system

Abstract

Provide a system. A system for preventing elderly people from getting lost when they go out, A means for acquiring the location at any time; means for transmitting the acquired position data to a central processing unit; means for recording and analyzing position data in a central processing unit; means for calculating an optimal route based on the analysis results and generating travel instructions; A means of automatically notifying emergency contacts in the event of an emergency; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, anxiety about getting lost and social isolation when going out are serious problems for elderly people. Creating an environment where elderly people can go out with confidence is important for their social participation and health maintenance. However, conventional technologies simply track location information, but they are unable to provide sufficient support, and emergency response times are often insufficient. Therefore, there is a need for a system that can learn behavioral patterns and provide personalized support in addition to tracking location information and responding to emergencies, so that elderly people can enjoy going out with peace of mind. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system for preventing elderly people from getting lost when they go out. Specifically, the system includes a means for constantly acquiring locations, a means for transmitting acquired location data to a central processing unit, a means for recording and analyzing the location data in the central processing unit, a means for calculating an optimal route based on the analysis results and generating travel instructions, and a means for automatically notifying emergency contacts in the event of an emergency. Furthermore, the system includes a function for learning behavioral patterns and providing personalized travel assistance based on individual routines, as well as a means for accepting voice input, thereby achieving comprehensive outing assistance for elderly people. This system creates an environment where elderly people can go out with peace of mind and prevents social isolation.

[0006] "Means for acquiring location at any time" refers to a device that periodically acquires the current location using a location information acquisition sensor such as GPS.

[0007] "Means for transmitting acquired location data to a central processing unit" refers to a device that transmits location information from a terminal to a server via a network.

[0008] "Central Processing Unit" refers to the server that processes, analyzes and records the received data.

[0009] "Means for recording and analyzing location data" refers to software and hardware that stores received location information in a database and uses it to analyze user behavior patterns.

[0010] "Means for calculating an optimal route and generating travel instructions" refers to an algorithm and device that calculates an optimal travel route based on a user's behavioral patterns and real-time traffic information, and generates instructions based on that.

[0011] "Means for automatically notifying emergency contacts in an emergency" refers to devices and software that automatically notify registered contacts of the user's location and situation when the user is in an emergency.

[0012] "A function that learns behavioral patterns and provides personalized mobility assistance based on individual routines" refers to algorithms and systems that perform machine learning based on the user's past behavioral data and provide assistance tailored to individual characteristics.

[0013] "Means for accepting voice input, transmitting voice data to a central processing unit, and generating appropriate advice or instructions" refers to devices and software that convert voice input from a user into text data, analyze it, and generate appropriate instructions or advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0022] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] MODE FOR CARRYING OUT THE INVENTION

[0036] This invention is a system that enables elderly people to go out safely and without getting lost. The system has functions for tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[0037] 1. Location Tracking

[0038] The system's terminal uses a GPS sensor to constantly obtain the elderly person's current location and transmits this information to a server. The server records the received location information in a database and performs analysis. This information is later used to learn behavioral patterns and calculate optimal routes.

[0039] 2. Learning behavioral patterns

[0040] The server analyzes the user's movement patterns based on the received location data and identifies frequently visited locations and routes, allowing the server to learn each user's individual behavioral patterns and provide personalized services.

[0041] 3. Optimal route suggestions

[0042] When the user sets a destination, the device notifies the server. The server calculates the optimal route based on past behavior patterns and real-time traffic information, and sends the results to the device. The device then displays this route to the user and provides voice guidance.

[0043] 4. Emergency notification

[0044] When a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server, which then immediately sends their current location and emergency information to their emergency contacts to promptly respond.

[0045] 5. Advice and Instruction Generation by Conversational AI

[0046] Users can ask questions or give instructions to the device using voice commands. For example, when a user asks a question such as "Where are you now?", the device converts the voice into text data and sends it to the server. The server analyzes the voice data, generates an appropriate response (e.g., "You are currently on Main Street"), and sends it to the device. The device then notifies the user of this response by voice.

[0047] Specific examples

[0048] Example 1: Navigation on the go

[0049] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[0050] 2. The device converts this voice command into text and sends it to the server.

[0051] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[0052] 4. The device will provide voice guidance for this route to the user and begin navigation.

[0053] 5. The user follows the directions to safely reach the supermarket.

[0054] Example 2: Emergency response

[0055] 1. The user falls while out and presses the emergency button.

[0056] 2. The device sends an emergency notification to the server.

[0057] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[0058] 4. Emergency contacts will be notified and will take necessary action.

[0059] This system not only allows seniors to feel safe when going out, but also allows for a quick response in the event of an emergency, and uses conversational AI to provide an even higher level of support and engagement.

[0060] The processing flow will be explained below.

[0061] Location tracking

[0062] Step 1:

[0063] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[0064] Step 2:

[0065] The location information acquired by the device is temporarily stored in memory.

[0066] Step 3:

[0067] The device sends location information to the server at set intervals (e.g., every minute).

[0068] Step 4:

[0069] The server records the received location information in a database.

[0070] Step 5:

[0071] The server records the time the location information was received and waits for the next update.

[0072] Learning behavioral patterns

[0073] Step 1:

[0074] The server retrieves previously stored location information from the database.

[0075] Step 2:

[0076] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[0077] Step 3:

[0078] The server creates a dataset based on the analysis results to learn user behavior patterns.

[0079] Step 4:

[0080] The server uses a machine learning model to learn user behavior patterns and stores the results.

[0081] Optimal route suggestions

[0082] Step 1:

[0083] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[0084] Step 2:

[0085] The terminal transmits the set destination to the server.

[0086] Step 3:

[0087] The server acquires the user's past behavioral patterns and real-time traffic information.

[0088] Step 4:

[0089] The server calculates the optimal travel route and sends the calculation results to the terminal.

[0090] Step 5:

[0091] The terminal displays the optimal route to the user and starts voice guidance.

[0092] emergency notification

[0093] Step 1:

[0094] When an emergency occurs, the user presses the emergency button on the terminal.

[0095] Step 2:

[0096] The device detects when the emergency button is pressed and obtains the current location information.

[0097] Step 3:

[0098] The device sends the acquired location information and emergency notification to the server.

[0099] Step 4:

[0100] The server will send your location and emergency information to your emergency contacts via email or SMS.

[0101] Conversational AI for generating advice and instructions

[0102] Step 1:

[0103] The user speaks a question to the device, such as "Where are you now?"

[0104] Step 2:

[0105] The device converts the voice data it receives into text and sends it to the server.

[0106] Step 3:

[0107] The server parses the received text data and generates an appropriate response.

[0108] Step 4:

[0109] The server generates a response and sends it to the terminal in text format.

[0110] Step 5:

[0111] The terminal converts the text data into speech and provides the user with the response content.

[0112] Specific examples

[0113] Example 1: Navigation on the go

[0114] Step 1:

[0115] The user speaks to the terminal, saying, "I want to go to the supermarket."

[0116] Step 2:

[0117] The device converts this voice command into text and sends it to the server.

[0118] Step 3:

[0119] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[0120] Step 4:

[0121] The server sends the calculation results to the terminal.

[0122] Step 5:

[0123] The terminal will then provide voice guidance for this route to the user and begin navigation.

[0124] Example 2: Emergency response

[0125] Step 1:

[0126] The user falls while out and presses the emergency button.

[0127] Step 2:

[0128] The device sends an emergency notification to the server.

[0129] Step 3:

[0130] The server will notify your emergency contact of your current location and status via email or SMS.

[0131] Step 4:

[0132] Emergency contacts will be notified and will take necessary action.

[0133] Example 1

[0134] 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."

[0135] In modern society, it is important for elderly people to be able to go out safely and with peace of mind. However, concerns about getting lost or traveling, as well as rapid response in emergencies, remain issues. Elderly people, in particular, tend to have limited mobility, so rapid response is required in emergencies. However, existing systems rarely provide integrated location tracking, behavioral pattern learning, optimal route suggestions, and emergency notifications, and they also lack support from conversational AI. Therefore, there is a need for a more comprehensive and personalized outing support system for elderly people.

[0136] 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.

[0137] In this invention, the server includes means for constantly acquiring locations, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for calculating an optimal route and generating travel instructions based on the analysis results, means for automatically notifying emergency contacts in the event of an emergency, means for converting voice commands into text data, means for transmitting voice data to the central processing unit and generating an appropriate response, and means for generating a voice response and notifying the user. This allows elderly people to go out with peace of mind, enables quick response in the event of an emergency, and further enables personalized support based on behavioral patterns and advanced support using conversational AI to be provided.

[0138] "Means for obtaining location information on a regular basis" refers to a device or method that periodically obtains the user's current location using a GPS sensor or other location measurement device.

[0139] "Means for transmitting to the central processing unit" refers to a device or method for transmitting location data and other necessary information to the central processing unit via wireless communication, the Internet, etc.

[0140] "Central processing unit" refers to a computer or server that records received data and performs analysis, etc.

[0141] "Means for recording and analyzing location data" refers to a device or method that stores received location data in a database or the like and analyzes the user's behavioral patterns and current location based on this data.

[0142] "Means for calculating the optimal route and generating travel instructions" refers to a device or method that calculates the optimal route to a destination for a user based on past behavioral patterns and real-time traffic information, and generates the contents of that route as instructions.

[0143] "Means for automatically notifying emergency contacts in the event of an emergency" refers to a device or method that automatically notifies emergency contacts when a user is in an emergency.

[0144] The "means for converting voice commands into text data" refers to a device or method for converting voice commands input by a user into text format.

[0145] The "means for transmitting voice data to the central processing unit" refers to a device or method for transmitting the converted text data to the central processing unit.

[0146] The "means for generating an appropriate response" is an algorithm or software that generates an appropriate response in response to a voice command.

[0147] The "means for generating a voice response and notifying the user" refers to a device or method for notifying the user of the generated appropriate response as voice.

[0148] This invention is a system that helps elderly people move around safely and securely when they go out. The system has the functions of tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[0149] Hardware and software used

[0150] Hardware

[0151] Terminal: A device with a built-in GPS sensor that captures the user's location. This includes smartphones and dedicated mobile terminals.

[0152] Server: A computing device that receives, records, analyzes data, and generates appropriate responses and routes.

[0153] software

[0154] GPS data collection software: Installed on the device, it acquires location information at any time.

[0155] Communication software: Sends and receives data between the device and the server.

[0156] Database software: Records and stores location information and user behavior patterns on the server.

[0157] Analysis algorithm: Installed on the server, it learns behavioral patterns from location information and calculates the optimal route.

[0158] Conversational AI model: AI software that interprets voice commands and generates appropriate instructions or advice.

[0159] System operation example

[0160] Example 1: Navigation on the go

[0161] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[0162] 2. The device converts this voice command into text and sends it to the server.

[0163] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[0164] 4. The device will provide voice guidance for this route to the user and begin navigation.

[0165] 5. The user follows the directions to safely reach the supermarket.

[0166] Example 2: Emergency response

[0167] 1. The user falls while out and presses the emergency button.

[0168] 2. The device sends an emergency notification to the server.

[0169] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[0170] 4. Emergency contacts receive a notification and rush to the user's rescue.

[0171] Implementation details

[0172] The device periodically acquires the user's current location using the built-in GPS sensor. This location information is sent to the server via wireless communication. The server stores the received location information in a database and uses it to analyze the user's behavioral patterns.

[0173] When a user sets a destination, for example by entering a voice command such as "I want to go to the supermarket," the device converts the voice into text data and sends it to the server. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device. The device then provides this route to the user via voice guidance and on-screen display, assisting them in their journey.

[0174] If a user experiences an emergency, they can press the emergency button on their device, which will send an emergency notification to the server. The server will then receive the information and notify pre-registered emergency contacts via email or SMS, enabling prompt assistance.

[0175] Furthermore, when a user inputs a voice command such as "Where are you now?", the device converts the voice data into text and sends it to the server. The server analyzes the voice data, generates an appropriate response, and sends it back to the device, which then notifies the user as voice.

[0176] This system allows elderly people to go out with peace of mind, and provides prompt support and assistance when needed. In addition, the use of conversational AI makes interactions with users natural and intuitive.

[0177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0178] Step 1:

[0179] The device uses the built-in GPS sensor to obtain the user's current location. The input is location information (latitude and longitude) from the GPS sensor, and the output is location data stored in the device. This acquisition is performed periodically, updating the location information every minute, for example.

[0180] Step 2:

[0181] The location data acquired by the device is sent to the server. The input is the location data stored in the device, and the output is the location data sent to the server. Communication is via wireless communication or the Internet, and data is sent in real time.

[0182] Step 3:

[0183] The server records the received location data in a database. The input is the location data sent from the device, and the output is the location data stored in the database. The recorded data is saved with the date and time and used for subsequent analysis.

[0184] Step 4:

[0185] The server periodically analyzes the location data recorded in the database. The input is the location data stored in the database, and the output is behavioral pattern data as a result of the analysis. A machine learning algorithm is used for the analysis, which identifies frequently visited places and routes.

[0186] Step 5:

[0187] The user speaks their destination into the device. The input is the user's voice command (e.g., "I want to go to the supermarket"), and the output is the speech data converted to text by the device. Speech recognition software performs this conversion.

[0188] Step 6:

[0189] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the text data sent to the server. Communication is performed via wireless communication or the Internet.

[0190] Step 7:

[0191] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information. The input is text data (destination), past behavioral patterns, and real-time traffic information, and the output is optimal route information. The optimal route is calculated using an algorithm.

[0192] Step 8:

[0193] The server sends the calculated optimal route to the terminal. The input is the optimal route information, and the output is the optimal route information sent to the terminal. Communication is again carried out via wireless communication or the Internet.

[0194] Step 9:

[0195] The terminal displays the optimal route to the user and provides voice guidance. The input is the optimal route information sent from the server, and the output is voice guidance and a display on the screen for the user. This task is performed by the navigation software.

[0196] Step 10:

[0197] When a user presses the emergency button, the device sends an emergency notification to the server. The input is the user's action (pressing the emergency button) and the output is the emergency notification data. The device sends data including its current location and status.

[0198] Step 11:

[0199] The server notifies emergency contacts of the current location and emergency. The input is the emergency notification data sent from the device, and the output is an email or SMS notification to the emergency contacts. The notification includes the current location and details of the emergency.

[0200] Step 12:

[0201] The user types a voice command (e.g., "Where am I?"). The input is the user's voice command, and the output is the data converted to text by the device. Speech recognition software performs the conversion.

[0202] Step 13:

[0203] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the data sent to the server.

[0204] Step 14:

[0205] The server analyzes the voice data and generates an appropriate response. The input is text data, and the output is the generated response data. The generative AI model performs this analysis and response generation.

[0206] Step 15:

[0207] The server sends response data to the terminal. The input is the generated response data, and the output is the data sent to the terminal.

[0208] Step 16:

[0209] The terminal notifies the user of the response by voice. The input is the response data sent from the server, and the output is the voice response to the user. The voice synthesis software generates the voice response.

[0210] (Application example 1)

[0211] 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."

[0212] Problems such as elderly people getting lost when out and about and being unable to respond to emergencies are serious issues in an aging society. In addition, as autonomous vehicles become more widespread, it is also necessary to ensure that elderly people can use them with peace of mind and that their safety is ensured while traveling. Conventional systems have not been able to provide sufficient safety and convenience for elderly people traveling alone.

[0213] 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.

[0214] In this invention, the server includes means for constantly acquiring location information, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for sharing location information in the autonomous vehicle, means for learning the usage history of the autonomous vehicle and the behavioral patterns of the elderly person, means for calculating an optimal route based on real-time traffic information and instructing the autonomous vehicle, means for sending an emergency notification when an emergency such as a fall occurs inside the autonomous vehicle, and means for providing voice support using interactive AI. This allows elderly people to use autonomous vehicles with peace of mind, prevents them from getting lost when they are out, and enables emergency response.

[0215] "Means for obtaining location information at any time" refers to hardware and software for obtaining location information of elderly people in real time.

[0216] "Means for transmitting acquired location data to a central processing unit" refers to the communication technology and protocols for transmitting location information acquired from a terminal to a server or cloud.

[0217] "Means for recording and analyzing location data in a central processing unit" refers to a device or system that stores received location information in a database and uses analytical algorithms to calculate movement patterns and destinations.

[0218] "Means for sharing location information in self-driving vehicles" refers to technology that enables real-time exchange of location information between devices used by elderly people and self-driving vehicles.

[0219] "Means for learning the usage history of autonomous vehicles and the behavioral patterns of elderly people" refers to an AI model that learns past usage data of autonomous vehicles and the movement history of elderly people and performs pattern recognition.

[0220] "Means for calculating the optimal route based on real-time traffic information and instructing it to an autonomous vehicle" refers to a system that calculates the optimal travel route that reflects current traffic conditions and provides that route to an autonomous vehicle.

[0221] "Means for sending emergency notifications in the event of an emergency such as a fall inside an autonomous vehicle" refers to a device or system that automatically notifies pre-set emergency contacts when an elderly person experiences an emergency inside an autonomous vehicle.

[0222] "Means for providing voice support using conversational AI" refers to an AI system that uses voice recognition technology and natural language processing to generate responses and respond verbally when an elderly person uses voice commands to ask questions or give instructions.

[0223] The present invention is a system that allows elderly people to travel safely and without getting lost when they go out using an autonomous vehicle. The system includes the following main components:

[0224] Location tracking

[0225] The device uses a GPS sensor to constantly obtain the elderly person's current location. This location information is sent to a central processing unit (server) via Wi-Fi or a mobile communication network. The obtained location data is recorded in a database on the server and used for subsequent analysis.

[0226] Learning behavioral patterns

[0227] The server analyzes the acquired location data and learns the elderly person's behavioral patterns. This analysis uses pattern recognition algorithms and machine learning techniques. This identifies frequently visited places and typical travel routes. The learned pattern data is used to provide personalized services based on individual routines.

[0228] Optimal route suggestions

[0229] When the user sets a destination using voice commands, the device sends this information to the server, which calculates the optimal route based on past behavioral pattern data and real-time traffic information, and issues instructions to the autonomous vehicle, allowing the elderly person to reach their destination via the optimal route.

[0230] emergency notification

[0231] The device is equipped with an emergency button, and if an elderly person experiences an emergency, pressing this button immediately sends an emergency notification to the server. The server then sends information about the current location and the emergency to pre-set emergency contacts, enabling a prompt response.

[0232] Conversational AI for generating advice and instructions

[0233] The device is equipped with a voice input function, and when the elderly person issues a voice command, the voice data is converted into text and sent to the server. The server analyzes the voice data and generates appropriate advice or instructions. The generated response is then notified to the elderly person via voice via the device.

[0234] Hardware and software used

[0235] GPS sensor: Obtains the elderly person's current location.

[0236] Wi-Fi / Cellular Network: Transmitting location data.

[0237] Database: Recording and storing location data.

[0238] Pattern recognition algorithms, machine learning techniques: learning behavioral patterns.

[0239] Real-time traffic information system: Calculating optimal routes.

[0240] Emergency button: Triggers emergency notifications.

[0241] Speech recognition technology, natural language processing (NLP) technology: analysis of voice input and conversational AI.

[0242] Specific examples

[0243] For example, if an elderly person says "I want to go to the hospital," the device converts this voice command into text and sends it to the server. The server calculates the optimal route based on behavioral patterns and real-time traffic information, and issues instructions to the autonomous vehicle. If an elderly person falls inside the vehicle, they can press the emergency button, which will send an emergency notification to their family and medical institutions.

[0244] Prompt Sentence Examples

[0245] "Tell me where you are."

[0246] "Tell me the best route"

[0247] "I fell, please send an emergency notification."

[0248] In this way, the invention provides an environment in which elderly people can use self-driving vehicles with peace of mind.

[0249] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0250] Step 1:

[0251] Acquiring GPS location information

[0252] Input: The device's GPS sensor detects the elderly person's current location.

[0253] Operation: The device periodically obtains location data (latitude and longitude) from the GPS module.

[0254] Output: The acquired position data is stored in memory.

[0255] Step 2:

[0256] Sending location data

[0257] Input: Location data stored on the device.

[0258] How it works: The device sends the acquired location data to a server via Wi-Fi or cellular networks.

[0259] Output: The location data received by the server.

[0260] Step 3:

[0261] Recording and analyzing location data

[0262] Input: The location data received by the server.

[0263] How it works: The server records location data in a database and analyzes the data using pattern recognition algorithms to identify frequently visited locations and travel routes.

[0264] Output: Recorded location data and analysis results (behavioral patterns).

[0265] Step 4:

[0266] Processing voice commands

[0267] Input: A voice command given by the user.

[0268] How it works: The device uses a microphone to capture voice commands, converts them into text data using speech recognition technology, and sends the text data to a server.

[0269] Output: Text data (converted voice commands).

[0270] Step 5:

[0271] Destination and optimal route calculation

[0272] Input: Text data of voice commands, behavioral pattern data, real-time traffic information.

