailored to the user, such as local health events and workshops for seniors. Event notifications are sent to the device and notify the user via audio and a pop-up display. The user can then view the notifications and view details of events of interest.

[0932] Additionally, the server analyzes the user's past request history and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts. These suggested topics are displayed on the device, allowing the user to ask further questions based on them. For example, they could ask, "What is the current flu outbreak situation?"

[0933] The components of the system work together to allow users to easily and intuitively obtain the information they need. For example, here is a prompt:

[0934] Example prompt: Receive a voice request saying, "What is the nearest internal medicine hospital?" and provide information about the appropriate internal medicine hospital.

[0935] In this way, this system can improve the quality of life of the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format.

[0936] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0937] Step 1: Log in

[0938] User: A user logs into the system by first entering a username and password.

[0939] Terminal: Processes the entered username and password and sends them to the server as an authentication request.

[0940] Server: Checks the received username and password against the database. If the check is successful, starts a session and returns a successful authentication message to the terminal. If the check is unsuccessful, an error message is returned.

[0941] Step 2: Accepting a voice request

[0942] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[0943] Device: Receives audio through a microphone, captures it as audio data, and sends the captured audio data to a speech recognition engine.

[0944] Step 3: Voice Recognition

[0945] Terminal: The received voice data is analyzed by a voice recognition engine and converted into text. For example, the text may be "Please tell me the location of the nearest internal medicine hospital."

[0946] Terminal: The converted text is sent to the server as request data. The input data is the text converted from speech, and the output is the parsed request data.

[0947] Step 4: Request Analysis

[0948] Server: Analyzes the text data using a natural language analysis engine to understand the user's intent. The input data is the converted text, and the analysis results contain the user's intent.

[0949] Server: Based on the analysis results, retrieves the user's profile information and desired information (e.g., internal medicine hospital) from the database. The output is data containing the user's intent and related information.

[0950] Step 5: Information Acquisition

[0951] Server: Based on the analysis results, it issues a search query to an external data source (e.g., a local hospital information API). For example, it searches for hospitals based on the criteria "internal medicine" and "current location."

[0952] Server: Receives the list of nearest internal medicine hospitals returned from the API. The input is the search query, and the output is the list of hospital information retrieved.

[0953] Step 6: Information filtering

[0954] Server: Filters the received list of hospitals and selects the best hospital for the user, for example, based on reputation, distance, and opening hours.

[0955] Server: Formats the filtered information and provides it to the user. The input is the hospital information list, and the output is the filtered information.

[0956] Step 7: Provide information

[0957] Server: Sends the formatted hospital information to the terminal. The input is the filtered information, and the output is the transmitted data.

[0958] Terminal: The received information is displayed to the user as voice and text. For example, it might output "The nearest internal medicine hospital is XX Hospital, and its address is XX." The input is the transmitted data, and the output is the displayed information.

[0959] Step 8: Related Event Notifications

[0960] Server: Based on the user's profile information, collects event information that the user may be interested in. The input is the profile information, and the output is a list of related events.

[0961] Server: Organizes event information and generates optimal event notifications for users.

[0962] Terminal: Receives event notifications sent from the server and notifies the user with sound and a popup. The input is the event notification data, and the output is the notification.

[0963] Users: Check notifications and view details about events that interest them.

[0964] Step 9: Topic Proposal

[0965] Server: Analyzes the user's past request history and suggests topics they may be interested in. The input is the request history, and the output is the suggested topics.

[0966] Terminal: Presents suggested topics to the user as speech or text. The input is the suggested topic, and the output is the displayed topic.

[0967] User: Ask follow-up questions based on the suggested topic, such as "What's the current flu outbreak like?"

[0968] (Application example 1)

[0969] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0970] It is important for elderly people to easily and quickly obtain the information they need in their daily lives. However, conventional information provision systems are not specialized for the needs of elderly people, and obtaining the necessary information can require a great deal of time and effort. Furthermore, they lack the functionality to suggest meal delivery services that may be of particular interest to elderly people. Therefore, there is a need for a system that allows elderly people to easily use appropriate meal delivery services based on their preferences and history.

[0971] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0972] In this invention, the server includes means for receiving requests from users using voice recognition and performing natural language analysis of the requests, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the retrieved information to the user in voice or text, and means for proposing food delivery services based on the user's preferences and history and notifying the user of the proposed information in voice or text. This enables elderly people to quickly and easily obtain appropriate information that meets their needs, and in particular, receive suggestions regarding meal delivery services.

[0973] "Speech recognition" is a technology that converts voice data into text data.

[0974] "Natural language analysis" is a technology that converts the natural language used by humans on a daily basis into a format that is easy for computers to understand and analyzes its meaning.

[0975] "Profile Information" refers to information that includes a user's personal data, preferences, and past activity history.

[0976] An "external API" is a standardized interface for connecting with other services and databases.

[0977] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0978] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a designated location such as your home.

[0979] "Audio notifications" are a means of conveying information to the user in audio form.

[0980] A "text notification" is a means of conveying information to a user in written form.

[0981] "Event Notification" is a method of periodically notifying users of events and activities that are relevant to them.

[0982] The system of this invention is designed to enable elderly people to efficiently and easily obtain information necessary for daily life. The system combines voice recognition technology, natural language analysis technology, a database, an external API, and a food delivery service suggestion function. A specific embodiment of this system is described below.

[0983] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, the user can request "Tell me the nearest sushi restaurant."

[0984] Voice Request Processing

[0985] User: The user makes a request by voice.

[0986] Device: Converts the voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The converted text is sent to the server as request data for "nearest sushi restaurant."

[0987] Request analysis and information acquisition

[0988] Server: Analyzes the received request data using a natural language analysis engine (e.g., NLPProcessor) to understand the user's intent. Based on the analysis results, the user's location information and desired service (sushi) are identified.

[0989] Server: Uses a database and external APIs (e.g., Zomato API or Uber Eats API) to obtain the best sushi restaurant information. For example, make a query using the keywords "sushi" and "nearest" and receive a list of the nearest sushi restaurants.

[0990] Server: Filters the acquired sushi restaurant information and selects the most suitable service for the user.

[0991] Providing information

[0992] Server: Sends filtered service information to the terminal.

[0993] Device: The received information is displayed to the user in voice and text format. For example, it might provide information such as, "The nearest sushi restaurant is XX Sushi, and its address is XX."

[0994] Event notifications

[0995] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[0996] Terminal: Receives event notifications sent from the server and notifies the user in the form of sound and popup display.

[0997] Topic Suggestion

[0998] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[0999] On the device: Suggested topics are presented to the user as audio and text.

[1000] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[1001] This system improves the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together to enable users to obtain the information they need easily and intuitively.

[1002] Prompt Sentence Examples

[1003] A user makes a voice request saying, "I'd like to order sushi." Use natural language analysis and a food delivery API to find the nearest recommended sushi restaurant and generate a response like this: "1. Sushi XX, address: XX. 2. Sushi △△, address: □□."

[1004] User location: Chuo-ku, Tokyo

[1005] Food preference: Sushi

[1006] Available APIs: Zomato, UberEats

[1007] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1008] Step 1:

[1009] A user logs in to the system. After logging in, they request specific information by voice or text. For example, the user might say, "Tell me the nearest sushi restaurant." The input is the user's voice data, and the output is digital voice data.

[1010] Step 2:

[1011] The device receives the user's voice data and converts it into text data using a speech recognition engine (for example, Google Speech-to-Text API). Specifically, the device sends the voice data to the speech recognition API and receives the text data "nearest sushi restaurant" as the conversion result. The input is digital voice data and the output is text data.

[1012] Step 3:

[1013] The server receives the request data sent from the device and analyzes it using a natural language analysis engine (e.g., NLPProcessor). Through this analysis, it understands the user's intent and converts a request such as "Tell me the nearest sushi restaurant" into a specific search query. The input is text data, and the output is the analyzed query data.

[1014] Step 4:

[1015] The server uses the user's location and preference information to send a search query to a database or external API (e.g., Zomato API or Uber Eats API). For example, a query is made using the keywords "sushi" and "nearest." The input is the query data, and the output is a list of services (sushi restaurants) provided.

[1016] Step 5:

[1017] The server filters the acquired service information and selects the sushi restaurant that best suits the user. Specifically, it selects the optimal service based on the user's past history and profile information. The input is the list data of sushi restaurants and the user profile data, and the output is the filtered sushi restaurant data.

[1018] Step 6:

[1019] The server sends the filtered sushi restaurant information to the terminal. It uses REST API as the transmission protocol to transfer data to the terminal. The input is the filtered sushi restaurant data, and the output is the data sent to the terminal.

[1020] Step 7:

[1021] The device receives the sushi restaurant information and displays it to the user in voice and text. It uses a voice output engine (Text-to-Speech TTS engine, such as Google Text-to-Speech API) to provide the information in voice, while simultaneously displaying text information on the screen. The input is the data sent to the device, and the output is voice and text information.

[1022] Step 8:

[1023] The server periodically collects relevant event information based on the user's profile information and generates optimal event notifications for the user, such as local health events or workshops for the elderly. The input is user profile data and event data, and the output is event notification data.

[1024] Step 9:

[1025] The terminal receives the event notification sent from the server and notifies the user. The notification is in the form of voice and a pop-up display. The input is the event notification data, and the output is voice and text notification.

[1026] Step 10:

[1027] The server analyzes the user's past request history and periodically suggests topics that the user may be interested in, such as the latest health news or weather forecasts. The input is the request history data, and the output is the suggested topic data.

[1028] Step 11:

[1029] The terminal displays the suggested topics to the user by voice and text. The input is the suggested topic data, and the output is the voice and text display.

[1030] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1031] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[1032] First, the user logs into the system. After logging in, the user can request specific information by voice or text, for example, "Tell me the nearest hospital that has an internal medicine department."

[1033] Voice Request Processing

[1034] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[1035] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[1036] Request analysis and information acquisition

[1037] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[1038] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[1039] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[1040] Sentiment analysis and information provision

[1041] On the device: The emotion analysis engine uses the voice data collected during the voice request to analyze the user's emotions. For example, if the user is feeling anxious, that emotion will be analyzed.

[1042] Server: Receives emotional data from the sentiment analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, the server will provide reassuring explanations in a more friendly tone.

[1043] Server: Sends the adjusted information to the device.

[1044] Device: The received information is displayed to the user in voice and text. For example, it may say, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[1045] Event notifications

[1046] Server: Based on the user's profile information, the server periodically collects relevant event information and generates appropriate event notifications for the user, such as local health events or workshops for seniors.

[1047] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[1048] Users: Check notifications and view details of events that interest them.

[1049] Topic Suggestion

[1050] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[1051] On device: Suggested topics are presented to the user as audio or text.

[1052] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[1053] Use of Emotional Data

[1054] Server: The emotion data recognized by the emotion analysis engine is added to the user's profile information, allowing the emotion data to be reflected in future information and topic suggestions, providing a more personalized service.

[1055] In this way, this system analyzes the user's voice request, determines their emotions, and provides appropriate information, thereby meeting the needs of the elderly while also providing a service that takes their emotions into consideration.The various components of the system work together, allowing users to obtain the information they need with peace of mind.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] User: Logs into the system. The login screen displays fields for entering ID and password.

[1059] Step 2:

[1060] Terminal: Sends the ID and password entered by the user to the server.

[1061] Step 3:

[1062] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[1063] Step 4:

[1064] Device: Upon successful authentication, a custom home screen based on the user's profile information is displayed. If it is the first time the user is logged in, an initial setup screen is displayed, prompting the user to enter personal information and topics of interest, if necessary.

[1065] Step 5:

[1066] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[1067] Step 6:

[1068] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[1069] Step 7:

[1070] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[1071] Step 8:

[1072] Server: Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) desired.

[1073] Step 9:

[1074] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[1075] Step 10:

[1076] Server: Filters the acquired hospital information and selects the hospital information that is most appropriate for the user.

[1077] Step 11:

[1078] On the device: Using voice data collected simultaneously with the voice request, an emotion analysis engine analyzes the user's emotions, for example, determining whether the user is feeling anxious.

[1079] Step 12:

[1080] Server: Receives emotional data from sentiment analysis and adjusts the tone and content of the information provided. For example, if the user is feeling anxious, it generates a more friendly and reassuring explanation.

[1081] Step 13:

[1082] Server: Sends the adjusted information to the device.

[1083] Step 14:

[1084] Device: Presents the received information to the user via voice and text. For example, it might say, "The nearest internal medicine hospital is XX Hospital, its address is XX. This hospital has a good reputation and is safe."

[1085] Step 15:

[1086] Server: Collects event information of interest based on user profile information and generates event notifications periodically.

[1087] Step 16:

[1088] Server: Sends generated event notifications to devices.

[1089] Step 17:

[1090] Device: Notify users of event notifications with sound and popup.

[1091] Step 18:

[1092] Users: Check the event information they are notified about and click or tap to view details about the event they are interested in.

[1093] Step 19:

[1094] Server: Analyzes the user's past request history and suggests topics that may be of interest to them.

[1095] Step 20:

[1096] On the device: Suggested topics are presented to the user in audio and text format.

[1097] Step 21:

[1098] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[1099] Step 22:

[1100] Terminal: Sends the user's add request to the server.

[1101] Step 23:

[1102] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[1103] Step 24:

[1104] Device: Displays the answers obtained to the user in audio and text format.

[1105] This series of processes allows users to easily obtain the information they need through voice or text, and allows them to receive emotionally sensitive services with peace of mind.

[1106] Example 2

[1107] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1108] Elderly people face challenges in efficiently and easily obtaining the information they need in their daily lives. Furthermore, conventional information provision systems do not provide information tailored to the user's emotions, making it difficult for users to understand the information and feel reassured. Furthermore, the need for suggesting related information based on past request history and providing filtered, accurate information is also a problem.

[1109] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1110] In this invention, the server includes: means for receiving a request from a user through speech recognition and converting the request into text; means for analyzing the converted text through natural language analysis to understand the user's intent; means for retrieving appropriate information from a database or external information source based on the user's profile information and past request history; means for filtering the retrieved information and providing it in a format optimal for the user; means for performing emotion analysis using the retrieved voice data; means for adjusting the tone and content of the information provided based on the analyzed emotion; and means for periodically generating information and event notifications for elderly users and notifying them of the information. This not only enables elderly users to efficiently and easily obtain the information they need, but also provides information that takes the user's emotions into consideration and gives them peace of mind. It also enables suggestions for related information and the provision of accurate information.

[1111] "Speech recognition" is a technology that analyzes voice signals and converts their contents into text data.

[1112] "Natural language analysis" is a technology that analyzes text data to understand its meaning and intent.

[1113] "Profile information" is data that records a user's basic information, past behavioral history, interests, etc.

[1114] An "external information source" is a resource used to obtain information, such as a database or API that exists outside the system.

[1115] "Filtering" is the process of selecting necessary information from acquired information based on specific conditions.

[1116] "Emotion analysis" is a technology that analyzes voice and text data to identify a user's emotional state.

[1117] "Tone" refers to the tone and attitude of the voice or writing used to provide information.

[1118] "Event Notifications" is a feature that notifies users of information about events and activities that may be of interest to them.

[1119] "Information provision" refers to the act of displaying or transmitting audio data necessary in response to a user's request.

[1120] "Related information" is topics and data suggested to you based on your interests and past requests.

[1121] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[1122] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest hospital that has an internal medicine department."

[1123] In the case of a voice request, the user makes a voice request such as "Tell me the nearest internal medicine hospital." The device receives the user's voice and converts it into text using a voice recognition engine (e.g., general voice recognition technology). The converted text is sent to the server as request data, "The nearest internal medicine hospital."

[1124] The server analyzes the received request data using a natural language analysis engine (e.g., general natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's location information and the type of hospital (internal medicine) desired. The server then uses a database and external API to obtain the most suitable hospital information. For example, it queries the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[1125] Next, the server filters the acquired hospital information and selects the most suitable hospital for the user. The device uses an emotion analysis engine (e.g., general emotion analysis technology) to analyze the user's emotions using the voice data collected during the voice request. The server receives the emotion data from the emotion analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, it will provide a more reassuring explanation in a more friendly tone. The adjusted information is sent to the device, which then displays the received information to the user in voice and text. Specifically, it provides information such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This is a safe hospital with a good reputation."

[1126] Furthermore, the system periodically collects relevant event information based on the user's profile information and generates event notifications appropriate for the user. For example, local health events or workshops for the elderly are targeted. The server updates this event information as needed, and the device provides notifications to the user in the form of audio and pop-up displays. The user can check the notifications and view details of events of interest.

[1127] The device also analyzes the user's past request history and periodically suggests topics that may be of interest to the user. For example, it suggests the latest health news or weather forecasts. The device displays the suggested topics to the user via voice or text, and the user can ask further questions based on the suggested topics. For example, a question could be, "What is the current flu epidemic situation?"