[0273] How it works: The server identifies the destination based on voice commands and calculates the optimal route, taking into account behavioral patterns and real-time traffic information.

[0274] Output: Optimal route information.

[0275] Step 6:

[0276] Optimal route instructions

[0277] Input: Optimal route information calculated by the server.

[0278] Operation: The server sends instructions to the self-driving vehicle, which then guides the elderly person to their destination. At the same time, it sends information about the optimal route to the terminal and starts voice guidance.

[0279] Output: Navigation instructions provided to the senior citizen.

[0280] Step 7:

[0281] Emergency detection and notification

[0282] Input: The elderly person presses the emergency button on the device.

[0283] Operation: The device sends an emergency notification signal to the server, which then notifies the elderly person's location data and the emergency contacts.

[0284] Output: Emergency notification sent to emergency contacts.

[0285] Step 8:

[0286] Advice and instructions from conversational AI

[0287] Input: The next voice command the user will say.

[0288] How it works: The device again converts the voice command into text data using speech recognition technology and sends it to the server. The server uses a generative AI model to generate appropriate advice or instructions, and then uses speech synthesis technology to return the text data to the user.

[0289] Output: Spoken advice and instructions.

[0290] 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.

[0291] MODE FOR CARRYING OUT THE INVENTION

[0292] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active with peace of mind. It also incorporates an emotion engine that takes into account the user's emotions and enables flexible responses. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using conversational AI, and recognizes emotions through the emotion engine.

[0293] 1. Location Tracking

[0294] The system's device uses a GPS sensor to obtain the elderly person's current location and sends this information to a server. The server records the received location information in a database and performs analysis. This information is used to learn behavioral patterns and calculate optimal routes.

[0295] 2. Learning behavioral patterns

[0296] The server analyzes the user's movement patterns based on the received location data. It identifies frequently visited locations and routes and performs machine learning to provide personalized behavioral support for each user. Based on this learning result, it optimizes next route suggestions and emergency responses.

[0297] 3. Optimal route suggestions

[0298] When the user sets a destination, the device sends this information to the server. The server calculates the optimal route based on past behavioral pattern data and real-time traffic information, and sends the results to the device. The device then presents the optimal route to the user using voice guidance, etc.

[0299] 4. Emergency notification

[0300] If a user experiences an emergency (e.g., falls), they can press the emergency button on their device and the device will send an emergency notification along with their current location to the server. Based on the emergency notification, the server will notify pre-registered emergency contacts of their current location and situation via email or SMS.

[0301] 5. Advice and Instruction Generation by Conversational AI

[0302] Users can ask questions or give instructions to the device through voice commands. For example, when a user asks a question such as "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[0303] 6. Emotion Recognition by Emotion Engine

[0304] The system also uses an emotion engine to recognize the user's emotions. The emotion engine uses voice analysis and facial recognition technology to detect the user's emotions. For example, if the user's voice is unstable or their facial expression is tense, this information is analyzed. Based on the recognized emotions, the system generates appropriate responses or instructions.

[0305] Specific examples

[0306] Example 1: Navigation on the go

[0307] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[0308] 2. The device converts this voice command into text and sends it to the server.

[0309] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[0310] 4. The device will provide voice guidance for this route to the user and begin navigation.

[0311] 5. The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "I'll guide you a little more slowly so you can feel at ease."

[0312] Example 2: Emergency response

[0313] 1. The user falls while out and presses the emergency button.

[0314] 2. The device sends an emergency notification to the server.

[0315] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[0316] 4. The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Remain calm, help is on the way," and sends it to the device.

[0317] 5. The device will then audibly convey this message to the user.

[0318] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[0319] The processing flow will be explained below.

[0320] Location tracking

[0321] Step 1:

[0322] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[0323] Step 2:

[0324] The location information acquired by the device is temporarily stored in memory.

[0325] Step 3:

[0326] The device sends location information to the server at set intervals (e.g., every minute).

[0327] Step 4:

[0328] The server records the received location information in a database.

[0329] Step 5:

[0330] The server records the time the location information was received and waits for the next update.

[0331] Learning behavioral patterns

[0332] Step 1:

[0333] The server retrieves previously stored location information from the database.

[0334] Step 2:

[0335] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[0336] Step 3:

[0337] The server creates a dataset based on the analysis results to learn user behavior patterns.

[0338] Step 4:

[0339] The server uses a machine learning model to learn user behavior patterns and stores the results.

[0340] Optimal route suggestions

[0341] Step 1:

[0342] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[0343] Step 2:

[0344] The terminal transmits the set destination to the server.

[0345] Step 3:

[0346] The server acquires the user's past behavioral patterns and real-time traffic information.

[0347] Step 4:

[0348] The server calculates the optimal travel route and sends the calculation results to the terminal.

[0349] Step 5:

[0350] The terminal displays the optimal route to the user and starts voice guidance.

[0351] emergency notification

[0352] Step 1:

[0353] When an emergency occurs, the user presses the emergency button on the terminal.

[0354] Step 2:

[0355] The device detects when the emergency button is pressed and obtains the current location information.

[0356] Step 3:

[0357] The device sends the acquired location information and emergency notification to the server.

[0358] Step 4:

[0359] The server will send your location and emergency information to your emergency contacts via email or SMS.

[0360] Conversational AI for generating advice and instructions

[0361] Step 1:

[0362] The user speaks a question to the device, such as "Where are you now?"

[0363] Step 2:

[0364] The device converts the voice data it receives into text and sends it to the server.

[0365] Step 3:

[0366] The server parses the received text data and generates an appropriate response.

[0367] Step 4:

[0368] The server generates a response and sends it to the terminal in text format.

[0369] Step 5:

[0370] The terminal converts the text data into speech and provides the user with the response content.

[0371] Emotion recognition by emotion engine

[0372] Step 1:

[0373] The user gives instructions and asks questions to the terminal by voice.

[0374] Step 2:

[0375] The terminal records the user's voice and transmits the voice data to the server.

[0376] Step 3:

[0377] The server analyzes the voice data and recognizes the user's emotions using an emotion engine.

[0378] Step 4:

[0379] The server generates appropriate advice and instructions based on the emotional state.

[0380] Step 5:

[0381] The server sends the generated advice and instructions to the terminal, which then conveys them to the user by voice.

[0382] Specific examples

[0383] Example 1: Navigation on the go

[0384] Step 1:

[0385] The user speaks to the terminal, saying, "I want to go to the supermarket."

[0386] Step 2:

[0387] The device converts this voice command into text and sends it to the server.

[0388] Step 3:

[0389] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[0390] Step 4:

[0391] The server sends the calculation results to the terminal.

[0392] Step 5:

[0393] The terminal will then provide voice guidance for this route to the user and begin navigation.

[0394] Step 6:

[0395] The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "We'll guide you a little more slowly so that you can feel at ease."

[0396] Example 2: Emergency response

[0397] Step 1:

[0398] The user falls while out and presses the emergency button.

[0399] Step 2:

[0400] The device sends an emergency notification to the server.

[0401] Step 3:

[0402] The server will notify your emergency contact of your current location and status via email or SMS.

[0403] Step 4:

[0404] The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Stay calm, help is on the way," and sends it to the device.

[0405] Step 5:

[0406] The terminal will then audibly convey this message to the user.

[0407] Example 2

[0408] 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."

[0409] There is an urgent need to improve the efficiency of responding to lost or emergency situations when elderly people are out and about, and to provide an environment where they can be active and feel safe. There is also a need for flexible support that takes into account the user's emotions. Conventional technologies have limited functionality for acquiring location information and emergency notifications, and lack the ability to recognize the user's emotions in real time and respond accordingly. Therefore, it is necessary to provide a system that allows elderly people to go out safely and with peace of mind.

[0410] 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.

[0411] In this invention, the server includes a means for constantly acquiring location information, a means for transmitting acquired location data to a central processing unit, a means for recording and analyzing the location data in the central processing unit, a means for calculating an optimal route and generating travel instructions based on the analysis results, a means for automatically notifying emergency contacts in the event of an emergency, and a means for analyzing the user's voice and facial expressions to recognize emotions and generate an appropriate response. This allows elderly people to constantly track their location, reducing the risk of getting lost and allowing them to receive prompt notification and assistance in the event of an emergency. Furthermore, the emotion recognition function provides more personalized support, increasing the user's sense of security and trust.

[0412] "Means for acquiring location information at any time" is a general term for sensors and devices that can acquire multiple pieces of location information in real time, as well as methods for acquiring that data.

[0413] "Means for transmitting acquired location data to a central processing unit" is a general term for means for collecting location information and transmitting it to a remote server or central processing unit via communication means.

[0414] "Central Processing Unit" is a general term for a computer system or server that records and analyzes received data.

[0415] "Means for recording and analyzing location data" is a general term for software and algorithms that store location information and analyze user behavior patterns and trends based on that data.

[0416] "Means for calculating the optimal route and generating travel instructions" is a general term for functions that take into account the user's current location, destination, and suspicious points, calculate an efficient travel route, and provide instructions to the user.

[0417] "Means for automatically notifying emergency contacts in the event of an emergency" is a general term for a system or function that automatically notifies pre-registered emergency contacts of current location information, etc. when an emergency occurs, such as when a user presses an emergency button.

[0418] "Means of analyzing a user's voice and facial expressions to recognize emotions and generate appropriate responses" is a general term for technologies and algorithms that read emotions from a user's voice and facial expressions and provide appropriate responses and guidance based on that information.

[0419] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, notifies users in emergencies, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine, providing flexible responses and a sense of security to users.

[0420] Location tracking

[0421] The system's terminals are equipped with GPS sensors that constantly acquire the elderly person's current location. The terminals transmit this location information in real time to a server, which then records the received location information in a database. The server then analyzes the location information to determine the user's current location and movement patterns.

[0422] Learning behavioral patterns

[0423] The server analyzes the user's behavioral patterns based on the accumulated location data, including frequently visited locations and specific travel routes. Using the results of this analysis, the server uses machine learning algorithms to learn the user's behavioral patterns and optimize next route suggestions and emergency response.

[0424] Optimal route suggestions

[0425] When the user sets their destination, the device sends this information to the server. The server uses past behavioral pattern data and real-time traffic information to calculate the optimal route and sends the results to the device. The device then presents the optimal route to the user through voice guidance and a display.

[0426] emergency notification

[0427] If a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server. Based on the emergency notification received, the server notifies pre-registered emergency contacts of the user's current location and situation via email or SMS. In addition, the server uses an emotion engine to analyze the user's emotional state, generates an appropriate reassuring message, and sends it to the device. The device then conveys this message to the user via voice.

[0428] Conversational AI for generating advice and instructions

[0429] Users can ask questions or give instructions to the device through voice commands. For example, if a user asks "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[0430] Emotion recognition by emotion engine

[0431] The system recognizes the user's emotions using an emotion engine, which uses voice analysis and facial recognition technology to detect the user's emotional state. For example, if the user's voice is unstable or their facial expression is tense, the system will analyze this and generate appropriate responses or instructions.

[0432] Specific examples

[0433] Prompt Sentence Examples

[0434] Explain how a system works when it suggests the optimal route when a user says they want to go to the supermarket.

[0435] Describe how a system works that sends an emergency notification when a user falls.

[0436] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[0437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0438] Step 1:

[0439] The user goes out with the terminal.

[0440] Specific operation: The user carries the device when out and about. The device has a built-in GPS sensor, so location information can be obtained at all times.

[0441] Step 2:

[0442] The device obtains location information using the GPS sensor.

[0443] Input: User's current location (GPS data).

[0444] Specific operation: The device uses the GPS sensor at regular intervals (e.g., every 10 seconds) to determine its current location.

[0445] Output: Current location (latitude and longitude).

[0446] Step 3:

[0447] The location information acquired by the device is sent to the server.

[0448] Input: Current location (latitude and longitude).

[0449] What it does: The device sends real-time location information to the server over a mobile network or Wi-Fi.

[0450] Output: The location sent to the server.

[0451] Step 4:

[0452] The server records the received location information in a database.

[0453] Input: The location information sent.

[0454] Specific operation: The server stores the location information it receives in a database in chronological order.

[0455] Output: Location information recorded in a database.

[0456] Step 5:

[0457] The server analyzes the location information.

[0458] Input: Location information stored in a database.

[0459] How it works: The server analyzes the user's movement patterns and frequently visited locations based on the accumulated location information. Clustering and time series analysis algorithms are used.

[0460] Output: Analysis results (user movement patterns and frequently visited places).

[0461] Step 6:

[0462] The user sets the destination.

[0463] Specific action: The user enters a destination using voice commands or a touchscreen.

[0464] Input: Destination information.

[0465] Step 7:

[0466] The terminal transmits the destination information to the server.

[0467] Input: Destination information.

[0468] Specific operation: The device sends the entered destination information to the server.

[0469] Output: The destination information sent to the server.

[0470] Step 8:

[0471] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information.

[0472] Input: Historical behavioral pattern data, real-time traffic information, and destination information.

[0473] Specific operation: The server calculates the optimal route using past recorded behavior data and real-time traffic information (e.g., Google (registered trademark) Maps API).

[0474] Output: Optimal route directions.

[0475] Step 9:

[0476] The server sends the calculation results to the terminal.

[0477] Input: Best Route Directions information.

[0478] Specific operation: The server sends the generated optimal route information to the terminal.

[0479] Output: The best route directions sent to the device.

[0480] Step 10:

[0481] The terminal presents the optimal route to the user using voice guidance, etc.

[0482] Input: Best Route Directions information.

[0483] Specific operation: The device guides the user to the optimal route through voice navigation and on-screen display.

[0484] Output: The optimal route presented to the user.

[0485] Step 11:

[0486] If the user encounters an emergency, he presses the emergency button.

[0487] Specific action: The user presses the emergency button on the device.

[0488] Input: Emergency button press information.

[0489] Step 12:

[0490] The device sends an emergency notification to the server along with its current location.

[0491] Input: Emergency button press information, current location information.

[0492] Specific operation: The device sends emergency button press information and current location information to the server.

[0493] Output: The emergency notification information sent to the server.

[0494] Step 13:

[0495] The server notifies the emergency contact of the current location and situation.

[0496] Input: Emergency notification information, emergency contact information.

[0497] Specific operation: The server notifies registered emergency contacts of the current location and situation via email or SMS.

[0498] Output: The notification message sent to the emergency contact.

[0499] Step 14:

[0500] The emotion engine analyzes the user's voice and facial expressions.

[0501] Input: User's voice data, facial expression data (camera footage).

[0502] How it works: The emotion engine uses voice analysis and facial recognition algorithms to analyze the user's emotions.

[0503] Output: The user's emotional state (anxious, relieved, etc.).

[0504] Step 15:

[0505] The server generates a reassuring message and sends it to the device.

[0506] Input: The user's emotional state.

[0507] Specific behavior: The server generates a message to reassure the user based on the analysis results.

[0508] Output: The reassurance message sent to the device.

[0509] Step 16:

[0510] The terminal will then audibly convey the message to the user.

[0511] Input: Reassurance message.

[0512] Specific operation: The device uses speech synthesis technology to audibly convey a reassuring message to the user.

[0513] Output: The message spoken to the user.

[0514] (Application example 2)

[0515] 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."

[0516] The challenge is to reduce the risk of elderly people getting lost when they go out and provide them with a means of moving safely and with peace of mind. Another important challenge is to respond quickly and appropriately in emergencies and reduce the anxiety of elderly people. Furthermore, there is a need to realize more effective mobility support by learning the individual behavioral patterns of elderly people and providing personalized assistance.

[0517] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for constantly acquiring location information; means for transmitting the acquired location data to a central processing unit; means for recording and analyzing the location data in the central processing unit; means for calculating an optimal route and generating travel instructions based on the analysis results; means for automatically notifying emergency contacts in the event of an emergency; means for learning behavioral patterns based on the acquired location data and identifying frequently visited locations and routes; means for calculating an optimal route based on past behavioral patterns and real-time traffic information and reflecting the results in the autonomous vehicle; means for accepting voice commands, transmitting voice data to the central processing unit, and generating appropriate advice and instructions; and means for detecting user emotions using voice analysis and facial recognition technology and generating appropriate responses. This reduces the risk of elderly people getting lost when out and allows them to travel safely and with peace of mind. It also enables prompt and appropriate responses in emergencies to alleviate elderly anxiety. Furthermore, personalized assistance based on behavioral pattern learning can be provided, thereby achieving more effective mobility support.

[0518] A "means for continuously obtaining location information" is a device or method that continuously obtains the current location of an elderly person using a GPS sensor or other location tracking technology.

[0519] The "means for transmitting the acquired location data to the central processing unit" refers to a communication means for transmitting the elderly person's current location information to a server or cloud computing system via a network.

[0520] "Means for recording and analyzing location data at a central processing unit" refers to a computer system and algorithms for recording transmitted location information in a database and analyzing the data.

[0521] The "means for calculating the optimal route based on the analysis results and generating travel instructions" refers to an algorithm or system that calculates the most suitable travel route for an elderly person and generates travel instructions based on the analysis results of location data and traffic information.

[0522] "Means for automatically notifying emergency contacts in the event of an emergency" refers to an alarm system and communication means for automatically notifying pre-registered contact persons in the event of an emergency.

[0523] "Means for learning behavioral patterns based on acquired location data and identifying frequently visited places and routes" refers to a machine learning algorithm that analyzes collected location data to learn the behavioral patterns of elderly people and identify frequently visited places and routes they use.

[0524] "Means of calculating the optimal route based on past behavioral patterns and real-time traffic information, and reflecting this in autonomous vehicles" refers to technology that determines the optimal travel route based on accumulated behavioral data and current traffic conditions, and integrates this information into the route planning system of autonomous vehicles.

[0525] The "means for accepting voice commands, transmitting voice data to a central processing unit, and generating appropriate advice or instructions" refers to a speech recognition and natural language processing system that recognizes voice input from the senior, converts the speech into text, transmits it to a central processing unit, and generates and returns an appropriate response.

[0526] "Means for detecting a user's emotions using voice analysis and facial recognition technology and generating an appropriate response" refers to an emotion analysis engine and facial recognition system that identifies a user's emotional state by analyzing changes in voice tone and facial expression, and generates an appropriate response according to that state.

[0527] The following describes in detail the mode for carrying out this invention. This invention is a system that reduces the risk of elderly people getting lost when they go out, allowing them to move around safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine.

[0528] The implementation of this system uses the following hardware and software:

[0529] Hardware and software used

[0530] 1. GPS sensor: Used to obtain the location of the elderly person at any time.

[0531] 2. Central processing unit (server): Contains a database and analysis algorithms for recording and analyzing acquired location data.

[0532] 3. Communication unit: A network communication device for transmitting acquired location data to a server.

[0533] 4. Self-driving vehicles: Vehicles that safely transport seniors to their destinations.

[0534] 5. Conversational AI systems: Software for speech recognition and generating appropriate advice and instructions.

[0535] 6. Emotion Analysis Engine and Facial Recognition System: Software for detecting user emotions using voice analysis and facial recognition technology.

[0536] Processing flow

[0537] 1. Location information acquisition: The server acquires the current location of the elderly person from the GPS sensor. This data is sequentially sent to the server and recorded in the database.

[0538] 2. Learning behavioral patterns: The server analyzes the acquired location data and learns the behavioral patterns of the elderly, for example, identifying frequently visited places and routes.

[0539] 3. Calculating the optimal route: When the elderly person inputs their destination, the server calculates the optimal route based on their past behavior patterns and real-time traffic information, and sends that information to the autonomous vehicle, which then uses this information to direct the elderly person to their destination.

[0540] 4. Emergency notification: If an elderly person experiences an emergency, such as a fall, they can press the emergency button, which sends an emergency notification along with their current location to the server. The server then notifies the emergency contact and generates a response to reassure the elderly person and sends it to their device.

[0541] 5. Conversational AI for advice and instruction generation: Users can ask questions or give instructions to the server through voice commands. The server analyzes this voice data and generates appropriate responses.

[0542] 6. Emotion Recognition: Using an emotion analysis engine and facial recognition system, the system detects the user's emotions and generates appropriate responses. For example, if the user appears anxious, the system generates a reassuring response.

[0543] Specific examples

[0544] Example 1: If an elderly person gets into an autonomous vehicle and says, "I want to go to the supermarket," the system will calculate the optimal route based on their past behavioral patterns and current traffic information, and reflect this in the autonomous vehicle. If the user seems anxious, the system will respond by saying, "Don't worry, we'll proceed slowly."

[0545] Example 2: If an elderly person falls while out, pressing the emergency button will send their current location information to the server, which will automatically notify emergency contacts. The server will then generate a message such as "Help is on the way, so don't worry," and the message will be played by voice on the device.