[1128] Emotional data recognized by the emotion analysis engine is added to the user's profile information and reflected in future information and topic suggestions, allowing for a more personalized service.

[1129] This system allows elderly people to obtain the information they need efficiently and easily, and provides information that is considerate of their emotions and gives them a sense of security. Below are some examples of prompts:

[1130] Example prompt:

[1131] User: "What's the nearest internal medicine hospital?"

[1132] Terminal: Receives voice and converts it into text (general voice recognition technology)

[1133] Server: Receives and analyzes the request text (general natural language analysis technology)

[1134] Server: Based on the analysis results, queries the local hospital information API and obtains the most appropriate hospital information.

[1135] Server: Analyzes the user's emotions and adjusts the tone of the information provided based on their emotions (general emotion analysis technology).

[1136] Device: Provides tailored information to the user via voice and text.

[1137] In this way, each component of the system works in cooperation with each other, allowing users to obtain the information they need with peace of mind.

[1138] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1139] Step 1:

[1140] Input: A voice request from the user (e.g., "What is the nearest internal medicine hospital?")

[1141] How it works: The user makes a verbal request: "Tell me the nearest internal medicine hospital."

[1142] Output: User's voice data

[1143] Step 2:

[1144] Input: User's voice data

[1145] How it works: The device receives the user's speech and converts it into text using a speech recognition engine. Specifically, it sends the speech data to a speech recognition API such as Google Cloud Speech-to-Text and retrieves the text data in return.

[1146] Output: Text data (e.g., "What is the nearest hospital for internal medicine?")

[1147] Step 3:

[1148] Input: Text data

[1149] Operation: The terminal sends the converted text data to the server.

[1150] Output: Text data is sent to the server

[1151] Step 4:

[1152] Input: Text data (on the server)

[1153] How it works: The server analyzes the text data using a natural language analysis engine (e.g., Google Cloud Natural Language API) to understand the user's intent. Specifically, it sends the text data to the analysis engine's API and obtains the user's intent and request as the analysis result.

[1154] Output: User intent and request (e.g., "Hospital for internal medicine" "Nearest")

[1155] Step 5:

[1156] Input: User intent and request

[1157] Operation: The server obtains the user's location information and uses the hospital information API to obtain information on the nearest internal medicine hospital. Specifically, it queries the API using keywords such as "internal medicine" and "nearest" and obtains a list of internal medicine hospitals.

[1158] Output: List of nearest internal medicine hospitals

[1159] Step 6:

[1160] Input: List of nearest internal medicine hospitals

[1161] How it works: The server filters the hospital information it obtains and selects the hospital that best suits the user. Filtering criteria include distance, reputation, and opening hours.

[1162] Output: Information on the best hospital (e.g., "Hospital X, address XX, good reputation")

[1163] Step 7:

[1164] Input: User's voice data

[1165] How it works: The device sends the voice data collected during a voice request to an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. The analysis results include the user's feelings of anxiety, relief, or confusion.

[1166] Output: User's emotion data (e.g., "anxiety")

[1167] Step 8:

[1168] Input: Optimal hospital information, user sentiment data

[1169] How it works: The server adjusts the tone and content of the information it provides based on the emotional data. For example, if a user feels anxious, it will provide information in a reassuring tone.

[1170] Output: Tailored information (e.g., "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe.")

[1171] Step 9:

[1172] Input: Adjusted information

[1173] How it works: The server sends the tailored information to the device, which then presents it to the user in voice and text.

[1174] Output: Information sent from the server to the device

[1175] Step 10:

[1176] Input: Information sent from the server to the device

[1177] Operation: The device displays the received information to the user in voice and text. The user receives information such as "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[1178] Output: Information provided to the user

[1179] (Application example 2)

[1180] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] It is important for elderly people to efficiently and easily obtain the information they need in their daily lives, but this is often technically difficult to achieve. Furthermore, information provision systems rarely respond in a way that takes into account the emotions and psychological state of elderly people, which can increase anxiety and stress. Furthermore, as the use of self-driving vehicles increases, there is a lack of support for elderly people to use self-driving vehicles safely. Therefore, there is a need for a system that allows elderly people to easily obtain information and provides support that takes their emotions into consideration.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1183] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis of the request, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the information to the user in voice or text, and means for analyzing emotions using the voice data and adjusting the tone and content of the information provided based on the analysis results. This enables elderly people to obtain the information they need with peace of mind when using an autonomous vehicle and receive responses that take their emotions into consideration.

[1184] "Speech recognition" is a technology that analyzes speech as digital data and converts the content into text.

[1185] "Request" means a request for specific information or services sought by a User.

[1186] "Natural language analysis" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[1187] "Profile information" is data that compiles information about a user, such as personal information, usage history, and interests.

[1188] An "external API" is a program interface for connecting with external systems and services.

[1189] A "database" is a system that efficiently stores and manages large amounts of data and allows it to be quickly retrieved when needed.

[1190] "Voice provision" refers to the means of conveying acquired information to the user by voice.

[1191] "Text provision" refers to the means of conveying acquired information to users in text.

[1192] "Emotion analysis" is a technology that infers and analyzes a user's emotional state from data such as voice and text.

[1193] "Tone" refers to the manner and tone of speech used when providing information, and should be adjusted according to the user's emotions.

[1194] "Information for elderly users" refers to information on medical care, welfare, and lifestyle support that is particularly needed by the elderly.

[1195] "Event Notification" is a system that notifies users in advance of relevant events and important announcements.

[1196] "Filtering" is the process of narrowing down the information obtained to what is useful to the user.

[1197] "Personalized services" refer to information and services that are optimized for specific users based on their profile information and emotional data.

[1198] An "autonomous vehicle" is a vehicle equipped with technology that allows it to drive automatically without the involvement of a driver.

[1199] This invention aims to provide a system that allows elderly people to efficiently and easily obtain the information they need when using self-driving vehicles. Furthermore, by adjusting the tone and content of the information provided based on emotion analysis, it aims to create an environment where elderly people can use vehicles with peace of mind.

[1200] First, the user makes a request to the terminal in the vehicle through the voice recognition system. For example, they might say, "Tell me where the nearest hospital is." The terminal converts this voice data into a digital signal, which is then converted into text using a voice recognition engine (for example, Google's voice recognition API). The converted text is sent to the server as request data for "nearest hospital."

[1201] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Based on this analysis, it understands the user's intent and then references the user's profile information and past request history to retrieve appropriate information from a database or external API (e.g., a local hospital information API). The retrieved information could be a list of the nearest hospitals, their detailed addresses, ratings, etc.

[1202] Next, an emotion analysis engine (e.g., EmotionRecognizer) installed on the device is used to analyze the emotions felt by the user when they input their voice. Specifically, anxiety or tension is detected from the voice data. This emotion data is sent to the server, which then adjusts the tone and content of the information provided. For example, if the user is feeling anxious, the server will provide information in a friendly tone, such as, "Don't worry. The nearest hospital is ____. It has a good reputation and is a safe place."

[1203] The adjusted information is then provided to the user via the terminal in the form of voice and text, and the user can follow the voice prompts to find the nearest hospital.

[1204] Furthermore, the server periodically generates relevant event notifications based on the user's profile information and sends them to the device, such as local health events or workshops for seniors. The device then notifies the user of these notifications in the form of voice and pop-up displays.

[1205] As a concrete example, if an elderly person requests, "Tell me the nearest cafe," the server will analyze the request using a natural language analysis engine and retrieve the most suitable cafe information from an external API based on the profile information and past history. At the same time, if the emotion analysis engine detects anxiety from the voice data, the server will provide information in a reassuring tone, such as, "Don't worry, this is a cafe with a good reputation."

[1206] The following are examples of prompts to aid in implementation:

[1207] "You will create a program that responds to the user's voice request with appropriate information. This includes voice recognition, emotion analysis, and location information acquisition."

[1208] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1209] Step 1:

[1210] The user inputs a voice request into the terminal, including specific information such as "Tell me where the nearest hospital is." The input voice is converted into digital data.

[1211] Step 2:

[1212] The device uses a speech recognition engine (for example, Google's speech recognition API) to convert the voice data into text data. The converted text data becomes request data such as "nearest hospital" and is sent to the server. Here, the voice data is converted into text data and sent as a result of processing, resulting in text data.

[1213] Step 3:

[1214] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Through analysis, it understands the details of the information the user is looking for (e.g., the category "hospitals" and the request "nearest"). During this analysis process, the text data is converted into data that indicates the user's intent.

[1215] Step 4:

[1216] The server references the user's profile information and past request history to retrieve the appropriate information. This may involve querying a database or an external API (e.g., a local hospital information API). Specifically, the server queries the database for the keywords "internal medicine" and "nearest," and retrieves the resulting list of nearby hospitals.

[1217] Step 5:

[1218] The server filters the obtained hospital list and selects the hospital information that is most suitable for the user. Here, it performs data calculations to determine the best option for the user using past profile information and request history.

[1219] Step 6:

[1220] The device analyzes the user's emotions based on the voice data using an emotion analysis engine (e.g., EmotionRecognizer). It identifies whether the user is feeling anxious or nervous when inputting voice data. The analyzed emotion data is sent to the server.

[1221] Step 7:

[1222] The server adjusts the tone and content of the information provided based on the emotion analysis results. For example, if the user is anxious, the server will provide information in a reassuring tone. In this procedure, the format of information provided is dynamically changed based on the emotion data.

[1223] Step 8:

[1224] The adjusted information is sent to the device in text and voice format. The device displays and outputs this information to the user, providing specific guidance such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[1225] Step 9:

[1226] The server periodically generates relevant event notifications based on the user's profile information and sends them to the device. For example, it periodically provides information about local health events and workshops for the elderly. This information is also notified to the user via voice and pop-up displays.

[1227] Step 10:

[1228] The device receives the event notification sent from the server and notifies the user. Detailed information about the notified event is also provided to the user, allowing the user to ask further questions or participate in the event.

[1229] 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.

[1230] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1231] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1232] [Fourth embodiment]

[1233] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1234] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1235] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1236] 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.

[1237] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1239] 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.

[1240] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1241] 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.

[1242] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1243] 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.

[1244] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1245] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1246] The present invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and combines voice recognition technology, natural language analysis technology, database technology, and external API utilization technology. Specific embodiments of this system are described below.

[1247] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, consider the request "Tell me the nearest internal medicine hospital."

[1248] Voice Request Processing

[1249] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[1250] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[1251] Request analysis and information acquisition

[1252] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[1253] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to receive a list of the nearest internal medicine hospitals.

[1254] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[1255] Providing information

[1256] Server: Sends filtered hospital information to the terminal.

[1257] Device: The received information is displayed to the user in voice and text format. For example, it may provide information such as, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[1258] Event notifications

[1259] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[1260] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[1261] Users: Check notifications and view details of events that interest them.

[1262] Topic Suggestion

[1263] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[1264] On device: Suggested topics are presented to the user as audio or text.

[1265] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[1266] In this way, this system can improve the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together, allowing users to obtain the information they need easily and intuitively.

[1267] The processing flow will be explained below.

[1268] Step 1:

[1269] User: Logs into the system. The login screen displays fields for entering ID and password.

[1270] Step 2:

[1271] Terminal: Sends the ID and password entered by the user to the server.

[1272] Step 3:

[1273] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[1274] Step 4:

[1275] On your device: Upon successful authentication, display a custom home screen based on the user's profile information. If it's your first time logging in, display an initial setup screen and ask for personal information and topics of interest.

[1276] Step 5:

[1277] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[1278] Step 6:

[1279] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[1280] Step 7:

[1281] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[1282] Step 8:

[1283] Server: Based on the analysis results, the server identifies the user's location and the type of hospital they prefer (e.g., internal medicine).

[1284] Step 9:

[1285] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[1286] Step 10:

[1287] Server: Filters the acquired hospital information and selects the most appropriate hospital information for the user.

[1288] Step 11:

[1289] Server: Sends the selected hospital information to the terminal.

[1290] Step 12:

[1291] Device: The received information is displayed to the user in voice and text format. For example, it may say, "The nearest internal medicine hospital is XX Hospital, and its address is XX."

[1292] Step 13:

[1293] Server: Based on user profile information, collects relevant event information and generates event notifications periodically.

[1294] Step 14:

[1295] Server: Sends generated event notifications to devices.

[1296] Step 15:

[1297] Device: Notify the user of the sent event notification with sound and popup.

[1298] Step 16:

[1299] Users: Check the notified event information and click or tap to view more information.

[1300] Step 17:

[1301] Server: Analyzes the user's past request history and periodically suggests topics that may be of interest to them.

[1302] Step 18:

[1303] On the device: Suggested topics are presented to the user in audio and text format.

[1304] Step 19:

[1305] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[1306] Step 20:

[1307] Terminal: Sends the user's add request to the server.

[1308] Step 21:

[1309] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[1310] Step 22:

[1311] Device: Displays the answers obtained to the user in audio and text format.

[1312] Through this series of processes, users can easily obtain the information they need through voice or text, and receive information and event notifications to assist with their daily lives.

[1313] Example 1

[1314] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1315] There is a need for systems that allow elderly people to easily and quickly obtain the information they need in their daily lives. Previous technologies have had problems such as low accuracy in speech recognition and natural language analysis, inefficient information retrieval, and a lack of suggestions for related events and topics. This has made it difficult for elderly people to obtain the information they want in an appropriate format.

[1316] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1317] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis on the request, means for retrieving appropriate information from a storage device or an external data source based on the user's profile information and past request history, and means for providing the retrieved information to the user in voice or text format. This allows elderly people to efficiently obtain the information they need and have it provided in an appropriate format, thereby improving their quality of life. The server also includes means for receiving a user's voice request and converting it into text using a voice recognition engine, means for automatically filtering information and providing it in an optimal format, and means for suggesting topics that may be of interest, further improving the user experience.

[1318] "Speech recognition" is a technology that analyzes voice signals and converts them into text.

[1319] "Natural language analysis" is a technology that analyzes human language into a form that a computer can understand and extracts intent.

[1320] "User profile information" refers to data such as a user's personal information, settings, and past behavioral history.

[1321] "Request History" is a record of requests that a user has made to the system in the past.

[1322] "Storage device" is a general term for hardware and software used to store data.

[1323] "External data sources" refers to APIs and databases provided by third parties.

[1324] "Filtering" is the process of selecting information that matches specific conditions from the acquired information.

[1325] "Topic Suggestion" is a feature that provides themes and topics that may be of interest to users based on their past behavioral history.

[1326] "Event Notification" is a function that regularly provides users with events and information specifically aimed at seniors.

[1327] A "speech recognition engine" is a software component that recognizes speech and converts it into text.

[1328] A "natural language analysis engine" is a software component that analyzes text data and converts it into information that a computer can understand.

[1329] This invention is a system that allows elderly people to efficiently and easily obtain information necessary for daily life, and it combines voice recognition technology, natural language analysis technology, database and external API utilization technology. This system can be maximized by implementing it as follows.

[1330] First, a user logs into the system using a username and password. If the login is successful, a session is started and the user can start using the system.

[1331] Next, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest internal medicine hospital." When the user makes a request by voice, a speech recognition engine (e.g., Google's speech recognition technology) is used to convert the voice signal into text. The converted text is sent to the server as request data, "the nearest internal medicine hospital."

[1332] The server analyzes the received request data using a natural language analysis engine (e.g., Azure's natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's profile information and desired information (e.g., internal medicine hospitals). The server then uses a database and external data sources (e.g., local hospital information APIs) to obtain the most appropriate information. For example, it queries the hospital information API using the keywords "internal medicine" and "nearest" and receives a list of the nearest internal medicine hospitals.

[1333] The acquired information is filtered and selected in the most appropriate form for the user. The filtered information is sent from the server to the device, which then displays it to the user in voice and text format. For example, it may be provided in the form of "The nearest internal medicine hospital is XX Hospital, and its address is XX." After providing the information, the device prompts the user with an additional question: "Is there any other information you are looking for?"

[1334] The server also periodically collects relevant event information based on the user's profile information and generates event notifications tailored to the user, such as local health events and workshops for seniors. Event notifications are sent to the device and notify the user via audio and a pop-up display. The user can then view the notifications and view details of events of interest.

[1335] Additionally, the server analyzes the user's past request history and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts. These suggested topics are displayed on the device, allowing the user to ask further questions based on them. For example, they could ask, "What is the current flu outbreak situation?"

[1336] The components of the system work together to allow users to easily and intuitively obtain the information they need. For example, here is a prompt:

[1337] Example prompt: Receive a voice request saying, "What is the nearest internal medicine hospital?" and provide information about the appropriate internal medicine hospital.

[1338] In this way, this system can improve the quality of life of the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format.