[0546] Prompt Sentence Examples

[0547] The following prompt sentence can be used to have a generative AI model create a detailed response about support systems for the elderly:

[0548] text

[0549] Please explain the support system for elderly people using self-driving vehicles. This system supports elderly people in going out safely and securely, and has functions such as location tracking, learning behavioral patterns, suggesting optimal routes, emergency notifications, emotion recognition using an emotion engine, and advice through conversational AI. Specifically, when an elderly person says, "I want to go to the supermarket," the system calculates the optimal route and provides a sense of security.

[0550] In this way, the mode for carrying out the invention can be concretely shown.

[0551] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0552] Step 1:

[0553] The user uses the terminal to input a voice command such as "I want to go to the supermarket." This voice data is converted into text data by the terminal and sent to the server.

[0554] Step 2:

[0555] The server receives the input text data and analyzes it based on past behavioral patterns and real-time traffic information. The results of this analysis are used to calculate the optimal route. In calculating this optimal route, data such as behavioral patterns and frequently visited locations is used to process and calculate the data.

[0556] Step 3:

[0557] The server sends the calculated optimal route to the autonomous vehicle's system, which uses this information to update its route plan and safely begin driving. The output is the updated route information.

[0558] Step 4:

[0559] The terminal provides navigation information to the user through voice guidance. The user's location information is acquired continuously and transmitted to the server in real time. The output is the user's current location information.

[0560] Step 5:

[0561] The server analyzes the location information and continues to learn behavioral patterns. Specifically, it analyzes the data to identify frequently visited locations and routes. The results of this analysis are used to suggest the next route.

[0562] Step 6:

[0563] If a user encounters an emergency while out and about, they press the emergency button on their device. The device then sends their current location and emergency notification to the server. The server then receives this information and notifies pre-registered emergency contacts. The output is the information of the notified emergency contacts and their current situation.

[0564] Step 7:

[0565] The server uses voice analysis and facial recognition technology to detect the user's emotions. Specifically, it analyzes voice tone and facial expressions to identify emotional states such as anxiety or tension. The results of this analysis are sent to the conversational AI system. The output is analyzed emotional data.

[0566] Step 8:

[0567] The conversational AI system generates an appropriate response based on the user's emotional state. For example, if the user seems anxious, it generates a response such as "Don't worry." This response is sent to the terminal and notified to the user by voice. The output is the generated response message.

[0568] This enables the entire system to function as a support system for elderly people to go out safely and securely.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Second embodiment]

[0573] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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."

[0585] MODE FOR CARRYING OUT THE INVENTION

[0586] This invention is a system that enables elderly people to go out safely and without getting lost. The system has functions for tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[0587] 1. Location Tracking

[0588] The system's terminal uses a GPS sensor to constantly obtain the elderly person's current location and transmits this information to a server. The server records the received location information in a database and performs analysis. This information is later used to learn behavioral patterns and calculate optimal routes.

[0589] 2. Learning behavioral patterns

[0590] The server analyzes the user's movement patterns based on the received location data and identifies frequently visited locations and routes, allowing the server to learn each user's individual behavioral patterns and provide personalized services.

[0591] 3. Optimal route suggestions

[0592] When the user sets a destination, the device notifies the server. The server calculates the optimal route based on past behavior patterns and real-time traffic information, and sends the results to the device. The device then displays this route to the user and provides voice guidance.

[0593] 4. Emergency notification

[0594] When a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server, which then immediately sends their current location and emergency information to their emergency contacts to promptly respond.

[0595] 5. Advice and Instruction Generation by Conversational AI

[0596] Users can ask questions or give instructions to the device using voice commands. For example, when a user asks a question such as "Where are you now?", the device converts the voice into text data and sends it to the server. The server analyzes the voice data, generates an appropriate response (e.g., "You are currently on Main Street"), and sends it to the device. The device then notifies the user of this response by voice.

[0597] Specific examples

[0598] Example 1: Navigation on the go

[0599] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[0600] 2. The device converts this voice command into text and sends it to the server.

[0601] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[0602] 4. The device will provide voice guidance for this route to the user and begin navigation.

[0603] 5. The user follows the directions to safely reach the supermarket.

[0604] Example 2: Emergency response

[0605] 1. The user falls while out and presses the emergency button.

[0606] 2. The device sends an emergency notification to the server.

[0607] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[0608] 4. Emergency contacts will be notified and will take necessary action.

[0609] This system not only allows seniors to feel safe when going out, but also allows for a quick response in the event of an emergency, and uses conversational AI to provide an even higher level of support and engagement.

[0610] The processing flow will be explained below.

[0611] Location tracking

[0612] Step 1:

[0613] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[0614] Step 2:

[0615] The location information acquired by the device is temporarily stored in memory.

[0616] Step 3:

[0617] The device sends location information to the server at set intervals (e.g., every minute).

[0618] Step 4:

[0619] The server records the received location information in a database.

[0620] Step 5:

[0621] The server records the time the location information was received and waits for the next update.

[0622] Learning behavioral patterns

[0623] Step 1:

[0624] The server retrieves previously stored location information from the database.

[0625] Step 2:

[0626] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[0627] Step 3:

[0628] The server creates a dataset based on the analysis results to learn user behavior patterns.

[0629] Step 4:

[0630] The server uses a machine learning model to learn user behavior patterns and stores the results.

[0631] Optimal route suggestions

[0632] Step 1:

[0633] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[0634] Step 2:

[0635] The terminal transmits the set destination to the server.

[0636] Step 3:

[0637] The server acquires the user's past behavioral patterns and real-time traffic information.

[0638] Step 4:

[0639] The server calculates the optimal travel route and sends the calculation results to the terminal.

[0640] Step 5:

[0641] The terminal displays the optimal route to the user and starts voice guidance.

[0642] emergency notification

[0643] Step 1:

[0644] When an emergency occurs, the user presses the emergency button on the terminal.

[0645] Step 2:

[0646] The device detects when the emergency button is pressed and obtains the current location information.

[0647] Step 3:

[0648] The device sends the acquired location information and emergency notification to the server.

[0649] Step 4:

[0650] The server will send your location and emergency information to your emergency contacts via email or SMS.

[0651] Conversational AI for generating advice and instructions

[0652] Step 1:

[0653] The user speaks a question to the device, such as "Where are you now?"

[0654] Step 2:

[0655] The device converts the voice data it receives into text and sends it to the server.

[0656] Step 3:

[0657] The server parses the received text data and generates an appropriate response.

[0658] Step 4:

[0659] The server generates a response and sends it to the terminal in text format.

[0660] Step 5:

[0661] The terminal converts the text data into speech and provides the user with the response content.

[0662] Specific examples

[0663] Example 1: Navigation on the go

[0664] Step 1:

[0665] The user speaks to the terminal, saying, "I want to go to the supermarket."

[0666] Step 2:

[0667] The device converts this voice command into text and sends it to the server.

[0668] Step 3:

[0669] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[0670] Step 4:

[0671] The server sends the calculation results to the terminal.

[0672] Step 5:

[0673] The terminal will then provide voice guidance for this route to the user and begin navigation.

[0674] Example 2: Emergency response

[0675] Step 1:

[0676] The user falls while out and presses the emergency button.

[0677] Step 2:

[0678] The device sends an emergency notification to the server.

[0679] Step 3:

[0680] The server will notify your emergency contact of your current location and status via email or SMS.

[0681] Step 4:

[0682] Emergency contacts will be notified and will take necessary action.

[0683] Example 1

[0684] 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."

[0685] In modern society, it is important for elderly people to be able to go out safely and with peace of mind. However, concerns about getting lost or traveling, as well as rapid response in emergencies, remain issues. Elderly people, in particular, tend to have limited mobility, so rapid response is required in emergencies. However, existing systems rarely provide integrated location tracking, behavioral pattern learning, optimal route suggestions, and emergency notifications, and they also lack support from conversational AI. Therefore, there is a need for a more comprehensive and personalized outing support system for elderly people.

[0686] 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.

[0687] In this invention, the server includes means for constantly acquiring locations, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for calculating an optimal route and generating travel instructions based on the analysis results, means for automatically notifying emergency contacts in the event of an emergency, means for converting voice commands into text data, means for transmitting voice data to the central processing unit and generating an appropriate response, and means for generating a voice response and notifying the user. This allows elderly people to go out with peace of mind, enables quick response in the event of an emergency, and further enables personalized support based on behavioral patterns and advanced support using conversational AI to be provided.

[0688] "Means for obtaining location information on a regular basis" refers to a device or method that periodically obtains the user's current location using a GPS sensor or other location measurement device.

[0689] "Means for transmitting to the central processing unit" refers to a device or method for transmitting location data and other necessary information to the central processing unit via wireless communication, the Internet, etc.

[0690] "Central processing unit" refers to a computer or server that records received data and performs analysis, etc.

[0691] "Means for recording and analyzing location data" refers to a device or method that stores received location data in a database or the like and analyzes the user's behavioral patterns and current location based on this data.

[0692] "Means for calculating the optimal route and generating travel instructions" refers to a device or method that calculates the optimal route to a destination for a user based on past behavioral patterns and real-time traffic information, and generates the contents of that route as instructions.

[0693] "Means for automatically notifying emergency contacts in the event of an emergency" refers to a device or method that automatically notifies emergency contacts when a user is in an emergency.

[0694] The "means for converting voice commands into text data" refers to a device or method for converting voice commands input by a user into text format.

[0695] The "means for transmitting voice data to the central processing unit" refers to a device or method for transmitting the converted text data to the central processing unit.

[0696] The "means for generating an appropriate response" is an algorithm or software that generates an appropriate response in response to a voice command.

[0697] The "means for generating a voice response and notifying the user" refers to a device or method for notifying the user of the generated appropriate response as voice.

[0698] This invention is a system that helps elderly people move around safely and securely when they go out. The system has the functions of tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[0699] Hardware and software used

[0700] Hardware

[0701] Terminal: A device with a built-in GPS sensor that captures the user's location. This includes smartphones and dedicated mobile terminals.

[0702] Server: A computing device that receives, records, analyzes data, and generates appropriate responses and routes.

[0703] software

[0704] GPS data collection software: Installed on the device, it acquires location information at any time.

[0705] Communication software: Sends and receives data between the device and the server.

[0706] Database software: Records and stores location information and user behavior patterns on the server.

[0707] Analysis algorithm: Installed on the server, it learns behavioral patterns from location information and calculates the optimal route.

[0708] Conversational AI model: AI software that interprets voice commands and generates appropriate instructions or advice.

[0709] System operation example

[0710] Example 1: Navigation on the go

[0711] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[0712] 2. The device converts this voice command into text and sends it to the server.

[0713] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[0714] 4. The device will provide voice guidance for this route to the user and begin navigation.

[0715] 5. The user follows the directions to safely reach the supermarket.

[0716] Example 2: Emergency response

[0717] 1. The user falls while out and presses the emergency button.

[0718] 2. The device sends an emergency notification to the server.

[0719] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[0720] 4. Emergency contacts receive a notification and rush to the user's rescue.

[0721] Implementation details

[0722] The device periodically acquires the user's current location using the built-in GPS sensor. This location information is sent to the server via wireless communication. The server stores the received location information in a database and uses it to analyze the user's behavioral patterns.

[0723] When a user sets a destination, for example by entering a voice command such as "I want to go to the supermarket," the device converts the voice into text data and sends it to the server. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device. The device then provides this route to the user via voice guidance and on-screen display, assisting them in their journey.

[0724] If a user experiences an emergency, they can press the emergency button on their device, which will send an emergency notification to the server. The server will then receive the information and notify pre-registered emergency contacts via email or SMS, enabling prompt assistance.

[0725] Furthermore, when a user inputs a voice command such as "Where are you now?", the device converts the voice data into text and sends it to the server. The server analyzes the voice data, generates an appropriate response, and sends it back to the device, which then notifies the user as voice.

[0726] This system allows elderly people to go out with peace of mind, and provides prompt support and assistance when needed. In addition, the use of conversational AI makes interactions with users natural and intuitive.

[0727] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0728] Step 1:

[0729] The device uses the built-in GPS sensor to obtain the user's current location. The input is location information (latitude and longitude) from the GPS sensor, and the output is location data stored in the device. This acquisition is performed periodically, updating the location information every minute, for example.

[0730] Step 2:

[0731] The location data acquired by the device is sent to the server. The input is the location data stored in the device, and the output is the location data sent to the server. Communication is via wireless communication or the Internet, and data is sent in real time.

[0732] Step 3:

[0733] The server records the received location data in a database. The input is the location data sent from the device, and the output is the location data stored in the database. The recorded data is saved with the date and time and used for subsequent analysis.

[0734] Step 4:

[0735] The server periodically analyzes the location data recorded in the database. The input is the location data stored in the database, and the output is behavioral pattern data as a result of the analysis. A machine learning algorithm is used for the analysis, which identifies frequently visited places and routes.

[0736] Step 5:

[0737] The user speaks their destination into the device. The input is the user's voice command (e.g., "I want to go to the supermarket"), and the output is the speech data converted to text by the device. Speech recognition software performs this conversion.

[0738] Step 6:

[0739] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the text data sent to the server. Communication is performed via wireless communication or the Internet.

[0740] Step 7:

[0741] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information. The input is text data (destination), past behavioral patterns, and real-time traffic information, and the output is optimal route information. The optimal route is calculated using an algorithm.

[0742] Step 8:

[0743] The server sends the calculated optimal route to the terminal. The input is the optimal route information, and the output is the optimal route information sent to the terminal. Communication is again carried out via wireless communication or the Internet.

[0744] Step 9:

[0745] The terminal displays the optimal route to the user and provides voice guidance. The input is the optimal route information sent from the server, and the output is voice guidance and a display on the screen for the user. This task is performed by the navigation software.

[0746] Step 10:

[0747] When a user presses the emergency button, the device sends an emergency notification to the server. The input is the user's action (pressing the emergency button) and the output is the emergency notification data. The device sends data including its current location and status.

[0748] Step 11:

[0749] The server notifies emergency contacts of the current location and emergency. The input is the emergency notification data sent from the device, and the output is an email or SMS notification to the emergency contacts. The notification includes the current location and details of the emergency.

[0750] Step 12:

[0751] The user types a voice command (e.g., "Where am I?"). The input is the user's voice command, and the output is the data converted to text by the device. Speech recognition software performs the conversion.

[0752] Step 13:

[0753] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the data sent to the server.

[0754] Step 14:

[0755] The server analyzes the voice data and generates an appropriate response. The input is text data, and the output is the generated response data. The generative AI model performs this analysis and response generation.

[0756] Step 15:

[0757] The server sends response data to the terminal. The input is the generated response data, and the output is the data sent to the terminal.

[0758] Step 16:

[0759] The terminal notifies the user of the response by voice. The input is the response data sent from the server, and the output is the voice response to the user. The voice synthesis software generates the voice response.

[0760] (Application example 1)

[0761] 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."

[0762] Problems such as elderly people getting lost when out and about and being unable to respond to emergencies are serious issues in an aging society. In addition, as autonomous vehicles become more widespread, it is also necessary to ensure that elderly people can use them with peace of mind and that their safety is ensured while traveling. Conventional systems have not been able to provide sufficient safety and convenience for elderly people traveling alone.

[0763] 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.

[0764] In this invention, the server includes means for constantly acquiring location information, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for sharing location information in the autonomous vehicle, means for learning the usage history of the autonomous vehicle and the behavioral patterns of the elderly person, means for calculating an optimal route based on real-time traffic information and instructing the autonomous vehicle, means for sending an emergency notification when an emergency such as a fall occurs inside the autonomous vehicle, and means for providing voice support using interactive AI. This allows elderly people to use autonomous vehicles with peace of mind, prevents them from getting lost when they are out, and enables emergency response.

[0765] "Means for obtaining location information at any time" refers to hardware and software for obtaining location information of elderly people in real time.

[0766] "Means for transmitting acquired location data to a central processing unit" refers to the communication technology and protocols for transmitting location information acquired from a terminal to a server or cloud.

[0767] "Means for recording and analyzing location data in a central processing unit" refers to a device or system that stores received location information in a database and uses analytical algorithms to calculate movement patterns and destinations.

[0768] "Means for sharing location information in self-driving vehicles" refers to technology that enables real-time exchange of location information between devices used by elderly people and self-driving vehicles.

[0769] "Means for learning the usage history of autonomous vehicles and the behavioral patterns of elderly people" refers to an AI model that learns past usage data of autonomous vehicles and the movement history of elderly people and performs pattern recognition.

[0770] "Means for calculating the optimal route based on real-time traffic information and instructing it to an autonomous vehicle" refers to a system that calculates the optimal travel route that reflects current traffic conditions and provides that route to an autonomous vehicle.

[0771] "Means for sending emergency notifications in the event of an emergency such as a fall inside an autonomous vehicle" refers to a device or system that automatically notifies pre-set emergency contacts when an elderly person experiences an emergency inside an autonomous vehicle.

[0772] "Means for providing voice support using conversational AI" refers to an AI system that uses voice recognition technology and natural language processing to generate responses and respond verbally when an elderly person uses voice commands to ask questions or give instructions.

[0773] The present invention is a system that allows elderly people to travel safely and without getting lost when they go out using an autonomous vehicle. The system includes the following main components:

[0774] Location tracking

[0775] The device uses a GPS sensor to constantly obtain the elderly person's current location. This location information is sent to a central processing unit (server) via Wi-Fi or a mobile communication network. The obtained location data is recorded in a database on the server and used for subsequent analysis.

[0776] Learning behavioral patterns

[0777] The server analyzes the acquired location data and learns the elderly person's behavioral patterns. This analysis uses pattern recognition algorithms and machine learning techniques. This identifies frequently visited places and typical travel routes. The learned pattern data is used to provide personalized services based on individual routines.

[0778] Optimal route suggestions

[0779] When the user sets a destination using voice commands, the device sends this information to the server, which calculates the optimal route based on past behavioral pattern data and real-time traffic information, and issues instructions to the autonomous vehicle, allowing the elderly person to reach their destination via the optimal route.

[0780] emergency notification

[0781] The device is equipped with an emergency button, and if an elderly person experiences an emergency, pressing this button immediately sends an emergency notification to the server. The server then sends information about the current location and the emergency to pre-set emergency contacts, enabling a prompt response.

[0782] Conversational AI for generating advice and instructions

[0783] The device is equipped with a voice input function, and when the elderly person issues a voice command, the voice data is converted into text and sent to the server. The server analyzes the voice data and generates appropriate advice or instructions. The generated response is then notified to the elderly person via voice via the device.

[0784] Hardware and software used

[0785] GPS sensor: Obtains the elderly person's current location.

[0786] Wi-Fi / Cellular Network: Transmitting location data.

[0787] Database: Recording and storing location data.

[0788] Pattern recognition algorithms, machine learning techniques: learning behavioral patterns.

[0789] Real-time traffic information system: Calculating optimal routes.

[0790] Emergency button: Triggers emergency notifications.

[0791] Speech recognition technology, natural language processing (NLP) technology: analysis of voice input and conversational AI.

[0792] Specific examples

[0793] For example, if an elderly person says "I want to go to the hospital," the device converts this voice command into text and sends it to the server. The server calculates the optimal route based on behavioral patterns and real-time traffic information, and issues instructions to the autonomous vehicle. If an elderly person falls inside the vehicle, they can press the emergency button, which will send an emergency notification to their family and medical institutions.

[0794] Prompt Sentence Examples

[0795] "Tell me where you are."

[0796] "Tell me the best route"

[0797] "I fell, please send an emergency notification."

[0798] In this way, the invention provides an environment in which elderly people can use self-driving vehicles with peace of mind.

[0799] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0800] Step 1:

[0801] Acquiring GPS location information

[0802] Input: The device's GPS sensor detects the elderly person's current location.

[0803] Operation: The device periodically obtains location data (latitude and longitude) from the GPS module.

[0804] Output: The acquired position data is stored in memory.

[0805] Step 2:

[0806] Sending location data

[0807] Input: Location data stored on the device.

[0808] How it works: The device sends the acquired location data to a server via Wi-Fi or cellular networks.

[0809] Output: The location data received by the server.

[0810] Step 3:

[0811] Recording and analyzing location data

[0812] Input: The location data received by the server.

[0813] How it works: The server records location data in a database and analyzes the data using pattern recognition algorithms to identify frequently visited locations and travel routes.

[0814] Output: Recorded location data and analysis results (behavioral patterns).

[0815] Step 4:

[0816] Processing voice commands

[0817] Input: A voice command given by the user.

[0818] How it works: The device uses a microphone to capture voice commands, converts them into text data using speech recognition technology, and sends the text data to a server.

[0819] Output: Text data (converted voice commands).

[0820] Step 5:

[0821] Destination and optimal route calculation

[0822] Input: Text data of voice commands, behavioral pattern data, real-time traffic information.

[0823] How it works: The server identifies the destination based on voice commands and calculates the optimal route, taking into account behavioral patterns and real-time traffic information.

[0824] Output: Optimal route information.

[0825] Step 6:

[0826] Optimal route instructions

[0827] Input: Optimal route information calculated by the server.

[0828] Operation: The server sends instructions to the self-driving vehicle, which then guides the elderly person to their destination. At the same time, it sends information about the optimal route to the terminal and starts voice guidance.

[0829] Output: Navigation instructions provided to the senior citizen.

[0830] Step 7:

[0831] Emergency detection and notification

[0832] Input: The elderly person presses the emergency button on the device.

[0833] Operation: The device sends an emergency notification signal to the server, which then notifies the elderly person's location data and the emergency contacts.

[0834] Output: Emergency notification sent to emergency contacts.

[0835] Step 8:

[0836] Advice and instructions from conversational AI

[0837] Input: The next voice command the user will say.

[0838] How it works: The device again converts the voice command into text data using speech recognition technology and sends it to the server. The server uses a generative AI model to generate appropriate advice or instructions, and then uses speech synthesis technology to return the text data to the user.

[0839] Output: Spoken advice and instructions.

[0840] 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.

[0841] MODE FOR CARRYING OUT THE INVENTION

[0842] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active with peace of mind. It also incorporates an emotion engine that takes into account the user's emotions and enables flexible responses. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using conversational AI, and recognizes emotions through the emotion engine.

[0843] 1. Location Tracking

[0844] The system's device uses a GPS sensor to obtain the elderly person's current location and sends this information to a server. The server records the received location information in a database and performs analysis. This information is used to learn behavioral patterns and calculate optimal routes.

[0845] 2. Learning behavioral patterns

[0846] The server analyzes the user's movement patterns based on the received location data. It identifies frequently visited locations and routes and performs machine learning to provide personalized behavioral support for each user. Based on this learning result, it optimizes next route suggestions and emergency responses.

[0847] 3. Optimal route suggestions

[0848] When the user sets a destination, the device sends this information to the server. The server calculates the optimal route based on past behavioral pattern data and real-time traffic information, and sends the results to the device. The device then presents the optimal route to the user using voice guidance, etc.

[0849] 4. Emergency notification

[0850] If a user experiences an emergency (e.g., falls), they can press the emergency button on their device and the device will send an emergency notification along with their current location to the server. Based on the emergency notification, the server will notify pre-registered emergency contacts of their current location and situation via email or SMS.

[0851] 5. Advice and Instruction Generation by Conversational AI

[0852] Users can ask questions or give instructions to the device through voice commands. For example, when a user asks a question such as "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[0853] 6. Emotion Recognition by Emotion Engine

[0854] The system also uses an emotion engine to recognize the user's emotions. The emotion engine uses voice analysis and facial recognition technology to detect the user's emotions. For example, if the user's voice is unstable or their facial expression is tense, this information is analyzed. Based on the recognized emotions, the system generates appropriate responses or instructions.

[0855] Specific examples

[0856] Example 1: Navigation on the go

[0857] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[0858] 2. The device converts this voice command into text and sends it to the server.

[0859] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[0860] 4. The device will provide voice guidance for this route to the user and begin navigation.

[0861] 5. The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "I'll guide you a little more slowly so you can feel at ease."

[0862] Example 2: Emergency response

[0863] 1. The user falls while out and presses the emergency button.

[0864] 2. The device sends an emergency notification to the server.

[0865] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[0866] 4. The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Remain calm, help is on the way," and sends it to the device.

[0867] 5. The device will then audibly convey this message to the user.

[0868] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[0869] The processing flow will be explained below.

[0870] Location tracking

[0871] Step 1:

[0872] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[0873] Step 2:

[0874] The location information acquired by the device is temporarily stored in memory.

[0875] Step 3:

[0876] The device sends location information to the server at set intervals (e.g., every minute).

[0877] Step 4:

[0878] The server records the received location information in a database.

[0879] Step 5:

[0880] The server records the time the location information was received and waits for the next update.

[0881] Learning behavioral patterns

[0882] Step 1:

[0883] The server retrieves previously stored location information from the database.

[0884] Step 2:

[0885] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[0886] Step 3:

[0887] The server creates a dataset based on the analysis results to learn user behavior patterns.

[0888] Step 4:

[0889] The server uses a machine learning model to learn user behavior patterns and stores the results.

[0890] Optimal route suggestions

[0891] Step 1:

[0892] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[0893] Step 2:

[0894] The terminal transmits the set destination to the server.

[0895] Step 3:

[0896] The server acquires the user's past behavioral patterns and real-time traffic information.

[0897] Step 4:

[0898] The server calculates the optimal travel route and sends the calculation results to the terminal.

[0899] Step 5:

[0900] The terminal displays the optimal route to the user and starts voice guidance.

[0901] emergency notification

[0902] Step 1:

[0903] When an emergency occurs, the user presses the emergency button on the terminal.

[0904] Step 2:

[0905] The device detects when the emergency button is pressed and obtains the current location information.

[0906] Step 3:

[0907] The device sends the acquired location information and emergency notification to the server.

[0908] Step 4:

[0909] The server will send your location and emergency information to your emergency contacts via email or SMS.

[0910] Conversational AI for generating advice and instructions

[0911] Step 1:

[0912] The user speaks a question to the device, such as "Where are you now?"

[0913] Step 2:

[0914] The device converts the voice data it receives into text and sends it to the server.

[0915] Step 3:

[0916] The server parses the received text data and generates an appropriate response.

[0917] Step 4:

[0918] The server generates a response and sends it to the terminal in text format.

[0919] Step 5:

[0920] The terminal converts the text data into speech and provides the user with the response content.

[0921] Emotion recognition by emotion engine

[0922] Step 1:

[0923] The user gives instructions and asks questions to the terminal by voice.

[0924] Step 2:

[0925] The terminal records the user's voice and transmits the voice data to the server.

[0926] Step 3:

[0927] The server analyzes the voice data and recognizes the user's emotions using an emotion engine.

[0928] Step 4:

[0929] The server generates appropriate advice and instructions based on the emotional state.

[0930] Step 5:

[0931] The server sends the generated advice and instructions to the terminal, which then conveys them to the user by voice.

[0932] Specific examples

[0933] Example 1: Navigation on the go

[0934] Step 1:

[0935] The user speaks to the terminal, saying, "I want to go to the supermarket."

[0936] Step 2:

[0937] The device converts this voice command into text and sends it to the server.

[0938] Step 3:

[0939] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[0940] Step 4:

[0941] The server sends the calculation results to the terminal.

[0942] Step 5:

[0943] The terminal will then provide voice guidance for this route to the user and begin navigation.

[0944] Step 6:

[0945] The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "We'll guide you a little more slowly so that you can feel at ease."

[0946] Example 2: Emergency response

[0947] Step 1:

[0948] The user falls while out and presses the emergency button.

[0949] Step 2:

[0950] The device sends an emergency notification to the server.

[0951] Step 3:

[0952] The server will notify your emergency contact of your current location and status via email or SMS.

[0953] Step 4:

[0954] The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Stay calm, help is on the way," and sends it to the device.

[0955] Step 5:

[0956] The terminal will then audibly convey this message to the user.

[0957] Example 2

[0958] 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."

[0959] There is an urgent need to improve the efficiency of responding to lost or emergency situations when elderly people are out and about, and to provide an environment where they can be active and feel safe. There is also a need for flexible support that takes into account the user's emotions. Conventional technologies have limited functionality for acquiring location information and emergency notifications, and lack the ability to recognize the user's emotions in real time and respond accordingly. Therefore, it is necessary to provide a system that allows elderly people to go out safely and with peace of mind.

[0960] 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.

[0961] In this invention, the server includes a means for constantly acquiring location information, a means for transmitting acquired location data to a central processing unit, a means for recording and analyzing the location data in the central processing unit, a means for calculating an optimal route and generating travel instructions based on the analysis results, a means for automatically notifying emergency contacts in the event of an emergency, and a means for analyzing the user's voice and facial expressions to recognize emotions and generate an appropriate response. This allows elderly people to constantly track their location, reducing the risk of getting lost and allowing them to receive prompt notification and assistance in the event of an emergency. Furthermore, the emotion recognition function provides more personalized support, increasing the user's sense of security and trust.

[0962] "Means for acquiring location information at any time" is a general term for sensors and devices that can acquire multiple pieces of location information in real time, as well as methods for acquiring that data.

[0963] "Means for transmitting acquired location data to a central processing unit" is a general term for means for collecting location information and transmitting it to a remote server or central processing unit via communication means.

[0964] "Central Processing Unit" is a general term for a computer system or server that records and analyzes received data.

[0965] "Means for recording and analyzing location data" is a general term for software and algorithms that store location information and analyze user behavior patterns and trends based on that data.

[0966] "Means for calculating the optimal route and generating travel instructions" is a general term for functions that take into account the user's current location, destination, and suspicious points, calculate an efficient travel route, and provide instructions to the user.

[0967] "Means for automatically notifying emergency contacts in the event of an emergency" is a general term for a system or function that automatically notifies pre-registered emergency contacts of current location information, etc. when an emergency occurs, such as when a user presses an emergency button.

[0968] "Means of analyzing a user's voice and facial expressions to recognize emotions and generate appropriate responses" is a general term for technologies and algorithms that read emotions from a user's voice and facial expressions and provide appropriate responses and guidance based on that information.

[0969] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, notifies users in emergencies, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine, providing flexible responses and a sense of security to users.

[0970] Location tracking

[0971] The system's terminals are equipped with GPS sensors that constantly acquire the elderly person's current location. The terminals transmit this location information in real time to a server, which then records the received location information in a database. The server then analyzes the location information to determine the user's current location and movement patterns.

[0972] Learning behavioral patterns

[0973] The server analyzes the user's behavioral patterns based on the accumulated location data, including frequently visited locations and specific travel routes. Using the results of this analysis, the server uses machine learning algorithms to learn the user's behavioral patterns and optimize next route suggestions and emergency response.

[0974] Optimal route suggestions

[0975] When the user sets their destination, the device sends this information to the server. The server uses past behavioral pattern data and real-time traffic information to calculate the optimal route and sends the results to the device. The device then presents the optimal route to the user through voice guidance and a display.

[0976] emergency notification

[0977] If a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server. Based on the emergency notification received, the server notifies pre-registered emergency contacts of the user's current location and situation via email or SMS. In addition, the server uses an emotion engine to analyze the user's emotional state, generates an appropriate reassuring message, and sends it to the device. The device then conveys this message to the user via voice.

[0978] Conversational AI for generating advice and instructions

[0979] Users can ask questions or give instructions to the device through voice commands. For example, if a user asks "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[0980] Emotion recognition by emotion engine

[0981] The system recognizes the user's emotions using an emotion engine, which uses voice analysis and facial recognition technology to detect the user's emotional state. For example, if the user's voice is unstable or their facial expression is tense, the system will analyze this and generate appropriate responses or instructions.

[0982] Specific examples

[0983] Prompt Sentence Examples

[0984] Explain how a system works when it suggests the optimal route when a user says they want to go to the supermarket.

[0985] Describe how a system works that sends an emergency notification when a user falls.

[0986] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[0987] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0988] Step 1:

[0989] The user goes out with the terminal.

[0990] Specific operation: The user carries the device when out and about. The device has a built-in GPS sensor, so location information can be obtained at all times.

[0991] Step 2:

[0992] The device obtains location information using the GPS sensor.

[0993] Input: User's current location (GPS data).

[0994] Specific operation: The device uses the GPS sensor at regular intervals (e.g., every 10 seconds) to determine its current location.

[0995] Output: Current location (latitude and longitude).

[0996] Step 3:

[0997] The location information acquired by the device is sent to the server.

[0998] Input: Current location (latitude and longitude).

[0999] What it does: The device sends real-time location information to the server over a mobile network or Wi-Fi.

[1000] Output: The location sent to the server.

[1001] Step 4:

[1002] The server records the received location information in a database.

[1003] Input: The location information sent.

[1004] Specific operation: The server stores the location information it receives in a database in chronological order.

[1005] Output: Location information recorded in a database.

[1006] Step 5:

[1007] The server analyzes the location information.

[1008] Input: Location information stored in a database.

[1009] How it works: The server analyzes the user's movement patterns and frequently visited locations based on the accumulated location information. Clustering and time series analysis algorithms are used.

[1010] Output: Analysis results (user movement patterns and frequently visited places).

[1011] Step 6:

[1012] The user sets the destination.

[1013] Specific action: The user enters a destination using voice commands or a touchscreen.

[1014] Input: Destination information.

[1015] Step 7:

[1016] The terminal transmits the destination information to the server.

[1017] Input: Destination information.

[1018] Specific operation: The device sends the entered destination information to the server.

[1019] Output: The destination information sent to the server.

[1020] Step 8:

[1021] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information.

[1022] Input: Historical behavioral pattern data, real-time traffic information, and destination information.

[1023] Specific operation: The server calculates the optimal route using past recorded behavior data and real-time traffic information (e.g., Google Maps API).

[1024] Output: Optimal route directions.

[1025] Step 9:

[1026] The server sends the calculation results to the terminal.

[1027] Input: Best Route Directions information.

[1028] Specific operation: The server sends the generated optimal route information to the terminal.

[1029] Output: The best route directions sent to the device.

[1030] Step 10:

[1031] The terminal presents the optimal route to the user using voice guidance, etc.

[1032] Input: Best Route Directions information.

[1033] Specific operation: The device guides the user to the optimal route through voice navigation and on-screen display.

[1034] Output: The optimal route presented to the user.

[1035] Step 11:

[1036] If the user encounters an emergency, he presses the emergency button.

[1037] Specific action: The user presses the emergency button on the device.

[1038] Input: Emergency button press information.

[1039] Step 12:

[1040] The device sends an emergency notification to the server along with its current location.

[1041] Input: Emergency button press information, current location information.

[1042] Specific operation: The device sends emergency button press information and current location information to the server.

[1043] Output: The emergency notification information sent to the server.

[1044] Step 13:

[1045] The server notifies the emergency contact of the current location and situation.

[1046] Input: Emergency notification information, emergency contact information.

[1047] Specific operation: The server notifies registered emergency contacts of the current location and situation via email or SMS.

[1048] Output: The notification message sent to the emergency contact.

[1049] Step 14:

[1050] The emotion engine analyzes the user's voice and facial expressions.

[1051] Input: User's voice data, facial expression data (camera footage).

[1052] How it works: The emotion engine uses voice analysis and facial recognition algorithms to analyze the user's emotions.

[1053] Output: The user's emotional state (anxious, relieved, etc.).

[1054] Step 15:

[1055] The server generates a reassuring message and sends it to the device.

[1056] Input: The user's emotional state.

[1057] Specific behavior: The server generates a message to reassure the user based on the analysis results.

[1058] Output: The reassurance message sent to the device.

[1059] Step 16:

[1060] The terminal will then audibly convey the message to the user.

[1061] Input: Reassurance message.

[1062] Specific operation: The device uses speech synthesis technology to audibly convey a reassuring message to the user.

[1063] Output: The message spoken to the user.

[1064] (Application example 2)

[1065] 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."

[1066] The challenge is to reduce the risk of elderly people getting lost when they go out and provide them with a means of moving safely and with peace of mind. Another important challenge is to respond quickly and appropriately in emergencies and reduce the anxiety of elderly people. Furthermore, there is a need to realize more effective mobility support by learning the individual behavioral patterns of elderly people and providing personalized assistance.

[1067] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for constantly acquiring location information; means for transmitting the acquired location data to a central processing unit; means for recording and analyzing the location data in the central processing unit; means for calculating an optimal route and generating travel instructions based on the analysis results; means for automatically notifying emergency contacts in the event of an emergency; means for learning behavioral patterns based on the acquired location data and identifying frequently visited locations and routes; means for calculating an optimal route based on past behavioral patterns and real-time traffic information and reflecting the results in the autonomous vehicle; means for accepting voice commands, transmitting voice data to the central processing unit, and generating appropriate advice and instructions; and means for detecting user emotions using voice analysis and facial recognition technology and generating appropriate responses. This reduces the risk of elderly people getting lost when out and allows them to travel safely and with peace of mind. It also enables prompt and appropriate responses in emergencies to alleviate elderly anxiety. Furthermore, personalized assistance based on behavioral pattern learning can be provided, thereby achieving more effective mobility support.

[1068] A "means for continuously obtaining location information" is a device or method that continuously obtains the current location of an elderly person using a GPS sensor or other location tracking technology.

[1069] The "means for transmitting the acquired location data to the central processing unit" refers to a communication means for transmitting the elderly person's current location information to a server or cloud computing system via a network.

[1070] "Means for recording and analyzing location data at a central processing unit" refers to a computer system and algorithms for recording transmitted location information in a database and analyzing the data.

[1071] The "means for calculating the optimal route based on the analysis results and generating travel instructions" refers to an algorithm or system that calculates the most suitable travel route for an elderly person and generates travel instructions based on the analysis results of location data and traffic information.

[1072] "Means for automatically notifying emergency contacts in the event of an emergency" refers to an alarm system and communication means for automatically notifying pre-registered contact persons in the event of an emergency.

[1073] "Means for learning behavioral patterns based on acquired location data and identifying frequently visited places and routes" refers to a machine learning algorithm that analyzes collected location data to learn the behavioral patterns of elderly people and identify frequently visited places and routes they use.

[1074] "Means of calculating the optimal route based on past behavioral patterns and real-time traffic information, and reflecting this in autonomous vehicles" refers to technology that determines the optimal travel route based on accumulated behavioral data and current traffic conditions, and integrates this information into the route planning system of autonomous vehicles.

[1075] The "means for accepting voice commands, transmitting voice data to a central processing unit, and generating appropriate advice or instructions" refers to a speech recognition and natural language processing system that recognizes voice input from the senior, converts the speech into text, transmits it to a central processing unit, and generates and returns an appropriate response.

[1076] "Means for detecting a user's emotions using voice analysis and facial recognition technology and generating an appropriate response" refers to an emotion analysis engine and facial recognition system that identifies a user's emotional state by analyzing changes in voice tone and facial expression, and generates an appropriate response according to that state.

[1077] The following describes in detail the mode for carrying out this invention. This invention is a system that reduces the risk of elderly people getting lost when they go out, allowing them to move around safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine.

[1078] The implementation of this system uses the following hardware and software:

[1079] Hardware and software used

[1080] 1. GPS sensor: Used to obtain the location of the elderly person at any time.

[1081] 2. Central processing unit (server): Contains a database and analysis algorithms for recording and analyzing acquired location data.

[1082] 3. Communication unit: A network communication device for transmitting acquired location data to a server.

[1083] 4. Self-driving vehicles: Vehicles that safely transport seniors to their destinations.

[1084] 5. Conversational AI systems: Software for speech recognition and generating appropriate advice and instructions.

[1085] 6. Emotion Analysis Engine and Facial Recognition System: Software for detecting user emotions using voice analysis and facial recognition technology.

[1086] Processing flow

[1087] 1. Location information acquisition: The server acquires the current location of the elderly person from the GPS sensor. This data is sequentially sent to the server and recorded in the database.

[1088] 2. Learning behavioral patterns: The server analyzes the acquired location data and learns the behavioral patterns of the elderly, for example, identifying frequently visited places and routes.

[1089] 3. Calculating the optimal route: When the elderly person inputs their destination, the server calculates the optimal route based on their past behavior patterns and real-time traffic information, and sends that information to the autonomous vehicle, which then uses this information to direct the elderly person to their destination.

[1090] 4. Emergency notification: If an elderly person experiences an emergency, such as a fall, they can press the emergency button, which sends an emergency notification along with their current location to the server. The server then notifies the emergency contact and generates a response to reassure the elderly person and sends it to their device.

[1091] 5. Conversational AI for advice and instruction generation: Users can ask questions or give instructions to the server through voice commands. The server analyzes this voice data and generates appropriate responses.

[1092] 6. Emotion Recognition: Using an emotion analysis engine and facial recognition system, the system detects the user's emotions and generates appropriate responses. For example, if the user appears anxious, the system generates a reassuring response.

[1093] Specific examples

[1094] Example 1: If an elderly person gets into an autonomous vehicle and says, "I want to go to the supermarket," the system will calculate the optimal route based on their past behavioral patterns and current traffic information, and reflect this in the autonomous vehicle. If the user seems anxious, the system will respond by saying, "Don't worry, we'll proceed slowly."

[1095] Example 2: If an elderly person falls while out, pressing the emergency button will send their current location information to the server, which will automatically notify emergency contacts. The server will then generate a message such as "Help is on the way, so don't worry," and the message will be played by voice on the device.

[1096] Prompt Sentence Examples

[1097] The following prompt sentence can be used to have a generative AI model create a detailed response about support systems for the elderly:

[1098] text

[1099] Please explain the support system for elderly people using self-driving vehicles. This system supports elderly people in going out safely and securely, and has functions such as location tracking, learning behavioral patterns, suggesting optimal routes, emergency notifications, emotion recognition using an emotion engine, and advice through conversational AI. Specifically, when an elderly person says, "I want to go to the supermarket," the system calculates the optimal route and provides a sense of security.

[1100] In this way, the mode for carrying out the invention can be concretely shown.