[1339] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1340] Step 1: Log in

[1341] User: A user logs into the system by first entering a username and password.

[1342] Terminal: Processes the entered username and password and sends them to the server as an authentication request.

[1343] Server: Checks the received username and password against the database. If the check is successful, starts a session and returns a successful authentication message to the terminal. If the check is unsuccessful, an error message is returned.

[1344] Step 2: Accepting a voice request

[1345] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[1346] Device: Receives audio through a microphone, captures it as audio data, and sends the captured audio data to a speech recognition engine.

[1347] Step 3: Voice Recognition

[1348] Terminal: The received voice data is analyzed by a voice recognition engine and converted into text. For example, the text may be "Please tell me the location of the nearest internal medicine hospital."

[1349] Terminal: The converted text is sent to the server as request data. The input data is the text converted from speech, and the output is the parsed request data.

[1350] Step 4: Request Analysis

[1351] Server: Analyzes the text data using a natural language analysis engine to understand the user's intent. The input data is the converted text, and the analysis results contain the user's intent.

[1352] Server: Based on the analysis results, retrieves the user's profile information and desired information (e.g., internal medicine hospital) from the database. The output is data containing the user's intent and related information.

[1353] Step 5: Information Acquisition

[1354] Server: Based on the analysis results, it issues a search query to an external data source (e.g., a local hospital information API). For example, it searches for hospitals based on the criteria "internal medicine" and "current location."

[1355] Server: Receives the list of nearest internal medicine hospitals returned from the API. The input is the search query, and the output is the list of hospital information retrieved.

[1356] Step 6: Information filtering

[1357] Server: Filters the received list of hospitals and selects the best hospital for the user, for example, based on reputation, distance, and opening hours.

[1358] Server: Formats the filtered information and provides it to the user. The input is the hospital information list, and the output is the filtered information.

[1359] Step 7: Provide information

[1360] Server: Sends the formatted hospital information to the terminal. The input is the filtered information, and the output is the transmitted data.

[1361] Terminal: The received information is displayed to the user as voice and text. For example, it might output "The nearest internal medicine hospital is XX Hospital, and its address is XX." The input is the transmitted data, and the output is the displayed information.

[1362] Step 8: Related Event Notifications

[1363] Server: Based on the user's profile information, collects event information that the user may be interested in. The input is the profile information, and the output is a list of related events.

[1364] Server: Organizes event information and generates optimal event notifications for users.

[1365] Terminal: Receives event notifications sent from the server and notifies the user with sound and a popup. The input is the event notification data, and the output is the notification.

[1366] Users: Check notifications and view details about events that interest them.

[1367] Step 9: Topic Proposal

[1368] Server: Analyzes the user's past request history and suggests topics they may be interested in. The input is the request history, and the output is the suggested topics.

[1369] Terminal: Presents suggested topics to the user as speech or text. The input is the suggested topic, and the output is the displayed topic.

[1370] User: Ask follow-up questions based on the suggested topic, such as "What's the current flu outbreak like?"

[1371] (Application example 1)

[1372] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] It is important for elderly people to easily and quickly obtain the information they need in their daily lives. However, conventional information provision systems are not specialized for the needs of elderly people, and obtaining the necessary information can require a great deal of time and effort. Furthermore, they lack the functionality to suggest meal delivery services that may be of particular interest to elderly people. Therefore, there is a need for a system that allows elderly people to easily use appropriate meal delivery services based on their preferences and history.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1375] In this invention, the server includes means for receiving requests from users using voice recognition and performing natural language analysis of the requests, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the retrieved information to the user in voice or text, and means for proposing food delivery services based on the user's preferences and history and notifying the user of the proposed information in voice or text. This enables elderly people to quickly and easily obtain appropriate information that meets their needs, and in particular, receive suggestions regarding meal delivery services.

[1376] "Speech recognition" is a technology that converts voice data into text data.

[1377] "Natural language analysis" is a technology that converts the natural language used by humans on a daily basis into a format that is easy for computers to understand and analyzes its meaning.

[1378] "Profile Information" refers to information that includes a user's personal data, preferences, and past activity history.

[1379] An "external API" is a standardized interface for connecting with other services and databases.

[1380] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[1381] A "food delivery service" is a service that allows you to order food and drinks and have them delivered to a designated location such as your home.

[1382] "Audio notifications" are a means of conveying information to the user in audio form.

[1383] A "text notification" is a means of conveying information to a user in written form.

[1384] "Event Notification" is a method of periodically notifying users of events and activities that are relevant to them.

[1385] The system of this invention is designed to enable elderly people to efficiently and easily obtain information necessary for daily life. The system combines voice recognition technology, natural language analysis technology, a database, an external API, and a food delivery service suggestion function. A specific embodiment of this system is described below.

[1386] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, the user can request "Tell me the nearest sushi restaurant."

[1387] Voice Request Processing

[1388] User: The user makes a request by voice.

[1389] Device: Converts the voice data into text data using a speech recognition engine (e.g., Google Speech-to-Text API). The converted text is sent to the server as request data for "nearest sushi restaurant."

[1390] Request analysis and information acquisition

[1391] Server: Analyzes the received request data using a natural language analysis engine (e.g., NLPProcessor) to understand the user's intent. Based on the analysis results, the user's location information and desired service (sushi) are identified.

[1392] Server: Uses a database and external APIs (e.g., Zomato API or Uber Eats API) to obtain the best sushi restaurant information. For example, make a query using the keywords "sushi" and "nearest" and receive a list of the nearest sushi restaurants.

[1393] Server: Filters the acquired sushi restaurant information and selects the most suitable service for the user.

[1394] Providing information

[1395] Server: Sends filtered service information to the terminal.

[1396] Device: The received information is displayed to the user in voice and text format. For example, it might provide information such as, "The nearest sushi restaurant is XX Sushi, and its address is XX."

[1397] Event notifications

[1398] Server: Based on the user's profile information, the server periodically collects relevant event information and generates personalized event notifications for the user, such as local health events or workshops for seniors.

[1399] Terminal: Receives event notifications sent from the server and notifies the user in the form of sound and popup display.

[1400] Topic Suggestion

[1401] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[1402] On the device: Suggested topics are presented to the user as audio and text.

[1403] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[1404] This system improves the quality of life for the elderly by efficiently obtaining information that meets the user's interests and needs and providing it in an appropriate format. The system's components work together to enable users to obtain the information they need easily and intuitively.

[1405] Prompt Sentence Examples

[1406] A user makes a voice request saying, "I'd like to order sushi." Use natural language analysis and a food delivery API to find the nearest recommended sushi restaurant and generate a response like this: "1. Sushi XX, address: XX. 2. Sushi △△, address: □□."

[1407] User location: Chuo-ku, Tokyo

[1408] Food preference: Sushi

[1409] Available APIs: Zomato, UberEats

[1410] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1411] Step 1:

[1412] A user logs in to the system. After logging in, they request specific information by voice or text. For example, the user might say, "Tell me the nearest sushi restaurant." The input is the user's voice data, and the output is digital voice data.

[1413] Step 2:

[1414] The device receives the user's voice data and converts it into text data using a speech recognition engine (for example, Google Speech-to-Text API). Specifically, the device sends the voice data to the speech recognition API and receives the text data "nearest sushi restaurant" as the conversion result. The input is digital voice data and the output is text data.

[1415] Step 3:

[1416] The server receives the request data sent from the device and analyzes it using a natural language analysis engine (e.g., NLPProcessor). Through this analysis, it understands the user's intent and converts a request such as "Tell me the nearest sushi restaurant" into a specific search query. The input is text data, and the output is the analyzed query data.

[1417] Step 4:

[1418] The server uses the user's location and preference information to send a search query to a database or external API (e.g., Zomato API or Uber Eats API). For example, a query is made using the keywords "sushi" and "nearest." The input is the query data, and the output is a list of services (sushi restaurants) provided.

[1419] Step 5:

[1420] The server filters the acquired service information and selects the sushi restaurant that best suits the user. Specifically, it selects the optimal service based on the user's past history and profile information. The input is the list data of sushi restaurants and the user profile data, and the output is the filtered sushi restaurant data.

[1421] Step 6:

[1422] The server sends the filtered sushi restaurant information to the terminal. It uses REST API as the transmission protocol to transfer data to the terminal. The input is the filtered sushi restaurant data, and the output is the data sent to the terminal.

[1423] Step 7:

[1424] The device receives the sushi restaurant information and displays it to the user in voice and text. It uses a voice output engine (Text-to-Speech TTS engine, such as Google Text-to-Speech API) to provide the information in voice, while simultaneously displaying text information on the screen. The input is the data sent to the device, and the output is voice and text information.