[1101] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1102] Step 1:

[1103] The user uses the terminal to input a voice command such as "I want to go to the supermarket." This voice data is converted into text data by the terminal and sent to the server.

[1104] Step 2:

[1105] The server receives the input text data and analyzes it based on past behavioral patterns and real-time traffic information. The results of this analysis are used to calculate the optimal route. In calculating this optimal route, data such as behavioral patterns and frequently visited locations is used to process and calculate the data.

[1106] Step 3:

[1107] The server sends the calculated optimal route to the autonomous vehicle's system, which uses this information to update its route plan and safely begin driving. The output is the updated route information.

[1108] Step 4:

[1109] The terminal provides navigation information to the user through voice guidance. The user's location information is acquired continuously and transmitted to the server in real time. The output is the user's current location information.

[1110] Step 5:

[1111] The server analyzes the location information and continues to learn behavioral patterns. Specifically, it analyzes the data to identify frequently visited locations and routes. The results of this analysis are used to suggest the next route.

[1112] Step 6:

[1113] If a user encounters an emergency while out and about, they press the emergency button on their device. The device then sends their current location and emergency notification to the server. The server then receives this information and notifies pre-registered emergency contacts. The output is the information of the notified emergency contacts and their current situation.

[1114] Step 7:

[1115] The server uses voice analysis and facial recognition technology to detect the user's emotions. Specifically, it analyzes voice tone and facial expressions to identify emotional states such as anxiety or tension. The results of this analysis are sent to the conversational AI system. The output is analyzed emotional data.

[1116] Step 8:

[1117] The conversational AI system generates an appropriate response based on the user's emotional state. For example, if the user seems anxious, it generates a response such as "Don't worry." This response is sent to the terminal and notified to the user by voice. The output is the generated response message.

[1118] This enables the entire system to function as a support system for elderly people to go out safely and securely.

[1119] 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.

[1120] 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.

[1121] 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.

[1122] [Third embodiment]

[1123] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1124] 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.

[1125] 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).

[1126] 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.

[1127] 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.

[1128] 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).

[1129] 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.

[1130] 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.

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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."

[1135] MODE FOR CARRYING OUT THE INVENTION

[1136] This invention is a system that enables elderly people to go out safely and without getting lost. The system has functions for tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[1137] 1. Location Tracking

[1138] The system's terminal uses a GPS sensor to constantly obtain the elderly person's current location and transmits this information to a server. The server records the received location information in a database and performs analysis. This information is later used to learn behavioral patterns and calculate optimal routes.

[1139] 2. Learning behavioral patterns

[1140] The server analyzes the user's movement patterns based on the received location data and identifies frequently visited locations and routes, allowing the server to learn each user's individual behavioral patterns and provide personalized services.

[1141] 3. Optimal route suggestions

[1142] When the user sets a destination, the device notifies the server. The server calculates the optimal route based on past behavior patterns and real-time traffic information, and sends the results to the device. The device then displays this route to the user and provides voice guidance.

[1143] 4. Emergency notification

[1144] When a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server, which then immediately sends their current location and emergency information to their emergency contacts to promptly respond.

[1145] 5. Advice and Instruction Generation by Conversational AI

[1146] Users can ask questions or give instructions to the device using voice commands. For example, when a user asks a question such as "Where are you now?", the device converts the voice into text data and sends it to the server. The server analyzes the voice data, generates an appropriate response (e.g., "You are currently on Main Street"), and sends it to the device. The device then notifies the user of this response by voice.

[1147] Specific examples

[1148] Example 1: Navigation on the go

[1149] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[1150] 2. The device converts this voice command into text and sends it to the server.

[1151] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[1152] 4. The device will provide voice guidance for this route to the user and begin navigation.

[1153] 5. The user follows the directions to safely reach the supermarket.

[1154] Example 2: Emergency response

[1155] 1. The user falls while out and presses the emergency button.

[1156] 2. The device sends an emergency notification to the server.

[1157] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[1158] 4. Emergency contacts will be notified and will take necessary action.

[1159] This system not only allows seniors to feel safe when going out, but also allows for a quick response in the event of an emergency, and uses conversational AI to provide an even higher level of support and engagement.

[1160] The processing flow will be explained below.

[1161] Location tracking

[1162] Step 1:

[1163] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[1164] Step 2:

[1165] The location information acquired by the device is temporarily stored in memory.

[1166] Step 3:

[1167] The device sends location information to the server at set intervals (e.g., every minute).

[1168] Step 4:

[1169] The server records the received location information in a database.

[1170] Step 5:

[1171] The server records the time the location information was received and waits for the next update.

[1172] Learning behavioral patterns

[1173] Step 1:

[1174] The server retrieves previously stored location information from the database.

[1175] Step 2:

[1176] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[1177] Step 3:

[1178] The server creates a dataset based on the analysis results to learn user behavior patterns.

[1179] Step 4:

[1180] The server uses a machine learning model to learn user behavior patterns and stores the results.

[1181] Optimal route suggestions

[1182] Step 1:

[1183] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[1184] Step 2:

[1185] The terminal transmits the set destination to the server.

[1186] Step 3:

[1187] The server acquires the user's past behavioral patterns and real-time traffic information.

[1188] Step 4:

[1189] The server calculates the optimal travel route and sends the calculation results to the terminal.

[1190] Step 5:

[1191] The terminal displays the optimal route to the user and starts voice guidance.

[1192] emergency notification

[1193] Step 1:

[1194] When an emergency occurs, the user presses the emergency button on the terminal.

[1195] Step 2:

[1196] The device detects when the emergency button is pressed and obtains the current location information.

[1197] Step 3:

[1198] The device sends the acquired location information and emergency notification to the server.

[1199] Step 4:

[1200] The server will send your location and emergency information to your emergency contacts via email or SMS.

[1201] Conversational AI for generating advice and instructions

[1202] Step 1:

[1203] The user speaks a question to the device, such as "Where are you now?"

[1204] Step 2:

[1205] The device converts the voice data it receives into text and sends it to the server.

[1206] Step 3:

[1207] The server parses the received text data and generates an appropriate response.

[1208] Step 4:

[1209] The server generates a response and sends it to the terminal in text format.

[1210] Step 5:

[1211] The terminal converts the text data into speech and provides the user with the response content.

[1212] Specific examples

[1213] Example 1: Navigation on the go

[1214] Step 1:

[1215] The user speaks to the terminal, saying, "I want to go to the supermarket."

[1216] Step 2:

[1217] The device converts this voice command into text and sends it to the server.

[1218] Step 3:

[1219] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[1220] Step 4:

[1221] The server sends the calculation results to the terminal.

[1222] Step 5:

[1223] The terminal will then provide voice guidance for this route to the user and begin navigation.

[1224] Example 2: Emergency response

[1225] Step 1:

[1226] The user falls while out and presses the emergency button.

[1227] Step 2:

[1228] The device sends an emergency notification to the server.

[1229] Step 3:

[1230] The server will notify your emergency contact of your current location and status via email or SMS.

[1231] Step 4:

[1232] Emergency contacts will be notified and will take necessary action.

[1233] Example 1

[1234] 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."

[1235] In modern society, it is important for elderly people to be able to go out safely and with peace of mind. However, concerns about getting lost or traveling, as well as rapid response in emergencies, remain issues. Elderly people, in particular, tend to have limited mobility, so rapid response is required in emergencies. However, existing systems rarely provide integrated location tracking, behavioral pattern learning, optimal route suggestions, and emergency notifications, and they also lack support from conversational AI. Therefore, there is a need for a more comprehensive and personalized outing support system for elderly people.

[1236] 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.

[1237] In this invention, the server includes means for constantly acquiring locations, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for calculating an optimal route and generating travel instructions based on the analysis results, means for automatically notifying emergency contacts in the event of an emergency, means for converting voice commands into text data, means for transmitting voice data to the central processing unit and generating an appropriate response, and means for generating a voice response and notifying the user. This allows elderly people to go out with peace of mind, enables quick response in the event of an emergency, and further enables personalized support based on behavioral patterns and advanced support using conversational AI to be provided.

[1238] "Means for obtaining location information on a regular basis" refers to a device or method that periodically obtains the user's current location using a GPS sensor or other location measurement device.

[1239] "Means for transmitting to the central processing unit" refers to a device or method for transmitting location data and other necessary information to the central processing unit via wireless communication, the Internet, etc.

[1240] "Central processing unit" refers to a computer or server that records received data and performs analysis, etc.

[1241] "Means for recording and analyzing location data" refers to a device or method that stores received location data in a database or the like and analyzes the user's behavioral patterns and current location based on this data.

[1242] "Means for calculating the optimal route and generating travel instructions" refers to a device or method that calculates the optimal route to a destination for a user based on past behavioral patterns and real-time traffic information, and generates the contents of that route as instructions.

[1243] "Means for automatically notifying emergency contacts in the event of an emergency" refers to a device or method that automatically notifies emergency contacts when a user is in an emergency.

[1244] The "means for converting voice commands into text data" refers to a device or method for converting voice commands input by a user into text format.

[1245] The "means for transmitting voice data to the central processing unit" refers to a device or method for transmitting the converted text data to the central processing unit.

[1246] The "means for generating an appropriate response" is an algorithm or software that generates an appropriate response in response to a voice command.

[1247] The "means for generating a voice response and notifying the user" refers to a device or method for notifying the user of the generated appropriate response as voice.

[1248] This invention is a system that helps elderly people move around safely and securely when they go out. The system has the functions of tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[1249] Hardware and software used

[1250] Hardware

[1251] Terminal: A device with a built-in GPS sensor that captures the user's location. This includes smartphones and dedicated mobile terminals.

[1252] Server: A computing device that receives, records, analyzes data, and generates appropriate responses and routes.

[1253] software

[1254] GPS data collection software: Installed on the device, it acquires location information at any time.

[1255] Communication software: Sends and receives data between the device and the server.

[1256] Database software: Records and stores location information and user behavior patterns on the server.

[1257] Analysis algorithm: Installed on the server, it learns behavioral patterns from location information and calculates the optimal route.

[1258] Conversational AI model: AI software that interprets voice commands and generates appropriate instructions or advice.

[1259] System operation example

[1260] Example 1: Navigation on the go

[1261] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[1262] 2. The device converts this voice command into text and sends it to the server.

[1263] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[1264] 4. The device will provide voice guidance for this route to the user and begin navigation.

[1265] 5. The user follows the directions to safely reach the supermarket.

[1266] Example 2: Emergency response

[1267] 1. The user falls while out and presses the emergency button.

[1268] 2. The device sends an emergency notification to the server.

[1269] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[1270] 4. Emergency contacts receive a notification and rush to the user's rescue.

[1271] Implementation details

[1272] The device periodically acquires the user's current location using the built-in GPS sensor. This location information is sent to the server via wireless communication. The server stores the received location information in a database and uses it to analyze the user's behavioral patterns.

[1273] When a user sets a destination, for example by entering a voice command such as "I want to go to the supermarket," the device converts the voice into text data and sends it to the server. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device. The device then provides this route to the user via voice guidance and on-screen display, assisting them in their journey.

[1274] If a user experiences an emergency, they can press the emergency button on their device, which will send an emergency notification to the server. The server will then receive the information and notify pre-registered emergency contacts via email or SMS, enabling prompt assistance.

[1275] Furthermore, when a user inputs a voice command such as "Where are you now?", the device converts the voice data into text and sends it to the server. The server analyzes the voice data, generates an appropriate response, and sends it back to the device, which then notifies the user as voice.

[1276] This system allows elderly people to go out with peace of mind, and provides prompt support and assistance when needed. In addition, the use of conversational AI makes interactions with users natural and intuitive.

[1277] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1278] Step 1:

[1279] The device uses the built-in GPS sensor to obtain the user's current location. The input is location information (latitude and longitude) from the GPS sensor, and the output is location data stored in the device. This acquisition is performed periodically, updating the location information every minute, for example.

[1280] Step 2:

[1281] The location data acquired by the device is sent to the server. The input is the location data stored in the device, and the output is the location data sent to the server. Communication is via wireless communication or the Internet, and data is sent in real time.

[1282] Step 3:

[1283] The server records the received location data in a database. The input is the location data sent from the device, and the output is the location data stored in the database. The recorded data is saved with the date and time and used for subsequent analysis.

[1284] Step 4:

[1285] The server periodically analyzes the location data recorded in the database. The input is the location data stored in the database, and the output is behavioral pattern data as a result of the analysis. A machine learning algorithm is used for the analysis, which identifies frequently visited places and routes.

[1286] Step 5:

[1287] The user speaks their destination into the device. The input is the user's voice command (e.g., "I want to go to the supermarket"), and the output is the speech data converted to text by the device. Speech recognition software performs this conversion.

[1288] Step 6:

[1289] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the text data sent to the server. Communication is performed via wireless communication or the Internet.

[1290] Step 7:

[1291] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information. The input is text data (destination), past behavioral patterns, and real-time traffic information, and the output is optimal route information. The optimal route is calculated using an algorithm.

[1292] Step 8:

[1293] The server sends the calculated optimal route to the terminal. The input is the optimal route information, and the output is the optimal route information sent to the terminal. Communication is again carried out via wireless communication or the Internet.

[1294] Step 9:

[1295] The terminal displays the optimal route to the user and provides voice guidance. The input is the optimal route information sent from the server, and the output is voice guidance and a display on the screen for the user. This task is performed by the navigation software.

[1296] Step 10:

[1297] When a user presses the emergency button, the device sends an emergency notification to the server. The input is the user's action (pressing the emergency button) and the output is the emergency notification data. The device sends data including its current location and status.

[1298] Step 11:

[1299] The server notifies emergency contacts of the current location and emergency. The input is the emergency notification data sent from the device, and the output is an email or SMS notification to the emergency contacts. The notification includes the current location and details of the emergency.

[1300] Step 12:

[1301] The user types a voice command (e.g., "Where am I?"). The input is the user's voice command, and the output is the data converted to text by the device. Speech recognition software performs the conversion.

[1302] Step 13:

[1303] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the data sent to the server.

[1304] Step 14:

[1305] The server analyzes the voice data and generates an appropriate response. The input is text data, and the output is the generated response data. The generative AI model performs this analysis and response generation.

[1306] Step 15:

[1307] The server sends response data to the terminal. The input is the generated response data, and the output is the data sent to the terminal.

[1308] Step 16:

[1309] The terminal notifies the user of the response by voice. The input is the response data sent from the server, and the output is the voice response to the user. The voice synthesis software generates the voice response.

[1310] (Application example 1)

[1311] 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."

[1312] Problems such as elderly people getting lost when out and about and being unable to respond to emergencies are serious issues in an aging society. In addition, as autonomous vehicles become more widespread, it is also necessary to ensure that elderly people can use them with peace of mind and that their safety is ensured while traveling. Conventional systems have not been able to provide sufficient safety and convenience for elderly people traveling alone.

[1313] 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.

[1314] In this invention, the server includes means for constantly acquiring location information, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for sharing location information in the autonomous vehicle, means for learning the usage history of the autonomous vehicle and the behavioral patterns of the elderly person, means for calculating an optimal route based on real-time traffic information and instructing the autonomous vehicle, means for sending an emergency notification when an emergency such as a fall occurs inside the autonomous vehicle, and means for providing voice support using interactive AI. This allows elderly people to use autonomous vehicles with peace of mind, prevents them from getting lost when they are out, and enables emergency response.

[1315] "Means for obtaining location information at any time" refers to hardware and software for obtaining location information of elderly people in real time.

[1316] "Means for transmitting acquired location data to a central processing unit" refers to the communication technology and protocols for transmitting location information acquired from a terminal to a server or cloud.

[1317] "Means for recording and analyzing location data in a central processing unit" refers to a device or system that stores received location information in a database and uses analytical algorithms to calculate movement patterns and destinations.

[1318] "Means for sharing location information in self-driving vehicles" refers to technology that enables real-time exchange of location information between devices used by elderly people and self-driving vehicles.

[1319] "Means for learning the usage history of autonomous vehicles and the behavioral patterns of elderly people" refers to an AI model that learns past usage data of autonomous vehicles and the movement history of elderly people and performs pattern recognition.

[1320] "Means for calculating the optimal route based on real-time traffic information and instructing it to an autonomous vehicle" refers to a system that calculates the optimal travel route that reflects current traffic conditions and provides that route to an autonomous vehicle.

[1321] "Means for sending emergency notifications in the event of an emergency such as a fall inside an autonomous vehicle" refers to a device or system that automatically notifies pre-set emergency contacts when an elderly person experiences an emergency inside an autonomous vehicle.

[1322] "Means for providing voice support using conversational AI" refers to an AI system that uses voice recognition technology and natural language processing to generate responses and respond verbally when an elderly person uses voice commands to ask questions or give instructions.

[1323] The present invention is a system that allows elderly people to travel safely and without getting lost when they go out using an autonomous vehicle. The system includes the following main components:

[1324] Location tracking

[1325] The device uses a GPS sensor to constantly obtain the elderly person's current location. This location information is sent to a central processing unit (server) via Wi-Fi or a mobile communication network. The obtained location data is recorded in a database on the server and used for subsequent analysis.

[1326] Learning behavioral patterns

[1327] The server analyzes the acquired location data and learns the elderly person's behavioral patterns. This analysis uses pattern recognition algorithms and machine learning techniques. This identifies frequently visited places and typical travel routes. The learned pattern data is used to provide personalized services based on individual routines.

[1328] Optimal route suggestions

[1329] When the user sets a destination using voice commands, the device sends this information to the server, which calculates the optimal route based on past behavioral pattern data and real-time traffic information, and issues instructions to the autonomous vehicle, allowing the elderly person to reach their destination via the optimal route.

[1330] emergency notification

[1331] The device is equipped with an emergency button, and if an elderly person experiences an emergency, pressing this button immediately sends an emergency notification to the server. The server then sends information about the current location and the emergency to pre-set emergency contacts, enabling a prompt response.

[1332] Conversational AI for generating advice and instructions

[1333] The device is equipped with a voice input function, and when the elderly person issues a voice command, the voice data is converted into text and sent to the server. The server analyzes the voice data and generates appropriate advice or instructions. The generated response is then notified to the elderly person via voice via the device.

[1334] Hardware and software used

[1335] GPS sensor: Obtains the elderly person's current location.

[1336] Wi-Fi / Cellular Network: Transmitting location data.

[1337] Database: Recording and storing location data.

[1338] Pattern recognition algorithms, machine learning techniques: learning behavioral patterns.

[1339] Real-time traffic information system: Calculating optimal routes.

[1340] Emergency button: Triggers emergency notifications.

[1341] Speech recognition technology, natural language processing (NLP) technology: analysis of voice input and conversational AI.

[1342] Specific examples

[1343] For example, if an elderly person says "I want to go to the hospital," the device converts this voice command into text and sends it to the server. The server calculates the optimal route based on behavioral patterns and real-time traffic information, and issues instructions to the autonomous vehicle. If an elderly person falls inside the vehicle, they can press the emergency button, which will send an emergency notification to their family and medical institutions.

[1344] Prompt Sentence Examples

[1345] "Tell me where you are."

[1346] "Tell me the best route"

[1347] "I fell, please send an emergency notification."

[1348] In this way, the invention provides an environment in which elderly people can use self-driving vehicles with peace of mind.

[1349] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1350] Step 1:

[1351] Acquiring GPS location information

[1352] Input: The device's GPS sensor detects the elderly person's current location.

[1353] Operation: The device periodically obtains location data (latitude and longitude) from the GPS module.

[1354] Output: The acquired position data is stored in memory.

[1355] Step 2:

[1356] Sending location data

[1357] Input: Location data stored on the device.

[1358] How it works: The device sends the acquired location data to a server via Wi-Fi or cellular networks.

[1359] Output: The location data received by the server.

[1360] Step 3:

[1361] Recording and analyzing location data

[1362] Input: The location data received by the server.

[1363] How it works: The server records location data in a database and analyzes the data using pattern recognition algorithms to identify frequently visited locations and travel routes.

[1364] Output: Recorded location data and analysis results (behavioral patterns).

[1365] Step 4:

[1366] Processing voice commands

[1367] Input: A voice command given by the user.

[1368] How it works: The device uses a microphone to capture voice commands, converts them into text data using speech recognition technology, and sends the text data to a server.

[1369] Output: Text data (converted voice commands).

[1370] Step 5:

[1371] Destination and optimal route calculation

[1372] Input: Text data of voice commands, behavioral pattern data, real-time traffic information.

[1373] How it works: The server identifies the destination based on voice commands and calculates the optimal route, taking into account behavioral patterns and real-time traffic information.

[1374] Output: Optimal route information.

[1375] Step 6:

[1376] Optimal route instructions

[1377] Input: Optimal route information calculated by the server.

[1378] Operation: The server sends instructions to the self-driving vehicle, which then guides the elderly person to their destination. At the same time, it sends information about the optimal route to the terminal and starts voice guidance.

[1379] Output: Navigation instructions provided to the senior citizen.