[1425] Step 8:

[1426] The server periodically collects relevant event information based on the user's profile information and generates optimal event notifications for the user, such as local health events or workshops for the elderly. The input is user profile data and event data, and the output is event notification data.

[1427] Step 9:

[1428] The terminal receives the event notification sent from the server and notifies the user. The notification is in the form of voice and a pop-up display. The input is the event notification data, and the output is voice and text notification.

[1429] Step 10:

[1430] The server analyzes the user's past request history and periodically suggests topics that the user may be interested in, such as the latest health news or weather forecasts. The input is the request history data, and the output is the suggested topic data.

[1431] Step 11:

[1432] The terminal displays the suggested topics to the user by voice and text. The input is the suggested topic data, and the output is the voice and text display.

[1433] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1434] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[1435] First, the user logs into the system. After logging in, the user can request specific information by voice or text, for example, "Tell me the nearest hospital that has an internal medicine department."

[1436] Voice Request Processing

[1437] User: The user makes a voice request saying, "Tell me the nearest internal medicine hospital."

[1438] Terminal: Receives the user's voice and converts it into text using a speech recognition engine. The converted text is sent to the server as request data for "nearest internal medicine hospital."

[1439] Request analysis and information acquisition

[1440] Server: Analyzes the received request data using a natural language processing engine to understand the user's intent. Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) they wish to visit.

[1441] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[1442] Server: Filters the acquired hospital information and selects the most suitable hospital for the user.

[1443] Sentiment analysis and information provision

[1444] On the device: The emotion analysis engine uses the voice data collected during the voice request to analyze the user's emotions. For example, if the user is feeling anxious, that emotion will be analyzed.

[1445] Server: Receives emotional data from the sentiment analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, the server will provide reassuring explanations in a more friendly tone.

[1446] Server: Sends the adjusted information to the device.

[1447] Device: The received information is displayed to the user in voice and text. For example, it may say, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[1448] Event notifications

[1449] Server: Based on the user's profile information, the server periodically collects relevant event information and generates appropriate event notifications for the user, such as local health events or workshops for seniors.

[1450] Terminal: Receives event notifications sent from the server and notifies the user. Notifications can be in the form of sound and popup display.

[1451] Users: Check notifications and view details of events that interest them.

[1452] Topic Suggestion

[1453] Server: Analyzes the user's past requests and periodically suggests topics that may be of interest to them, such as the latest health news or weather forecasts.

[1454] On device: Suggested topics are presented to the user as audio or text.

[1455] Users can ask further questions based on the suggested topics, for example, "What's the latest flu outbreak like?" in response to the suggested "Health News Update."

[1456] Use of Emotional Data

[1457] Server: The emotion data recognized by the emotion analysis engine is added to the user's profile information, allowing the emotion data to be reflected in future information and topic suggestions, providing a more personalized service.

[1458] In this way, this system analyzes the user's voice request, determines their emotions, and provides appropriate information, thereby meeting the needs of the elderly while also providing a service that takes their emotions into consideration.The various components of the system work together, allowing users to obtain the information they need with peace of mind.

[1459] The processing flow will be explained below.

[1460] Step 1:

[1461] User: Logs into the system. The login screen displays fields for entering ID and password.

[1462] Step 2:

[1463] Terminal: Sends the ID and password entered by the user to the server.

[1464] Step 3:

[1465] Server: The server verifies the user's ID and password against the user data in the database. If authentication is successful, it retrieves the user profile information and sends it to the device.

[1466] Step 4:

[1467] Device: Upon successful authentication, a custom home screen based on the user's profile information is displayed. If it is the first time the user is logged in, an initial setup screen is displayed, prompting the user to enter personal information and topics of interest, if necessary.

[1468] Step 5:

[1469] User: Request information by voice or text (e.g., "What is the nearest internal medicine clinic?").

[1470] Step 6:

[1471] Device: Converts the request into text using a speech recognition engine, then sends the converted text to the server.

[1472] Step 7:

[1473] Server: Analyzes the received text request using a natural language processing engine to understand the user's intent.

[1474] Step 8:

[1475] Server: Based on the analysis results, the server identifies the user's location and the type of hospital (internal medicine) desired.

[1476] Step 9:

[1477] Server: Uses databases and external APIs to obtain the most suitable hospital information. For example, query the local hospital information API using keywords such as "internal medicine" and "nearest."

[1478] Step 10:

[1479] Server: Filters the acquired hospital information and selects the hospital information that is most appropriate for the user.

[1480] Step 11:

[1481] On the device: Using voice data collected simultaneously with the voice request, an emotion analysis engine analyzes the user's emotions, for example, determining whether the user is feeling anxious.

[1482] Step 12:

[1483] Server: Receives emotional data from sentiment analysis and adjusts the tone and content of the information provided. For example, if the user is feeling anxious, it generates a more friendly and reassuring explanation.

[1484] Step 13:

[1485] Server: Sends the adjusted information to the device.

[1486] Step 14:

[1487] Device: Presents the received information to the user via voice and text. For example, it might say, "The nearest internal medicine hospital is XX Hospital, its address is XX. This hospital has a good reputation and is safe."

[1488] Step 15:

[1489] Server: Collects event information of interest based on user profile information and generates event notifications periodically.

[1490] Step 16:

[1491] Server: Sends generated event notifications to devices.

[1492] Step 17:

[1493] Device: Notify users of event notifications with sound and popup.

[1494] Step 18:

[1495] Users: Check the event information they are notified about and click or tap to view details about the event they are interested in.

[1496] Step 19:

[1497] Server: Analyzes the user's past request history and suggests topics that may be of interest to them.

[1498] Step 20:

[1499] On the device: Suggested topics are presented to the user in audio and text format.

[1500] Step 21:

[1501] Users: Ask questions or follow-up requests to suggested topics, such as "What's the latest flu outbreak?"

[1502] Step 22:

[1503] Terminal: Sends the user's add request to the server.

[1504] Step 23:

[1505] Server: Retrieves the answer from the appropriate database or external API and sends it to the device.

[1506] Step 24:

[1507] Device: Displays the answers obtained to the user in audio and text format.

[1508] This series of processes allows users to easily obtain the information they need through voice or text, and allows them to receive emotionally sensitive services with peace of mind.

[1509] Example 2

[1510] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1511] Elderly people face challenges in efficiently and easily obtaining the information they need in their daily lives. Furthermore, conventional information provision systems do not provide information tailored to the user's emotions, making it difficult for users to understand the information and feel reassured. Furthermore, the need for suggesting related information based on past request history and providing filtered, accurate information is also a problem.

[1512] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1513] In this invention, the server includes: means for receiving a request from a user through speech recognition and converting the request into text; means for analyzing the converted text through natural language analysis to understand the user's intent; means for retrieving appropriate information from a database or external information source based on the user's profile information and past request history; means for filtering the retrieved information and providing it in a format optimal for the user; means for performing emotion analysis using the retrieved voice data; means for adjusting the tone and content of the information provided based on the analyzed emotion; and means for periodically generating information and event notifications for elderly users and notifying them of the information. This not only enables elderly users to efficiently and easily obtain the information they need, but also provides information that takes the user's emotions into consideration and gives them peace of mind. It also enables suggestions for related information and the provision of accurate information.

[1514] "Speech recognition" is a technology that analyzes voice signals and converts their contents into text data.

[1515] "Natural language analysis" is a technology that analyzes text data to understand its meaning and intent.

[1516] "Profile information" is data that records a user's basic information, past behavioral history, interests, etc.

[1517] An "external information source" is a resource used to obtain information, such as a database or API that exists outside the system.

[1518] "Filtering" is the process of selecting necessary information from acquired information based on specific conditions.

[1519] "Emotion analysis" is a technology that analyzes voice and text data to identify a user's emotional state.

[1520] "Tone" refers to the tone and attitude of the voice or writing used to provide information.

[1521] "Event Notifications" is a feature that notifies users of information about events and activities that may be of interest to them.

[1522] "Information provision" refers to the act of displaying or transmitting audio data necessary in response to a user's request.

[1523] "Related information" is topics and data suggested to you based on your interests and past requests.

[1524] The present invention provides a system that allows elderly people to efficiently and easily obtain information necessary for their daily lives and that combines an emotion analysis engine. Specific embodiments of this system are described below.

[1525] First, the user logs in to the system. After logging in, the user can request specific information by voice or text. For example, a request might be, "Tell me the nearest hospital that has an internal medicine department."