[1380] Step 7:

[1381] Emergency detection and notification

[1382] Input: The elderly person presses the emergency button on the device.

[1383] Operation: The device sends an emergency notification signal to the server, which then notifies the elderly person's location data and the emergency contacts.

[1384] Output: Emergency notification sent to emergency contacts.

[1385] Step 8:

[1386] Advice and instructions from conversational AI

[1387] Input: The next voice command the user will say.

[1388] How it works: The device again converts the voice command into text data using speech recognition technology and sends it to the server. The server uses a generative AI model to generate appropriate advice or instructions, and then uses speech synthesis technology to return the text data to the user.

[1389] Output: Spoken advice and instructions.

[1390] 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.

[1391] MODE FOR CARRYING OUT THE INVENTION

[1392] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active with peace of mind. It also incorporates an emotion engine that takes into account the user's emotions and enables flexible responses. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using conversational AI, and recognizes emotions through the emotion engine.

[1393] 1. Location Tracking

[1394] The system's device uses a GPS sensor to obtain the elderly person's current location and sends this information to a server. The server records the received location information in a database and performs analysis. This information is used to learn behavioral patterns and calculate optimal routes.

[1395] 2. Learning behavioral patterns

[1396] The server analyzes the user's movement patterns based on the received location data. It identifies frequently visited locations and routes and performs machine learning to provide personalized behavioral support for each user. Based on this learning result, it optimizes next route suggestions and emergency responses.

[1397] 3. Optimal route suggestions

[1398] When the user sets a destination, the device sends this information to the server. The server calculates the optimal route based on past behavioral pattern data and real-time traffic information, and sends the results to the device. The device then presents the optimal route to the user using voice guidance, etc.

[1399] 4. Emergency notification

[1400] If a user experiences an emergency (e.g., falls), they can press the emergency button on their device and the device will send an emergency notification along with their current location to the server. Based on the emergency notification, the server will notify pre-registered emergency contacts of their current location and situation via email or SMS.

[1401] 5. Advice and Instruction Generation by Conversational AI

[1402] Users can ask questions or give instructions to the device through voice commands. For example, when a user asks a question such as "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[1403] 6. Emotion Recognition by Emotion Engine

[1404] The system also uses an emotion engine to recognize the user's emotions. The emotion engine uses voice analysis and facial recognition technology to detect the user's emotions. For example, if the user's voice is unstable or their facial expression is tense, this information is analyzed. Based on the recognized emotions, the system generates appropriate responses or instructions.

[1405] Specific examples

[1406] Example 1: Navigation on the go

[1407] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[1408] 2. The device converts this voice command into text and sends it to the server.

[1409] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[1410] 4. The device will provide voice guidance for this route to the user and begin navigation.

[1411] 5. The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "I'll guide you a little more slowly so you can feel at ease."

[1412] Example 2: Emergency response

[1413] 1. The user falls while out and presses the emergency button.

[1414] 2. The device sends an emergency notification to the server.

[1415] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[1416] 4. The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Remain calm, help is on the way," and sends it to the device.

[1417] 5. The device will then audibly convey this message to the user.

[1418] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[1419] The processing flow will be explained below.

[1420] Location tracking

[1421] Step 1:

[1422] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[1423] Step 2:

[1424] The location information acquired by the device is temporarily stored in memory.

[1425] Step 3:

[1426] The device sends location information to the server at set intervals (e.g., every minute).

[1427] Step 4:

[1428] The server records the received location information in a database.

[1429] Step 5:

[1430] The server records the time the location information was received and waits for the next update.

[1431] Learning behavioral patterns

[1432] Step 1:

[1433] The server retrieves previously stored location information from the database.

[1434] Step 2:

[1435] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[1436] Step 3:

[1437] The server creates a dataset based on the analysis results to learn user behavior patterns.

[1438] Step 4:

[1439] The server uses a machine learning model to learn user behavior patterns and stores the results.

[1440] Optimal route suggestions

[1441] Step 1:

[1442] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[1443] Step 2:

[1444] The terminal transmits the set destination to the server.

[1445] Step 3:

[1446] The server acquires the user's past behavioral patterns and real-time traffic information.

[1447] Step 4:

[1448] The server calculates the optimal travel route and sends the calculation results to the terminal.

[1449] Step 5:

[1450] The terminal displays the optimal route to the user and starts voice guidance.

[1451] emergency notification

[1452] Step 1:

[1453] When an emergency occurs, the user presses the emergency button on the terminal.

[1454] Step 2:

[1455] The device detects when the emergency button is pressed and obtains the current location information.

[1456] Step 3:

[1457] The device sends the acquired location information and emergency notification to the server.

[1458] Step 4:

[1459] The server will send your location and emergency information to your emergency contacts via email or SMS.

[1460] Conversational AI for generating advice and instructions

[1461] Step 1:

[1462] The user speaks a question to the device, such as "Where are you now?"

[1463] Step 2:

[1464] The device converts the voice data it receives into text and sends it to the server.

[1465] Step 3:

[1466] The server parses the received text data and generates an appropriate response.

[1467] Step 4:

[1468] The server generates a response and sends it to the terminal in text format.

[1469] Step 5:

[1470] The terminal converts the text data into speech and provides the user with the response content.

[1471] Emotion recognition by emotion engine

[1472] Step 1:

[1473] The user gives instructions and asks questions to the terminal by voice.

[1474] Step 2:

[1475] The terminal records the user's voice and transmits the voice data to the server.

[1476] Step 3:

[1477] The server analyzes the voice data and recognizes the user's emotions using an emotion engine.

[1478] Step 4:

[1479] The server generates appropriate advice and instructions based on the emotional state.

[1480] Step 5:

[1481] The server sends the generated advice and instructions to the terminal, which then conveys them to the user by voice.

[1482] Specific examples

[1483] Example 1: Navigation on the go

[1484] Step 1:

[1485] The user speaks to the terminal, saying, "I want to go to the supermarket."

[1486] Step 2:

[1487] The device converts this voice command into text and sends it to the server.

[1488] Step 3:

[1489] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[1490] Step 4:

[1491] The server sends the calculation results to the terminal.

[1492] Step 5:

[1493] The terminal will then provide voice guidance for this route to the user and begin navigation.

[1494] Step 6:

[1495] The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "We'll guide you a little more slowly so that you can feel at ease."

[1496] Example 2: Emergency response

[1497] Step 1:

[1498] The user falls while out and presses the emergency button.

[1499] Step 2:

[1500] The device sends an emergency notification to the server.

[1501] Step 3:

[1502] The server will notify your emergency contact of your current location and status via email or SMS.

[1503] Step 4:

[1504] The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Stay calm, help is on the way," and sends it to the device.

[1505] Step 5:

[1506] The terminal will then audibly convey this message to the user.

[1507] Example 2

[1508] 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."

[1509] There is an urgent need to improve the efficiency of responding to lost or emergency situations when elderly people are out and about, and to provide an environment where they can be active and feel safe. There is also a need for flexible support that takes into account the user's emotions. Conventional technologies have limited functionality for acquiring location information and emergency notifications, and lack the ability to recognize the user's emotions in real time and respond accordingly. Therefore, it is necessary to provide a system that allows elderly people to go out safely and with peace of mind.

[1510] 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.

[1511] In this invention, the server includes a means for constantly acquiring location information, a means for transmitting acquired location data to a central processing unit, a means for recording and analyzing the location data in the central processing unit, a means for calculating an optimal route and generating travel instructions based on the analysis results, a means for automatically notifying emergency contacts in the event of an emergency, and a means for analyzing the user's voice and facial expressions to recognize emotions and generate an appropriate response. This allows elderly people to constantly track their location, reducing the risk of getting lost and allowing them to receive prompt notification and assistance in the event of an emergency. Furthermore, the emotion recognition function provides more personalized support, increasing the user's sense of security and trust.

[1512] "Means for acquiring location information at any time" is a general term for sensors and devices that can acquire multiple pieces of location information in real time, as well as methods for acquiring that data.

[1513] "Means for transmitting acquired location data to a central processing unit" is a general term for means for collecting location information and transmitting it to a remote server or central processing unit via communication means.

[1514] "Central Processing Unit" is a general term for a computer system or server that records and analyzes received data.

[1515] "Means for recording and analyzing location data" is a general term for software and algorithms that store location information and analyze user behavior patterns and trends based on that data.

[1516] "Means for calculating the optimal route and generating travel instructions" is a general term for functions that take into account the user's current location, destination, and suspicious points, calculate an efficient travel route, and provide instructions to the user.

[1517] "Means for automatically notifying emergency contacts in the event of an emergency" is a general term for a system or function that automatically notifies pre-registered emergency contacts of current location information, etc. when an emergency occurs, such as when a user presses an emergency button.

[1518] "Means of analyzing a user's voice and facial expressions to recognize emotions and generate appropriate responses" is a general term for technologies and algorithms that read emotions from a user's voice and facial expressions and provide appropriate responses and guidance based on that information.

[1519] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, notifies users in emergencies, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine, providing flexible responses and a sense of security to users.

[1520] Location tracking

[1521] The system's terminals are equipped with GPS sensors that constantly acquire the elderly person's current location. The terminals transmit this location information in real time to a server, which then records the received location information in a database. The server then analyzes the location information to determine the user's current location and movement patterns.

[1522] Learning behavioral patterns

[1523] The server analyzes the user's behavioral patterns based on the accumulated location data, including frequently visited locations and specific travel routes. Using the results of this analysis, the server uses machine learning algorithms to learn the user's behavioral patterns and optimize next route suggestions and emergency response.

[1524] Optimal route suggestions

[1525] When the user sets their destination, the device sends this information to the server. The server uses past behavioral pattern data and real-time traffic information to calculate the optimal route and sends the results to the device. The device then presents the optimal route to the user through voice guidance and a display.

[1526] emergency notification

[1527] If a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server. Based on the emergency notification received, the server notifies pre-registered emergency contacts of the user's current location and situation via email or SMS. In addition, the server uses an emotion engine to analyze the user's emotional state, generates an appropriate reassuring message, and sends it to the device. The device then conveys this message to the user via voice.

[1528] Conversational AI for generating advice and instructions

[1529] Users can ask questions or give instructions to the device through voice commands. For example, if a user asks "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[1530] Emotion recognition by emotion engine

[1531] The system recognizes the user's emotions using an emotion engine, which uses voice analysis and facial recognition technology to detect the user's emotional state. For example, if the user's voice is unstable or their facial expression is tense, the system will analyze this and generate appropriate responses or instructions.

[1532] Specific examples

[1533] Prompt Sentence Examples

[1534] Explain how a system works when it suggests the optimal route when a user says they want to go to the supermarket.

[1535] Describe how a system works that sends an emergency notification when a user falls.

[1536] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[1537] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1538] Step 1:

[1539] The user goes out with the terminal.

[1540] Specific operation: The user carries the device when out and about. The device has a built-in GPS sensor, so location information can be obtained at all times.

[1541] Step 2:

[1542] The device obtains location information using the GPS sensor.

[1543] Input: User's current location (GPS data).

[1544] Specific operation: The device uses the GPS sensor at regular intervals (e.g., every 10 seconds) to determine its current location.

[1545] Output: Current location (latitude and longitude).

[1546] Step 3:

[1547] The location information acquired by the device is sent to the server.

[1548] Input: Current location (latitude and longitude).

[1549] What it does: The device sends real-time location information to the server over a mobile network or Wi-Fi.

[1550] Output: The location sent to the server.

[1551] Step 4:

[1552] The server records the received location information in a database.

[1553] Input: The location information sent.

[1554] Specific operation: The server stores the location information it receives in a database in chronological order.

[1555] Output: Location information recorded in a database.

[1556] Step 5:

[1557] The server analyzes the location information.

[1558] Input: Location information stored in a database.

[1559] How it works: The server analyzes the user's movement patterns and frequently visited locations based on the accumulated location information. Clustering and time series analysis algorithms are used.

[1560] Output: Analysis results (user movement patterns and frequently visited places).

[1561] Step 6:

[1562] The user sets the destination.

[1563] Specific action: The user enters a destination using voice commands or a touchscreen.

[1564] Input: Destination information.

[1565] Step 7:

[1566] The terminal transmits the destination information to the server.

[1567] Input: Destination information.

[1568] Specific operation: The device sends the entered destination information to the server.

[1569] Output: The destination information sent to the server.

[1570] Step 8:

[1571] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information.

[1572] Input: Historical behavioral pattern data, real-time traffic information, and destination information.

[1573] Specific operation: The server calculates the optimal route using past recorded behavior data and real-time traffic information (e.g., Google Maps API).

[1574] Output: Optimal route directions.

[1575] Step 9:

[1576] The server sends the calculation results to the terminal.

[1577] Input: Best Route Directions information.

[1578] Specific operation: The server sends the generated optimal route information to the terminal.

[1579] Output: The best route directions sent to the device.

[1580] Step 10:

[1581] The terminal presents the optimal route to the user using voice guidance, etc.

[1582] Input: Best Route Directions information.

[1583] Specific operation: The device guides the user to the optimal route through voice navigation and on-screen display.

[1584] Output: The optimal route presented to the user.

[1585] Step 11:

[1586] If the user encounters an emergency, he presses the emergency button.

[1587] Specific action: The user presses the emergency button on the device.

[1588] Input: Emergency button press information.

[1589] Step 12:

[1590] The device sends an emergency notification to the server along with its current location.

[1591] Input: Emergency button press information, current location information.

[1592] Specific operation: The device sends emergency button press information and current location information to the server.

[1593] Output: The emergency notification information sent to the server.

[1594] Step 13:

[1595] The server notifies the emergency contact of the current location and situation.

[1596] Input: Emergency notification information, emergency contact information.

[1597] Specific operation: The server notifies registered emergency contacts of the current location and situation via email or SMS.

[1598] Output: The notification message sent to the emergency contact.

[1599] Step 14:

[1600] The emotion engine analyzes the user's voice and facial expressions.

[1601] Input: User's voice data, facial expression data (camera footage).

[1602] How it works: The emotion engine uses voice analysis and facial recognition algorithms to analyze the user's emotions.

[1603] Output: The user's emotional state (anxious, relieved, etc.).

[1604] Step 15:

[1605] The server generates a reassuring message and sends it to the device.

[1606] Input: The user's emotional state.

[1607] Specific behavior: The server generates a message to reassure the user based on the analysis results.

[1608] Output: The reassurance message sent to the device.

[1609] Step 16:

[1610] The terminal will then audibly convey the message to the user.

[1611] Input: Reassurance message.

[1612] Specific operation: The device uses speech synthesis technology to audibly convey a reassuring message to the user.

[1613] Output: The message spoken to the user.

[1614] (Application example 2)

[1615] 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."

[1616] The challenge is to reduce the risk of elderly people getting lost when they go out and provide them with a means of moving safely and with peace of mind. Another important challenge is to respond quickly and appropriately in emergencies and reduce the anxiety of elderly people. Furthermore, there is a need to realize more effective mobility support by learning the individual behavioral patterns of elderly people and providing personalized assistance.

[1617] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for constantly acquiring location information; means for transmitting the acquired location data to a central processing unit; means for recording and analyzing the location data in the central processing unit; means for calculating an optimal route and generating travel instructions based on the analysis results; means for automatically notifying emergency contacts in the event of an emergency; means for learning behavioral patterns based on the acquired location data and identifying frequently visited locations and routes; means for calculating an optimal route based on past behavioral patterns and real-time traffic information and reflecting the results in the autonomous vehicle; means for accepting voice commands, transmitting voice data to the central processing unit, and generating appropriate advice and instructions; and means for detecting user emotions using voice analysis and facial recognition technology and generating appropriate responses. This reduces the risk of elderly people getting lost when out and allows them to travel safely and with peace of mind. It also enables prompt and appropriate responses in emergencies to alleviate elderly anxiety. Furthermore, personalized assistance based on behavioral pattern learning can be provided, thereby achieving more effective mobility support.

[1618] A "means for continuously obtaining location information" is a device or method that continuously obtains the current location of an elderly person using a GPS sensor or other location tracking technology.

[1619] The "means for transmitting the acquired location data to the central processing unit" refers to a communication means for transmitting the elderly person's current location information to a server or cloud computing system via a network.

[1620] "Means for recording and analyzing location data at a central processing unit" refers to a computer system and algorithms for recording transmitted location information in a database and analyzing the data.

[1621] The "means for calculating the optimal route based on the analysis results and generating travel instructions" refers to an algorithm or system that calculates the most suitable travel route for an elderly person and generates travel instructions based on the analysis results of location data and traffic information.

[1622] "Means for automatically notifying emergency contacts in the event of an emergency" refers to an alarm system and communication means for automatically notifying pre-registered contact persons in the event of an emergency.

[1623] "Means for learning behavioral patterns based on acquired location data and identifying frequently visited places and routes" refers to a machine learning algorithm that analyzes collected location data to learn the behavioral patterns of elderly people and identify frequently visited places and routes they use.

[1624] "Means of calculating the optimal route based on past behavioral patterns and real-time traffic information, and reflecting this in autonomous vehicles" refers to technology that determines the optimal travel route based on accumulated behavioral data and current traffic conditions, and integrates this information into the route planning system of autonomous vehicles.

[1625] The "means for accepting voice commands, transmitting voice data to a central processing unit, and generating appropriate advice or instructions" refers to a speech recognition and natural language processing system that recognizes voice input from the senior, converts the speech into text, transmits it to a central processing unit, and generates and returns an appropriate response.

[1626] "Means for detecting a user's emotions using voice analysis and facial recognition technology and generating an appropriate response" refers to an emotion analysis engine and facial recognition system that identifies a user's emotional state by analyzing changes in voice tone and facial expression, and generates an appropriate response according to that state.

[1627] The following describes in detail the mode for carrying out this invention. This invention is a system that reduces the risk of elderly people getting lost when they go out, allowing them to move around safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine.

[1628] The implementation of this system uses the following hardware and software:

[1629] Hardware and software used

[1630] 1. GPS sensor: Used to obtain the location of the elderly person at any time.

[1631] 2. Central processing unit (server): Contains a database and analysis algorithms for recording and analyzing acquired location data.

[1632] 3. Communication unit: A network communication device for transmitting acquired location data to a server.

[1633] 4. Self-driving vehicles: Vehicles that safely transport seniors to their destinations.

[1634] 5. Conversational AI systems: Software for speech recognition and generating appropriate advice and instructions.

[1635] 6. Emotion Analysis Engine and Facial Recognition System: Software for detecting user emotions using voice analysis and facial recognition technology.

[1636] Processing flow

[1637] 1. Location information acquisition: The server acquires the current location of the elderly person from the GPS sensor. This data is sequentially sent to the server and recorded in the database.

[1638] 2. Learning behavioral patterns: The server analyzes the acquired location data and learns the behavioral patterns of the elderly, for example, identifying frequently visited places and routes.

[1639] 3. Calculating the optimal route: When the elderly person inputs their destination, the server calculates the optimal route based on their past behavior patterns and real-time traffic information, and sends that information to the autonomous vehicle, which then uses this information to direct the elderly person to their destination.

[1640] 4. Emergency notification: If an elderly person experiences an emergency, such as a fall, they can press the emergency button, which sends an emergency notification along with their current location to the server. The server then notifies the emergency contact and generates a response to reassure the elderly person and sends it to their device.

[1641] 5. Conversational AI for advice and instruction generation: Users can ask questions or give instructions to the server through voice commands. The server analyzes this voice data and generates appropriate responses.

[1642] 6. Emotion Recognition: Using an emotion analysis engine and facial recognition system, the system detects the user's emotions and generates appropriate responses. For example, if the user appears anxious, the system generates a reassuring response.

[1643] Specific examples

[1644] Example 1: If an elderly person gets into an autonomous vehicle and says, "I want to go to the supermarket," the system will calculate the optimal route based on their past behavioral patterns and current traffic information, and reflect this in the autonomous vehicle. If the user seems anxious, the system will respond by saying, "Don't worry, we'll proceed slowly."

[1645] Example 2: If an elderly person falls while out, pressing the emergency button will send their current location information to the server, which will automatically notify emergency contacts. The server will then generate a message such as "Help is on the way, so don't worry," and the message will be played by voice on the device.

[1646] Prompt Sentence Examples

[1647] The following prompt sentence can be used to have a generative AI model create a detailed response about support systems for the elderly:

[1648] text

[1649] Please explain the support system for elderly people using self-driving vehicles. This system supports elderly people in going out safely and securely, and has functions such as location tracking, learning behavioral patterns, suggesting optimal routes, emergency notifications, emotion recognition using an emotion engine, and advice through conversational AI. Specifically, when an elderly person says, "I want to go to the supermarket," the system calculates the optimal route and provides a sense of security.

[1650] In this way, the mode for carrying out the invention can be concretely shown.

[1651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1652] Step 1:

[1653] The user uses the terminal to input a voice command such as "I want to go to the supermarket." This voice data is converted into text data by the terminal and sent to the server.

[1654] Step 2:

[1655] The server receives the input text data and analyzes it based on past behavioral patterns and real-time traffic information. The results of this analysis are used to calculate the optimal route. In calculating this optimal route, data such as behavioral patterns and frequently visited locations is used to process and calculate the data.

[1656] Step 3:

[1657] The server sends the calculated optimal route to the autonomous vehicle's system, which uses this information to update its route plan and safely begin driving. The output is the updated route information.

[1658] Step 4:

[1659] The terminal provides navigation information to the user through voice guidance. The user's location information is acquired continuously and transmitted to the server in real time. The output is the user's current location information.

[1660] Step 5:

[1661] The server analyzes the location information and continues to learn behavioral patterns. Specifically, it analyzes the data to identify frequently visited locations and routes. The results of this analysis are used to suggest the next route.

[1662] Step 6:

[1663] If a user encounters an emergency while out and about, they press the emergency button on their device. The device then sends their current location and emergency notification to the server. The server then receives this information and notifies pre-registered emergency contacts. The output is the information of the notified emergency contacts and their current situation.

[1664] Step 7:

[1665] The server uses voice analysis and facial recognition technology to detect the user's emotions. Specifically, it analyzes voice tone and facial expressions to identify emotional states such as anxiety or tension. The results of this analysis are sent to the conversational AI system. The output is analyzed emotional data.

[1666] Step 8:

[1667] The conversational AI system generates an appropriate response based on the user's emotional state. For example, if the user seems anxious, it generates a response such as "Don't worry." This response is sent to the terminal and notified to the user by voice. The output is the generated response message.

[1668] This enables the entire system to function as a support system for elderly people to go out safely and securely.

[1669] 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.

[1670] 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.

[1671] 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.

[1672] [Fourth embodiment]

[1673] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1674] 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.

[1675] 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).

[1676] 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.

[1677] 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.

[1678] 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).

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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."

[1686] MODE FOR CARRYING OUT THE INVENTION

[1687] This invention is a system that enables elderly people to go out safely and without getting lost. The system has functions for tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[1688] 1. Location Tracking

[1689] The system's terminal uses a GPS sensor to constantly obtain the elderly person's current location and transmits this information to a server. The server records the received location information in a database and performs analysis. This information is later used to learn behavioral patterns and calculate optimal routes.

[1690] 2. Learning behavioral patterns

[1691] The server analyzes the user's movement patterns based on the received location data and identifies frequently visited locations and routes, allowing the server to learn each user's individual behavioral patterns and provide personalized services.

[1692] 3. Optimal route suggestions

[1693] When the user sets a destination, the device notifies the server. The server calculates the optimal route based on past behavior patterns and real-time traffic information, and sends the results to the device. The device then displays this route to the user and provides voice guidance.

[1694] 4. Emergency notification

[1695] When a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server, which then immediately sends their current location and emergency information to their emergency contacts to promptly respond.

[1696] 5. Advice and Instruction Generation by Conversational AI

[1697] Users can ask questions or give instructions to the device using voice commands. For example, when a user asks a question such as "Where are you now?", the device converts the voice into text data and sends it to the server. The server analyzes the voice data, generates an appropriate response (e.g., "You are currently on Main Street"), and sends it to the device. The device then notifies the user of this response by voice.

[1698] Specific examples

[1699] Example 1: Navigation on the go

[1700] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[1701] 2. The device converts this voice command into text and sends it to the server.

[1702] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[1703] 4. The device will provide voice guidance for this route to the user and begin navigation.

[1704] 5. The user follows the directions to safely reach the supermarket.

[1705] Example 2: Emergency response

[1706] 1. The user falls while out and presses the emergency button.

[1707] 2. The device sends an emergency notification to the server.

[1708] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[1709] 4. Emergency contacts will be notified and will take necessary action.

[1710] This system not only allows seniors to feel safe when going out, but also allows for a quick response in the event of an emergency, and uses conversational AI to provide an even higher level of support and engagement.

[1711] The processing flow will be explained below.

[1712] Location tracking

[1713] Step 1:

[1714] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[1715] Step 2:

[1716] The location information acquired by the device is temporarily stored in memory.

[1717] Step 3:

[1718] The device sends location information to the server at set intervals (e.g., every minute).

[1719] Step 4:

[1720] The server records the received location information in a database.

[1721] Step 5:

[1722] The server records the time the location information was received and waits for the next update.

[1723] Learning behavioral patterns

[1724] Step 1:

[1725] The server retrieves previously stored location information from the database.

[1726] Step 2:

[1727] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[1728] Step 3:

[1729] The server creates a dataset based on the analysis results to learn user behavior patterns.

[1730] Step 4:

[1731] The server uses a machine learning model to learn user behavior patterns and stores the results.

[1732] Optimal route suggestions

[1733] Step 1:

[1734] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[1735] Step 2:

[1736] The terminal transmits the set destination to the server.

[1737] Step 3:

[1738] The server acquires the user's past behavioral patterns and real-time traffic information.

[1739] Step 4:

[1740] The server calculates the optimal travel route and sends the calculation results to the terminal.

[1741] Step 5:

[1742] The terminal displays the optimal route to the user and starts voice guidance.

[1743] emergency notification

[1744] Step 1:

[1745] When an emergency occurs, the user presses the emergency button on the terminal.

[1746] Step 2:

[1747] The device detects when the emergency button is pressed and obtains the current location information.

[1748] Step 3:

[1749] The device sends the acquired location information and emergency notification to the server.

[1750] Step 4:

[1751] The server will send your location and emergency information to your emergency contacts via email or SMS.

[1752] Conversational AI for generating advice and instructions

[1753] Step 1:

[1754] The user speaks a question to the device, such as "Where are you now?"

[1755] Step 2:

[1756] The device converts the voice data it receives into text and sends it to the server.

[1757] Step 3:

[1758] The server parses the received text data and generates an appropriate response.

[1759] Step 4:

[1760] The server generates a response and sends it to the terminal in text format.

[1761] Step 5:

[1762] The terminal converts the text data into speech and provides the user with the response content.

[1763] Specific examples

[1764] Example 1: Navigation on the go

[1765] Step 1:

[1766] The user speaks to the terminal, saying, "I want to go to the supermarket."

[1767] Step 2:

[1768] The device converts this voice command into text and sends it to the server.

[1769] Step 3:

[1770] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[1771] Step 4:

[1772] The server sends the calculation results to the terminal.

[1773] Step 5:

[1774] The terminal will then provide voice guidance for this route to the user and begin navigation.

[1775] Example 2: Emergency response

[1776] Step 1:

[1777] The user falls while out and presses the emergency button.

[1778] Step 2:

[1779] The device sends an emergency notification to the server.

[1780] Step 3:

[1781] The server will notify your emergency contact of your current location and status via email or SMS.

[1782] Step 4:

[1783] Emergency contacts will be notified and will take necessary action.

[1784] Example 1

[1785] 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."

[1786] In modern society, it is important for elderly people to be able to go out safely and with peace of mind. However, concerns about getting lost or traveling, as well as rapid response in emergencies, remain issues. Elderly people, in particular, tend to have limited mobility, so rapid response is required in emergencies. However, existing systems rarely provide integrated location tracking, behavioral pattern learning, optimal route suggestions, and emergency notifications, and they also lack support from conversational AI. Therefore, there is a need for a more comprehensive and personalized outing support system for elderly people.

[1787] 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.

[1788] In this invention, the server includes means for constantly acquiring locations, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for calculating an optimal route and generating travel instructions based on the analysis results, means for automatically notifying emergency contacts in the event of an emergency, means for converting voice commands into text data, means for transmitting voice data to the central processing unit and generating an appropriate response, and means for generating a voice response and notifying the user. This allows elderly people to go out with peace of mind, enables quick response in the event of an emergency, and further enables personalized support based on behavioral patterns and advanced support using conversational AI to be provided.

[1789] "Means for obtaining location information on a regular basis" refers to a device or method that periodically obtains the user's current location using a GPS sensor or other location measurement device.

[1790] "Means for transmitting to the central processing unit" refers to a device or method for transmitting location data and other necessary information to the central processing unit via wireless communication, the Internet, etc.

[1791] "Central processing unit" refers to a computer or server that records received data and performs analysis, etc.

[1792] "Means for recording and analyzing location data" refers to a device or method that stores received location data in a database or the like and analyzes the user's behavioral patterns and current location based on this data.

[1793] "Means for calculating the optimal route and generating travel instructions" refers to a device or method that calculates the optimal route to a destination for a user based on past behavioral patterns and real-time traffic information, and generates the contents of that route as instructions.

[1794] "Means for automatically notifying emergency contacts in the event of an emergency" refers to a device or method that automatically notifies emergency contacts when a user is in an emergency.

[1795] The "means for converting voice commands into text data" refers to a device or method for converting voice commands input by a user into text format.

[1796] The "means for transmitting voice data to the central processing unit" refers to a device or method for transmitting the converted text data to the central processing unit.

[1797] The "means for generating an appropriate response" is an algorithm or software that generates an appropriate response in response to a voice command.

[1798] The "means for generating a voice response and notifying the user" refers to a device or method for notifying the user of the generated appropriate response as voice.

[1799] This invention is a system that helps elderly people move around safely and securely when they go out. The system has the functions of tracking location information, learning behavioral patterns, suggesting optimal routes, providing emergency notifications, and generating advice and instructions using conversational AI.

[1800] Hardware and software used

[1801] Hardware

[1802] Terminal: A device with a built-in GPS sensor that captures the user's location. This includes smartphones and dedicated mobile terminals.

[1803] Server: A computing device that receives, records, analyzes data, and generates appropriate responses and routes.

[1804] software

[1805] GPS data collection software: Installed on the device, it acquires location information at any time.

[1806] Communication software: Sends and receives data between the device and the server.

[1807] Database software: Records and stores location information and user behavior patterns on the server.

[1808] Analysis algorithm: Installed on the server, it learns behavioral patterns from location information and calculates the optimal route.

[1809] Conversational AI model: AI software that interprets voice commands and generates appropriate instructions or advice.

[1810] System operation example

[1811] Example 1: Navigation on the go

[1812] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[1813] 2. The device converts this voice command into text and sends it to the server.

[1814] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[1815] 4. The device will provide voice guidance for this route to the user and begin navigation.

[1816] 5. The user follows the directions to safely reach the supermarket.

[1817] Example 2: Emergency response

[1818] 1. The user falls while out and presses the emergency button.

[1819] 2. The device sends an emergency notification to the server.

[1820] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[1821] 4. Emergency contacts receive a notification and rush to the user's rescue.

[1822] Implementation details

[1823] The device periodically acquires the user's current location using the built-in GPS sensor. This location information is sent to the server via wireless communication. The server stores the received location information in a database and uses it to analyze the user's behavioral patterns.

[1824] When a user sets a destination, for example by entering a voice command such as "I want to go to the supermarket," the device converts the voice into text data and sends it to the server. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device. The device then provides this route to the user via voice guidance and on-screen display, assisting them in their journey.

[1825] If a user experiences an emergency, they can press the emergency button on their device, which will send an emergency notification to the server. The server will then receive the information and notify pre-registered emergency contacts via email or SMS, enabling prompt assistance.

[1826] Furthermore, when a user inputs a voice command such as "Where are you now?", the device converts the voice data into text and sends it to the server. The server analyzes the voice data, generates an appropriate response, and sends it back to the device, which then notifies the user as voice.

[1827] This system allows elderly people to go out with peace of mind, and provides prompt support and assistance when needed. In addition, the use of conversational AI makes interactions with users natural and intuitive.

[1828] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1829] Step 1:

[1830] The device uses the built-in GPS sensor to obtain the user's current location. The input is location information (latitude and longitude) from the GPS sensor, and the output is location data stored in the device. This acquisition is performed periodically, updating the location information every minute, for example.

[1831] Step 2:

[1832] The location data acquired by the device is sent to the server. The input is the location data stored in the device, and the output is the location data sent to the server. Communication is via wireless communication or the Internet, and data is sent in real time.

[1833] Step 3:

[1834] The server records the received location data in a database. The input is the location data sent from the device, and the output is the location data stored in the database. The recorded data is saved with the date and time and used for subsequent analysis.

[1835] Step 4:

[1836] The server periodically analyzes the location data recorded in the database. The input is the location data stored in the database, and the output is behavioral pattern data as a result of the analysis. A machine learning algorithm is used for the analysis, which identifies frequently visited places and routes.

[1837] Step 5:

[1838] The user speaks their destination into the device. The input is the user's voice command (e.g., "I want to go to the supermarket"), and the output is the speech data converted to text by the device. Speech recognition software performs this conversion.

[1839] Step 6:

[1840] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the text data sent to the server. Communication is performed via wireless communication or the Internet.

[1841] Step 7:

[1842] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information. The input is text data (destination), past behavioral patterns, and real-time traffic information, and the output is optimal route information. The optimal route is calculated using an algorithm.

[1843] Step 8:

[1844] The server sends the calculated optimal route to the terminal. The input is the optimal route information, and the output is the optimal route information sent to the terminal. Communication is again carried out via wireless communication or the Internet.

[1845] Step 9:

[1846] The terminal displays the optimal route to the user and provides voice guidance. The input is the optimal route information sent from the server, and the output is voice guidance and a display on the screen for the user. This task is performed by the navigation software.

[1847] Step 10:

[1848] When a user presses the emergency button, the device sends an emergency notification to the server. The input is the user's action (pressing the emergency button) and the output is the emergency notification data. The device sends data including its current location and status.

[1849] Step 11:

[1850] The server notifies emergency contacts of the current location and emergency. The input is the emergency notification data sent from the device, and the output is an email or SMS notification to the emergency contacts. The notification includes the current location and details of the emergency.

[1851] Step 12:

[1852] The user types a voice command (e.g., "Where am I?"). The input is the user's voice command, and the output is the data converted to text by the device. Speech recognition software performs the conversion.

[1853] Step 13:

[1854] The terminal sends text data to the server. The input is the text data converted by the terminal, and the output is the data sent to the server.

[1855] Step 14:

[1856] The server analyzes the voice data and generates an appropriate response. The input is text data, and the output is the generated response data. The generative AI model performs this analysis and response generation.

[1857] Step 15:

[1858] The server sends response data to the terminal. The input is the generated response data, and the output is the data sent to the terminal.

[1859] Step 16:

[1860] The terminal notifies the user of the response by voice. The input is the response data sent from the server, and the output is the voice response to the user. The voice synthesis software generates the voice response.

[1861] (Application example 1)

[1862] 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."

[1863] Problems such as elderly people getting lost when out and about and being unable to respond to emergencies are serious issues in an aging society. In addition, as autonomous vehicles become more widespread, it is also necessary to ensure that elderly people can use them with peace of mind and that their safety is ensured while traveling. Conventional systems have not been able to provide sufficient safety and convenience for elderly people traveling alone.

[1864] 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.

[1865] In this invention, the server includes means for constantly acquiring location information, means for transmitting acquired location data to a central processing unit, means for recording and analyzing the location data in the central processing unit, means for sharing location information in the autonomous vehicle, means for learning the usage history of the autonomous vehicle and the behavioral patterns of the elderly person, means for calculating an optimal route based on real-time traffic information and instructing the autonomous vehicle, means for sending an emergency notification when an emergency such as a fall occurs inside the autonomous vehicle, and means for providing voice support using interactive AI. This allows elderly people to use autonomous vehicles with peace of mind, prevents them from getting lost when they are out, and enables emergency response.

[1866] "Means for obtaining location information at any time" refers to hardware and software for obtaining location information of elderly people in real time.

[1867] "Means for transmitting acquired location data to a central processing unit" refers to the communication technology and protocols for transmitting location information acquired from a terminal to a server or cloud.

[1868] "Means for recording and analyzing location data in a central processing unit" refers to a device or system that stores received location information in a database and uses analytical algorithms to calculate movement patterns and destinations.

[1869] "Means for sharing location information in self-driving vehicles" refers to technology that enables real-time exchange of location information between devices used by elderly people and self-driving vehicles.

[1870] "Means for learning the usage history of autonomous vehicles and the behavioral patterns of elderly people" refers to an AI model that learns past usage data of autonomous vehicles and the movement history of elderly people and performs pattern recognition.

[1871] "Means for calculating the optimal route based on real-time traffic information and instructing it to an autonomous vehicle" refers to a system that calculates the optimal travel route that reflects current traffic conditions and provides that route to an autonomous vehicle.

[1872] "Means for sending emergency notifications in the event of an emergency such as a fall inside an autonomous vehicle" refers to a device or system that automatically notifies pre-set emergency contacts when an elderly person experiences an emergency inside an autonomous vehicle.

[1873] "Means for providing voice support using conversational AI" refers to an AI system that uses voice recognition technology and natural language processing to generate responses and respond verbally when an elderly person uses voice commands to ask questions or give instructions.

[1874] The present invention is a system that allows elderly people to travel safely and without getting lost when they go out using an autonomous vehicle. The system includes the following main components:

[1875] Location tracking

[1876] The device uses a GPS sensor to constantly obtain the elderly person's current location. This location information is sent to a central processing unit (server) via Wi-Fi or a mobile communication network. The obtained location data is recorded in a database on the server and used for subsequent analysis.

[1877] Learning behavioral patterns

[1878] The server analyzes the acquired location data and learns the elderly person's behavioral patterns. This analysis uses pattern recognition algorithms and machine learning techniques. This identifies frequently visited places and typical travel routes. The learned pattern data is used to provide personalized services based on individual routines.

[1879] Optimal route suggestions

[1880] When the user sets a destination using voice commands, the device sends this information to the server, which calculates the optimal route based on past behavioral pattern data and real-time traffic information, and issues instructions to the autonomous vehicle, allowing the elderly person to reach their destination via the optimal route.

[1881] emergency notification

[1882] The device is equipped with an emergency button, and if an elderly person experiences an emergency, pressing this button immediately sends an emergency notification to the server. The server then sends information about the current location and the emergency to pre-set emergency contacts, enabling a prompt response.

[1883] Conversational AI for generating advice and instructions

[1884] The device is equipped with a voice input function, and when the elderly person issues a voice command, the voice data is converted into text and sent to the server. The server analyzes the voice data and generates appropriate advice or instructions. The generated response is then notified to the elderly person via voice via the device.

[1885] Hardware and software used

[1886] GPS sensor: Obtains the elderly person's current location.

[1887] Wi-Fi / Cellular Network: Transmitting location data.

[1888] Database: Recording and storing location data.

[1889] Pattern recognition algorithms, machine learning techniques: learning behavioral patterns.

[1890] Real-time traffic information system: Calculating optimal routes.

[1891] Emergency button: Triggers emergency notifications.

[1892] Speech recognition technology, natural language processing (NLP) technology: analysis of voice input and conversational AI.

[1893] Specific examples

[1894] For example, if an elderly person says "I want to go to the hospital," the device converts this voice command into text and sends it to the server. The server calculates the optimal route based on behavioral patterns and real-time traffic information, and issues instructions to the autonomous vehicle. If an elderly person falls inside the vehicle, they can press the emergency button, which will send an emergency notification to their family and medical institutions.

[1895] Prompt Sentence Examples

[1896] "Tell me where you are."

[1897] "Tell me the best route"

[1898] "I fell, please send an emergency notification."

[1899] In this way, the invention provides an environment in which elderly people can use self-driving vehicles with peace of mind.

[1900] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1901] Step 1:

[1902] Acquiring GPS location information

[1903] Input: The device's GPS sensor detects the elderly person's current location.

[1904] Operation: The device periodically obtains location data (latitude and longitude) from the GPS module.

[1905] Output: The acquired position data is stored in memory.

[1906] Step 2:

[1907] Sending location data

[1908] Input: Location data stored on the device.

[1909] How it works: The device sends the acquired location data to a server via Wi-Fi or cellular networks.

[1910] Output: The location data received by the server.

[1911] Step 3:

[1912] Recording and analyzing location data

[1913] Input: The location data received by the server.

[1914] How it works: The server records location data in a database and analyzes the data using pattern recognition algorithms to identify frequently visited locations and travel routes.

[1915] Output: Recorded location data and analysis results (behavioral patterns).

[1916] Step 4:

[1917] Processing voice commands

[1918] Input: A voice command given by the user.

[1919] How it works: The device uses a microphone to capture voice commands, converts them into text data using speech recognition technology, and sends the text data to a server.

[1920] Output: Text data (converted voice commands).

[1921] Step 5:

[1922] Destination and optimal route calculation

[1923] Input: Text data of voice commands, behavioral pattern data, real-time traffic information.

[1924] How it works: The server identifies the destination based on voice commands and calculates the optimal route, taking into account behavioral patterns and real-time traffic information.

[1925] Output: Optimal route information.

[1926] Step 6:

[1927] Optimal route instructions

[1928] Input: Optimal route information calculated by the server.

[1929] Operation: The server sends instructions to the self-driving vehicle, which then guides the elderly person to their destination. At the same time, it sends information about the optimal route to the terminal and starts voice guidance.