[1526] In the case of a voice request, the user makes a voice request such as "Tell me the nearest internal medicine hospital." The device receives the user's voice and converts it into text using a voice recognition engine (e.g., general voice recognition technology). The converted text is sent to the server as request data, "The nearest internal medicine hospital."

[1527] The server analyzes the received request data using a natural language analysis engine (e.g., general natural language analysis technology) to understand the user's intent. Based on the analysis results, the server identifies the user's location information and the type of hospital (internal medicine) desired. The server then uses a database and external API to obtain the most suitable hospital information. For example, it queries the local hospital information API using the keywords "internal medicine" and "nearest" to obtain a list of the nearest internal medicine hospitals.

[1528] Next, the server filters the acquired hospital information and selects the most suitable hospital for the user. The device uses an emotion analysis engine (e.g., general emotion analysis technology) to analyze the user's emotions using the voice data collected during the voice request. The server receives the emotion data from the emotion analysis engine and adjusts the tone and content of the information provided based on the user's emotions. For example, if the user is feeling anxious, it will provide a more reassuring explanation in a more friendly tone. The adjusted information is sent to the device, which then displays the received information to the user in voice and text. Specifically, it provides information such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This is a safe hospital with a good reputation."

[1529] Furthermore, the system periodically collects relevant event information based on the user's profile information and generates event notifications appropriate for the user. For example, local health events or workshops for the elderly are targeted. The server updates this event information as needed, and the device provides notifications to the user in the form of audio and pop-up displays. The user can check the notifications and view details of events of interest.

[1530] The device also analyzes the user's past request history and periodically suggests topics that may be of interest to the user. For example, it suggests the latest health news or weather forecasts. The device displays the suggested topics to the user via voice or text, and the user can ask further questions based on the suggested topics. For example, a question could be, "What is the current flu epidemic situation?"

[1531] Emotional data recognized by the emotion analysis engine is added to the user's profile information and reflected in future information and topic suggestions, allowing for a more personalized service.

[1532] This system allows elderly people to obtain the information they need efficiently and easily, and provides information that is considerate of their emotions and gives them a sense of security. Below are some examples of prompts:

[1533] Example prompt:

[1534] User: "What's the nearest internal medicine hospital?"

[1535] Terminal: Receives voice and converts it into text (general voice recognition technology)

[1536] Server: Receives and analyzes the request text (general natural language analysis technology)

[1537] Server: Based on the analysis results, queries the local hospital information API and obtains the most appropriate hospital information.

[1538] Server: Analyzes the user's emotions and adjusts the tone of the information provided based on their emotions (general emotion analysis technology).

[1539] Device: Provides tailored information to the user via voice and text.

[1540] In this way, each component of the system works in cooperation with each other, allowing users to obtain the information they need with peace of mind.

[1541] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1542] Step 1:

[1543] Input: A voice request from the user (e.g., "What is the nearest internal medicine hospital?")

[1544] How it works: The user makes a verbal request: "Tell me the nearest internal medicine hospital."

[1545] Output: User's voice data

[1546] Step 2:

[1547] Input: User's voice data

[1548] How it works: The device receives the user's speech and converts it into text using a speech recognition engine. Specifically, it sends the speech data to a speech recognition API such as Google Cloud Speech-to-Text and retrieves the text data in return.

[1549] Output: Text data (e.g., "What is the nearest hospital for internal medicine?")

[1550] Step 3:

[1551] Input: Text data

[1552] Operation: The terminal sends the converted text data to the server.

[1553] Output: Text data is sent to the server

[1554] Step 4:

[1555] Input: Text data (on the server)

[1556] How it works: The server analyzes the text data using a natural language analysis engine (e.g., Google Cloud Natural Language API) to understand the user's intent. Specifically, it sends the text data to the analysis engine's API and obtains the user's intent and request as the analysis result.

[1557] Output: User intent and request (e.g., "Hospital for internal medicine" "Nearest")

[1558] Step 5:

[1559] Input: User intent and request

[1560] Operation: The server obtains the user's location information and uses the hospital information API to obtain information on the nearest internal medicine hospital. Specifically, it queries the API using keywords such as "internal medicine" and "nearest" and obtains a list of internal medicine hospitals.

[1561] Output: List of nearest internal medicine hospitals

[1562] Step 6:

[1563] Input: List of nearest internal medicine hospitals

[1564] How it works: The server filters the hospital information it obtains and selects the hospital that best suits the user. Filtering criteria include distance, reputation, and opening hours.

[1565] Output: Information on the best hospital (e.g., "Hospital X, address XX, good reputation")

[1566] Step 7:

[1567] Input: User's voice data

[1568] How it works: The device sends the voice data collected during a voice request to an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions. The analysis results include the user's feelings of anxiety, relief, or confusion.

[1569] Output: User's emotion data (e.g., "anxiety")

[1570] Step 8:

[1571] Input: Optimal hospital information, user sentiment data

[1572] How it works: The server adjusts the tone and content of the information it provides based on the emotional data. For example, if a user feels anxious, it will provide information in a reassuring tone.

[1573] Output: Tailored information (e.g., "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe.")

[1574] Step 9:

[1575] Input: Adjusted information

[1576] How it works: The server sends the tailored information to the device, which then presents it to the user in voice and text.

[1577] Output: Information sent from the server to the device

[1578] Step 10:

[1579] Input: Information sent from the server to the device

[1580] Operation: The device displays the received information to the user in voice and text. The user receives information such as "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[1581] Output: Information provided to the user

[1582] (Application example 2)

[1583] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1584] It is important for elderly people to efficiently and easily obtain the information they need in their daily lives, but this is often technically difficult to achieve. Furthermore, information provision systems rarely respond in a way that takes into account the emotions and psychological state of elderly people, which can increase anxiety and stress. Furthermore, as the use of self-driving vehicles increases, there is a lack of support for elderly people to use self-driving vehicles safely. Therefore, there is a need for a system that allows elderly people to easily obtain information and provides support that takes their emotions into consideration.

[1585] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1586] In this invention, the server includes means for receiving a request from a user using voice recognition and performing natural language analysis of the request, means for retrieving appropriate information from a database or an external API based on the user's profile information and past request history, means for providing the information to the user in voice or text, and means for analyzing emotions using the voice data and adjusting the tone and content of the information provided based on the analysis results. This enables elderly people to obtain the information they need with peace of mind when using an autonomous vehicle and receive responses that take their emotions into consideration.

[1587] "Speech recognition" is a technology that analyzes speech as digital data and converts the content into text.

[1588] "Request" means a request for specific information or services sought by a User.

[1589] "Natural language analysis" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[1590] "Profile information" is data that compiles information about a user, such as personal information, usage history, and interests.

[1591] An "external API" is a program interface for connecting with external systems and services.

[1592] A "database" is a system that efficiently stores and manages large amounts of data and allows it to be quickly retrieved when needed.

[1593] "Voice provision" refers to the means of conveying acquired information to the user by voice.

[1594] "Text provision" refers to the means of conveying acquired information to users in text.

[1595] "Emotion analysis" is a technology that infers and analyzes a user's emotional state from data such as voice and text.

[1596] "Tone" refers to the manner and tone of speech used when providing information, and should be adjusted according to the user's emotions.

[1597] "Information for elderly users" refers to information on medical care, welfare, and lifestyle support that is particularly needed by the elderly.

[1598] "Event Notification" is a system that notifies users in advance of relevant events and important announcements.

[1599] "Filtering" is the process of narrowing down the information obtained to what is useful to the user.

[1600] "Personalized services" refer to information and services that are optimized for specific users based on their profile information and emotional data.

[1601] An "autonomous vehicle" is a vehicle equipped with technology that allows it to drive automatically without the involvement of a driver.

[1602] This invention aims to provide a system that allows elderly people to efficiently and easily obtain the information they need when using self-driving vehicles. Furthermore, by adjusting the tone and content of the information provided based on emotion analysis, it aims to create an environment where elderly people can use vehicles with peace of mind.

[1603] First, the user makes a request to the terminal in the vehicle through the voice recognition system. For example, they might say, "Tell me where the nearest hospital is." The terminal converts this voice data into a digital signal, which is then converted into text using a voice recognition engine (for example, Google's voice recognition API). The converted text is sent to the server as request data for "nearest hospital."

[1604] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Based on this analysis, it understands the user's intent and then references the user's profile information and past request history to retrieve appropriate information from a database or external API (e.g., a local hospital information API). The retrieved information could be a list of the nearest hospitals, their detailed addresses, ratings, etc.

[1605] Next, an emotion analysis engine (e.g., EmotionRecognizer) installed on the device is used to analyze the emotions felt by the user when they input their voice. Specifically, anxiety or tension is detected from the voice data. This emotion data is sent to the server, which then adjusts the tone and content of the information provided. For example, if the user is feeling anxious, the server will provide information in a friendly tone, such as, "Don't worry. The nearest hospital is ____. It has a good reputation and is a safe place."

[1606] The adjusted information is then provided to the user via the terminal in the form of voice and text, and the user can follow the voice prompts to find the nearest hospital.

[1607] Furthermore, the server periodically generates relevant event notifications based on the user's profile information and sends them to the device, such as local health events or workshops for seniors. The device then notifies the user of these notifications in the form of voice and pop-up displays.

[1608] As a concrete example, if an elderly person requests, "Tell me the nearest cafe," the server will analyze the request using a natural language analysis engine and retrieve the most suitable cafe information from an external API based on the profile information and past history. At the same time, if the emotion analysis engine detects anxiety from the voice data, the server will provide information in a reassuring tone, such as, "Don't worry, this is a cafe with a good reputation."

[1609] The following are examples of prompts to aid in implementation:

[1610] "You will create a program that responds to the user's voice request with appropriate information. This includes voice recognition, emotion analysis, and location information acquisition."

[1611] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1612] Step 1:

[1613] The user inputs a voice request into the terminal, including specific information such as "Tell me where the nearest hospital is." The input voice is converted into digital data.

[1614] Step 2:

[1615] The device uses a speech recognition engine (for example, Google's speech recognition API) to convert the voice data into text data. The converted text data becomes request data such as "nearest hospital" and is sent to the server. Here, the voice data is converted into text data and sent as a result of processing, resulting in text data.

[1616] Step 3:

[1617] The server receives the request data and analyzes it using a natural language analysis engine (e.g., NLTK). Through analysis, it understands the details of the information the user is looking for (e.g., the category "hospitals" and the request "nearest"). During this analysis process, the text data is converted into data that indicates the user's intent.

[1618] Step 4:

[1619] The server references the user's profile information and past request history to retrieve the appropriate information. This may involve querying a database or an external API (e.g., a local hospital information API). Specifically, the server queries the database for the keywords "internal medicine" and "nearest," and retrieves the resulting list of nearby hospitals.

[1620] Step 5:

[1621] The server filters the obtained hospital list and selects the hospital information that is most suitable for the user. Here, it performs data calculations to determine the best option for the user using past profile information and request history.

[1622] Step 6:

[1623] The device analyzes the user's emotions based on the voice data using an emotion analysis engine (e.g., EmotionRecognizer). It identifies whether the user is feeling anxious or nervous when inputting voice data. The analyzed emotion data is sent to the server.

[1624] Step 7:

[1625] The server adjusts the tone and content of the information provided based on the emotion analysis results. For example, if the user is anxious, the server will provide information in a reassuring tone. In this procedure, the format of information provided is dynamically changed based on the emotion data.

[1626] Step 8:

[1627] The adjusted information is sent to the device in text and voice format. The device displays and outputs this information to the user, providing specific guidance such as, "The nearest internal medicine hospital is XX Hospital. The address is XX. This hospital has a good reputation and is safe."

[1628] Step 9:

[1629] The server periodically generates relevant event notifications based on the user's profile information and sends them to the device. For example, it periodically provides information about local health events and workshops for the elderly. This information is also notified to the user via voice and pop-up displays.

[1630] Step 10:

[1631] The device receives the event notification sent from the server and notifies the user. Detailed information about the notified event is also provided to the user, allowing the user to ask further questions or participate in the event.

[1632] 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.

[1633] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1634] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1635] 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.

[1636] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1637] 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.

[1638] 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).

[1639] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1640] 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."

[1641] 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.

[1642] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1643] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1648] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] The following is further disclosed regarding the above embodiment.

[1654] (Claim 1)

[1655] means for receiving a request from a user using speech recognition and performing natural language analysis of the request;

[1656] A means to retrieve the appropriate information from a database or external API based on the user's profile information and past request history;

[1657] A means for providing the retrieved information to the user by voice or text;

[1658] a means for periodically generating and notifying elderly users of information and event notifications;

[1659] A system including:

[1660] (Claim 2)

[1661] 10. The system of claim 1, wherein the system suggests topics based on a user's interests for a particular information category.

[1662] (Claim 3)

[1663] 10. The system of claim 1, wherein the acquired information is filtered and displayed in a form optimal for the user.

[1664] "Example 1"

[1665] (Claim 1)

[1666] means for receiving a request from a user using speech recognition and performing natural language analysis of the request;

[1667] means for retrieving appropriate information from storage or an external data source based on the user's profile information and past request history;

[1668] A means for providing the retrieved information to the user by voice or text;

[1669] a means for periodically generating and notifying elderly users of information and event notifications;

[1670] means for receiving a user's voice request and converting it into text via a speech recognition engine;

[1671] A means of analyzing text data using a natural language analysis engine to understand user intent,

[1672] A means for filtering the information obtained and presenting it in a form that is most suitable for the user;

[1673] A way to analyze users' past request history and suggest topics that may be of interest to them;

[1674] A system including:

[1675] (Claim 2)

[1676] 10. The system of claim 1, wherein the system suggests topics based on a user's interests for a particular information category.

[1677] (Claim 3)

[1678] 10. The system of claim 1, wherein the acquired information is filtered and displayed in a form optimal for the user.

[1679] "Application Example 1"

[1680] (Claim 1)

[1681] means for receiving a request from a user using speech recognition and performing natural language analysis of the request;

[1682] A means to retrieve the appropriate information from a database or external API based on the user's profile information and past request history;

[1683] A means for providing the retrieved information to the user by voice or text;

[1684] a means for periodically generating and notifying elderly users of information and event notifications;

[1685] a means for proposing food provision services based on the user's preferences and history and notifying the user of the proposed information by voice or text;

[1686] A system including:

[1687] (Claim 2)

[1688] 10. The system of claim 1, wherein the system suggests topics based on a user's interests for a particular information category.

[1689] (Claim 3)

[1690] 10. The system of claim 1, wherein the acquired information is filtered and displayed in a form optimal for the user.

[1691] "Example 2: Combining Emotion Engines"

[1692] (Claim 1)

[1693] means for receiving a request from a user through speech recognition and converting the request into text;

[1694] A means of analyzing the converted text using natural language analysis to understand the user's intent;

[1695] A means for retrieving appropriate information from a database or external source based on the user's profile information and past request history;

[1696] A means of filtering the information obtained and providing it to the user in the most appropriate form;

[1697] A means for performing emotion analysis using the acquired voice data;

[1698] A means of adjusting the tone and content of information provided based on the analyzed emotions;

[1699] a means for periodically generating and notifying elderly users of information and event notifications;

[1700] A system including:

[1701] (Claim 2)

[1702] 10. The system of claim 1, wherein the system suggests topics based on a user's interests for a particular information category.

[1703] (Claim 3)

[1704] 2. The system according to claim 1, wherein the acquired information is filtered based on a result of sentiment analysis.

[1705] "Application example 2 when combining emotion engines"

[1706] (Claim 1)

[1707] means for receiving a request from a user using speech recognition and performing natural language analysis of the request;

[1708] A means to retrieve the appropriate information from a database or external API based on the user's profile information and past request history;

[1709] A means for providing the retrieved information to the user by voice or text;

[1710] a means for periodically generating and notifying elderly users of information and event notifications;

[1711] A means for analyzing emotions using voice data and adjusting the tone and content of information provided based on the analysis results;

[1712] A system including:

[1713] (Claim 2)

[1714] 10. The system of claim 1, wherein the system suggests topics based on a user's interests for a particular information category.

[1715] (Claim 3)

[1716] 10. The system according to claim 1, wherein the acquired information is filtered and displayed in a form optimal for the user, and emotional data is used to provide personalized services. [Explanation of symbols]

[1717] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a request from a user using speech recognition and performing natural language analysis of the request; A means to retrieve the appropriate information from a database or external API based on the user's profile information and past request history; A means for providing the retrieved information to the user by voice or text; a means for periodically generating and notifying elderly users of information and event notifications; A system including:

2. 10. The system of claim 1, wherein the system suggests topics based on the user's interests for a particular information category.

3. 2. The system of claim 1, wherein the acquired information is filtered and displayed in a form optimal for the user.

Images