[1930] Output: Navigation instructions provided to the senior citizen.

[1931] Step 7:

[1932] Emergency detection and notification

[1933] Input: The elderly person presses the emergency button on the device.

[1934] Operation: The device sends an emergency notification signal to the server, which then notifies the elderly person's location data and the emergency contacts.

[1935] Output: Emergency notification sent to emergency contacts.

[1936] Step 8:

[1937] Advice and instructions from conversational AI

[1938] Input: The next voice command the user will say.

[1939] How it works: The device again converts the voice command into text data using speech recognition technology and sends it to the server. The server uses a generative AI model to generate appropriate advice or instructions, and then uses speech synthesis technology to return the text data to the user.

[1940] Output: Spoken advice and instructions.

[1941] 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.

[1942] MODE FOR CARRYING OUT THE INVENTION

[1943] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active with peace of mind. It also incorporates an emotion engine that takes into account the user's emotions and enables flexible responses. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using conversational AI, and recognizes emotions through the emotion engine.

[1944] 1. Location Tracking

[1945] The system's device uses a GPS sensor to obtain the elderly person's current location and sends this information to a server. The server records the received location information in a database and performs analysis. This information is used to learn behavioral patterns and calculate optimal routes.

[1946] 2. Learning behavioral patterns

[1947] The server analyzes the user's movement patterns based on the received location data. It identifies frequently visited locations and routes and performs machine learning to provide personalized behavioral support for each user. Based on this learning result, it optimizes next route suggestions and emergency responses.

[1948] 3. Optimal route suggestions

[1949] When the user sets a destination, the device sends this information to the server. The server calculates the optimal route based on past behavioral pattern data and real-time traffic information, and sends the results to the device. The device then presents the optimal route to the user using voice guidance, etc.

[1950] 4. Emergency notification

[1951] If a user experiences an emergency (e.g., falls), they can press the emergency button on their device and the device will send an emergency notification along with their current location to the server. Based on the emergency notification, the server will notify pre-registered emergency contacts of their current location and situation via email or SMS.

[1952] 5. Advice and Instruction Generation by Conversational AI

[1953] Users can ask questions or give instructions to the device through voice commands. For example, when a user asks a question such as "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[1954] 6. Emotion Recognition by Emotion Engine

[1955] The system also uses an emotion engine to recognize the user's emotions. The emotion engine uses voice analysis and facial recognition technology to detect the user's emotions. For example, if the user's voice is unstable or their facial expression is tense, this information is analyzed. Based on the recognized emotions, the system generates appropriate responses or instructions.

[1956] Specific examples

[1957] Example 1: Navigation on the go

[1958] 1. The user speaks into the device, saying, "I want to go to the supermarket."

[1959] 2. The device converts this voice command into text and sends it to the server.

[1960] 3. The server calculates the optimal route based on past behavioral patterns and real-time traffic information, and sends the results to the device.

[1961] 4. The device will provide voice guidance for this route to the user and begin navigation.

[1962] 5. The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "I'll guide you a little more slowly so you can feel at ease."

[1963] Example 2: Emergency response

[1964] 1. The user falls while out and presses the emergency button.

[1965] 2. The device sends an emergency notification to the server.

[1966] 3. The server notifies the emergency contact of the current location and situation via email or SMS.

[1967] 4. The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Remain calm, help is on the way," and sends it to the device.

[1968] 5. The device will then audibly convey this message to the user.

[1969] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[1970] The processing flow will be explained below.

[1971] Location tracking

[1972] Step 1:

[1973] Your device will obtain its current latitude and longitude using the built-in GPS sensor.

[1974] Step 2:

[1975] The location information acquired by the device is temporarily stored in memory.

[1976] Step 3:

[1977] The device sends location information to the server at set intervals (e.g., every minute).

[1978] Step 4:

[1979] The server records the received location information in a database.

[1980] Step 5:

[1981] The server records the time the location information was received and waits for the next update.

[1982] Learning behavioral patterns

[1983] Step 1:

[1984] The server retrieves previously stored location information from the database.

[1985] Step 2:

[1986] The server analyzes the location information obtained and identifies frequently visited places and regular travel routes.

[1987] Step 3:

[1988] The server creates a dataset based on the analysis results to learn user behavior patterns.

[1989] Step 4:

[1990] The server uses a machine learning model to learn user behavior patterns and stores the results.

[1991] Optimal route suggestions

[1992] Step 1:

[1993] The user sets a destination (e.g., supermarket) on the device using voice or touch input.

[1994] Step 2:

[1995] The terminal transmits the set destination to the server.

[1996] Step 3:

[1997] The server acquires the user's past behavioral patterns and real-time traffic information.

[1998] Step 4:

[1999] The server calculates the optimal travel route and sends the calculation results to the terminal.

[2000] Step 5:

[2001] The terminal displays the optimal route to the user and starts voice guidance.

[2002] emergency notification

[2003] Step 1:

[2004] When an emergency occurs, the user presses the emergency button on the terminal.

[2005] Step 2:

[2006] The device detects when the emergency button is pressed and obtains the current location information.

[2007] Step 3:

[2008] The device sends the acquired location information and emergency notification to the server.

[2009] Step 4:

[2010] The server will send your location and emergency information to your emergency contacts via email or SMS.

[2011] Conversational AI for generating advice and instructions

[2012] Step 1:

[2013] The user speaks a question to the device, such as "Where are you now?"

[2014] Step 2:

[2015] The device converts the voice data it receives into text and sends it to the server.

[2016] Step 3:

[2017] The server parses the received text data and generates an appropriate response.

[2018] Step 4:

[2019] The server generates a response and sends it to the terminal in text format.

[2020] Step 5:

[2021] The terminal converts the text data into speech and provides the user with the response content.

[2022] Emotion recognition by emotion engine

[2023] Step 1:

[2024] The user gives instructions and asks questions to the terminal by voice.

[2025] Step 2:

[2026] The terminal records the user's voice and transmits the voice data to the server.

[2027] Step 3:

[2028] The server analyzes the voice data and recognizes the user's emotions using an emotion engine.

[2029] Step 4:

[2030] The server generates appropriate advice and instructions based on the emotional state.

[2031] Step 5:

[2032] The server sends the generated advice and instructions to the terminal, which then conveys them to the user by voice.

[2033] Specific examples

[2034] Example 1: Navigation on the go

[2035] Step 1:

[2036] The user speaks to the terminal, saying, "I want to go to the supermarket."

[2037] Step 2:

[2038] The device converts this voice command into text and sends it to the server.

[2039] Step 3:

[2040] The server calculates the optimal route based on past behavior patterns and real-time traffic information.

[2041] Step 4:

[2042] The server sends the calculation results to the terminal.

[2043] Step 5:

[2044] The terminal will then provide voice guidance for this route to the user and begin navigation.

[2045] Step 6:

[2046] The emotion engine analyzes the user's tone of voice and facial expressions, and if the user appears anxious, the server will make adjustments such as "We'll guide you a little more slowly so that you can feel at ease."

[2047] Example 2: Emergency response

[2048] Step 1:

[2049] The user falls while out and presses the emergency button.

[2050] Step 2:

[2051] The device sends an emergency notification to the server.

[2052] Step 3:

[2053] The server will notify your emergency contact of your current location and status via email or SMS.

[2054] Step 4:

[2055] The server analyzes the user's voice and face through an emotion engine, generates a reassuring message such as "Stay calm, help is on the way," and sends it to the device.

[2056] Step 5:

[2057] The terminal will then audibly convey this message to the user.

[2058] Example 2

[2059] 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."

[2060] There is an urgent need to improve the efficiency of responding to lost or emergency situations when elderly people are out and about, and to provide an environment where they can be active and feel safe. There is also a need for flexible support that takes into account the user's emotions. Conventional technologies have limited functionality for acquiring location information and emergency notifications, and lack the ability to recognize the user's emotions in real time and respond accordingly. Therefore, it is necessary to provide a system that allows elderly people to go out safely and with peace of mind.

[2061] 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.

[2062] In this invention, the server includes a means for constantly acquiring location information, a means for transmitting acquired location data to a central processing unit, a means for recording and analyzing the location data in the central processing unit, a means for calculating an optimal route and generating travel instructions based on the analysis results, a means for automatically notifying emergency contacts in the event of an emergency, and a means for analyzing the user's voice and facial expressions to recognize emotions and generate an appropriate response. This allows elderly people to constantly track their location, reducing the risk of getting lost and allowing them to receive prompt notification and assistance in the event of an emergency. Furthermore, the emotion recognition function provides more personalized support, increasing the user's sense of security and trust.

[2063] "Means for acquiring location information at any time" is a general term for sensors and devices that can acquire multiple pieces of location information in real time, as well as methods for acquiring that data.

[2064] "Means for transmitting acquired location data to a central processing unit" is a general term for means for collecting location information and transmitting it to a remote server or central processing unit via communication means.

[2065] "Central Processing Unit" is a general term for a computer system or server that records and analyzes received data.

[2066] "Means for recording and analyzing location data" is a general term for software and algorithms that store location information and analyze user behavior patterns and trends based on that data.

[2067] "Means for calculating the optimal route and generating travel instructions" is a general term for functions that take into account the user's current location, destination, and suspicious points, calculate an efficient travel route, and provide instructions to the user.

[2068] "Means for automatically notifying emergency contacts in the event of an emergency" is a general term for a system or function that automatically notifies pre-registered emergency contacts of current location information, etc. when an emergency occurs, such as when a user presses an emergency button.

[2069] "Means of analyzing a user's voice and facial expressions to recognize emotions and generate appropriate responses" is a general term for technologies and algorithms that read emotions from a user's voice and facial expressions and provide appropriate responses and guidance based on that information.

[2070] This invention is a system that prevents elderly people from getting lost when they go out and provides an environment where they can be active safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, notifies users in emergencies, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine, providing flexible responses and a sense of security to users.

[2071] Location tracking

[2072] The system's terminals are equipped with GPS sensors that constantly acquire the elderly person's current location. The terminals transmit this location information in real time to a server, which then records the received location information in a database. The server then analyzes the location information to determine the user's current location and movement patterns.

[2073] Learning behavioral patterns

[2074] The server analyzes the user's behavioral patterns based on the accumulated location data, including frequently visited locations and specific travel routes. Using the results of this analysis, the server uses machine learning algorithms to learn the user's behavioral patterns and optimize next route suggestions and emergency response.

[2075] Optimal route suggestions

[2076] When the user sets their destination, the device sends this information to the server. The server uses past behavioral pattern data and real-time traffic information to calculate the optimal route and sends the results to the device. The device then presents the optimal route to the user through voice guidance and a display.

[2077] emergency notification

[2078] If a user experiences an emergency (e.g., a fall), they can press the emergency button on their device to send an emergency notification to the server. Based on the emergency notification received, the server notifies pre-registered emergency contacts of the user's current location and situation via email or SMS. In addition, the server uses an emotion engine to analyze the user's emotional state, generates an appropriate reassuring message, and sends it to the device. The device then conveys this message to the user via voice.

[2079] Conversational AI for generating advice and instructions

[2080] Users can ask questions or give instructions to the device through voice commands. For example, if a user asks "Where are you now?", the device converts this into text data and sends it to the server. The server analyzes it, generates an appropriate response (e.g., "You are currently in the park"), and sends it to the device. The device then notifies the user of this response by voice.

[2081] Emotion recognition by emotion engine

[2082] The system recognizes the user's emotions using an emotion engine, which uses voice analysis and facial recognition technology to detect the user's emotional state. For example, if the user's voice is unstable or their facial expression is tense, the system will analyze this and generate appropriate responses or instructions.

[2083] Specific examples

[2084] Prompt Sentence Examples

[2085] Explain how a system works when it suggests the optimal route when a user says they want to go to the supermarket.

[2086] Describe how a system works that sends an emergency notification when a user falls.

[2087] This system not only allows elderly people to go out more safely, but also increases their sense of security and trust by receiving responses that take their emotions into consideration.

[2088] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2089] Step 1:

[2090] The user goes out with the terminal.

[2091] Specific operation: The user carries the device when out and about. The device has a built-in GPS sensor, so location information can be obtained at all times.

[2092] Step 2:

[2093] The device obtains location information using the GPS sensor.

[2094] Input: User's current location (GPS data).

[2095] Specific operation: The device uses the GPS sensor at regular intervals (e.g., every 10 seconds) to determine its current location.

[2096] Output: Current location (latitude and longitude).

[2097] Step 3:

[2098] The location information acquired by the device is sent to the server.

[2099] Input: Current location (latitude and longitude).

[2100] What it does: The device sends real-time location information to the server over a mobile network or Wi-Fi.

[2101] Output: The location sent to the server.

[2102] Step 4:

[2103] The server records the received location information in a database.

[2104] Input: The location information sent.

[2105] Specific operation: The server stores the location information it receives in a database in chronological order.

[2106] Output: Location information recorded in a database.

[2107] Step 5:

[2108] The server analyzes the location information.

[2109] Input: Location information stored in a database.

[2110] How it works: The server analyzes the user's movement patterns and frequently visited locations based on the accumulated location information. Clustering and time series analysis algorithms are used.

[2111] Output: Analysis results (user movement patterns and frequently visited places).

[2112] Step 6:

[2113] The user sets the destination.

[2114] Specific action: The user enters a destination using voice commands or a touchscreen.

[2115] Input: Destination information.

[2116] Step 7:

[2117] The terminal transmits the destination information to the server.

[2118] Input: Destination information.

[2119] Specific operation: The device sends the entered destination information to the server.

[2120] Output: The destination information sent to the server.

[2121] Step 8:

[2122] The server calculates the optimal route based on past behavioral pattern data and real-time traffic information.

[2123] Input: Historical behavioral pattern data, real-time traffic information, and destination information.

[2124] Specific operation: The server calculates the optimal route using past recorded behavior data and real-time traffic information (e.g., Google Maps API).

[2125] Output: Optimal route directions.

[2126] Step 9:

[2127] The server sends the calculation results to the terminal.

[2128] Input: Best Route Directions information.

[2129] Specific operation: The server sends the generated optimal route information to the terminal.

[2130] Output: The best route directions sent to the device.

[2131] Step 10:

[2132] The terminal presents the optimal route to the user using voice guidance, etc.

[2133] Input: Best Route Directions information.

[2134] Specific operation: The device guides the user to the optimal route through voice navigation and on-screen display.

[2135] Output: The optimal route presented to the user.

[2136] Step 11:

[2137] If the user encounters an emergency, he presses the emergency button.

[2138] Specific action: The user presses the emergency button on the device.

[2139] Input: Emergency button press information.

[2140] Step 12:

[2141] The device sends an emergency notification to the server along with its current location.

[2142] Input: Emergency button press information, current location information.

[2143] Specific operation: The device sends emergency button press information and current location information to the server.

[2144] Output: The emergency notification information sent to the server.

[2145] Step 13:

[2146] The server notifies the emergency contact of the current location and situation.

[2147] Input: Emergency notification information, emergency contact information.

[2148] Specific operation: The server notifies registered emergency contacts of the current location and situation via email or SMS.

[2149] Output: The notification message sent to the emergency contact.

[2150] Step 14:

[2151] The emotion engine analyzes the user's voice and facial expressions.

[2152] Input: User's voice data, facial expression data (camera footage).

[2153] How it works: The emotion engine uses voice analysis and facial recognition algorithms to analyze the user's emotions.

[2154] Output: The user's emotional state (anxious, relieved, etc.).

[2155] Step 15:

[2156] The server generates a reassuring message and sends it to the device.

[2157] Input: The user's emotional state.

[2158] Specific behavior: The server generates a message to reassure the user based on the analysis results.

[2159] Output: The reassurance message sent to the device.

[2160] Step 16:

[2161] The terminal will then audibly convey the message to the user.

[2162] Input: Reassurance message.

[2163] Specific operation: The device uses speech synthesis technology to audibly convey a reassuring message to the user.

[2164] Output: The message spoken to the user.

[2165] (Application example 2)

[2166] 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."

[2167] The challenge is to reduce the risk of elderly people getting lost when they go out and provide them with a means of moving safely and with peace of mind. Another important challenge is to respond quickly and appropriately in emergencies and reduce the anxiety of elderly people. Furthermore, there is a need to realize more effective mobility support by learning the individual behavioral patterns of elderly people and providing personalized assistance.

[2168] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for constantly acquiring location information; means for transmitting the acquired location data to a central processing unit; means for recording and analyzing the location data in the central processing unit; means for calculating an optimal route and generating travel instructions based on the analysis results; means for automatically notifying emergency contacts in the event of an emergency; means for learning behavioral patterns based on the acquired location data and identifying frequently visited locations and routes; means for calculating an optimal route based on past behavioral patterns and real-time traffic information and reflecting the results in the autonomous vehicle; means for accepting voice commands, transmitting voice data to the central processing unit, and generating appropriate advice and instructions; and means for detecting user emotions using voice analysis and facial recognition technology and generating appropriate responses. This reduces the risk of elderly people getting lost when out and allows them to travel safely and with peace of mind. It also enables prompt and appropriate responses in emergencies to alleviate elderly anxiety. Furthermore, personalized assistance based on behavioral pattern learning can be provided, thereby achieving more effective mobility support.

[2169] A "means for continuously obtaining location information" is a device or method that continuously obtains the current location of an elderly person using a GPS sensor or other location tracking technology.

[2170] The "means for transmitting the acquired location data to the central processing unit" refers to a communication means for transmitting the elderly person's current location information to a server or cloud computing system via a network.

[2171] "Means for recording and analyzing location data at a central processing unit" refers to a computer system and algorithms for recording transmitted location information in a database and analyzing the data.

[2172] The "means for calculating the optimal route based on the analysis results and generating travel instructions" refers to an algorithm or system that calculates the most suitable travel route for an elderly person and generates travel instructions based on the analysis results of location data and traffic information.

[2173] "Means for automatically notifying emergency contacts in the event of an emergency" refers to an alarm system and communication means for automatically notifying pre-registered contact persons in the event of an emergency.

[2174] "Means for learning behavioral patterns based on acquired location data and identifying frequently visited places and routes" refers to a machine learning algorithm that analyzes collected location data to learn the behavioral patterns of elderly people and identify frequently visited places and routes they use.

[2175] "Means of calculating the optimal route based on past behavioral patterns and real-time traffic information, and reflecting this in autonomous vehicles" refers to technology that determines the optimal travel route based on accumulated behavioral data and current traffic conditions, and integrates this information into the route planning system of autonomous vehicles.

[2176] The "means for accepting voice commands, transmitting voice data to a central processing unit, and generating appropriate advice or instructions" refers to a speech recognition and natural language processing system that recognizes voice input from the senior, converts the speech into text, transmits it to a central processing unit, and generates and returns an appropriate response.

[2177] "Means for detecting a user's emotions using voice analysis and facial recognition technology and generating an appropriate response" refers to an emotion analysis engine and facial recognition system that identifies a user's emotional state by analyzing changes in voice tone and facial expression, and generates an appropriate response according to that state.

[2178] The following describes in detail the mode for carrying out this invention. This invention is a system that reduces the risk of elderly people getting lost when they go out, allowing them to move around safely and with peace of mind. This system tracks location information, learns behavioral patterns, suggests optimal routes, issues emergency notifications, generates advice and instructions using a conversational AI, and recognizes emotions using an emotion engine.

[2179] The implementation of this system uses the following hardware and software:

[2180] Hardware and software used

[2181] 1. GPS sensor: Used to obtain the location of the elderly person at any time.

[2182] 2. Central processing unit (server): Contains a database and analysis algorithms for recording and analyzing acquired location data.

[2183] 3. Communication unit: A network communication device for transmitting acquired location data to a server.

[2184] 4. Self-driving vehicles: Vehicles that safely transport seniors to their destinations.

[2185] 5. Conversational AI systems: Software for speech recognition and generating appropriate advice and instructions.

[2186] 6. Emotion Analysis Engine and Facial Recognition System: Software for detecting user emotions using voice analysis and facial recognition technology.

[2187] Processing flow

[2188] 1. Location information acquisition: The server acquires the current location of the elderly person from the GPS sensor. This data is sequentially sent to the server and recorded in the database.

[2189] 2. Learning behavioral patterns: The server analyzes the acquired location data and learns the behavioral patterns of the elderly, for example, identifying frequently visited places and routes.

[2190] 3. Calculating the optimal route: When the elderly person inputs their destination, the server calculates the optimal route based on their past behavior patterns and real-time traffic information, and sends that information to the autonomous vehicle, which then uses this information to direct the elderly person to their destination.

[2191] 4. Emergency notification: If an elderly person experiences an emergency, such as a fall, they can press the emergency button, which sends an emergency notification along with their current location to the server. The server then notifies the emergency contact and generates a response to reassure the elderly person and sends it to their device.

[2192] 5. Conversational AI for advice and instruction generation: Users can ask questions or give instructions to the server through voice commands. The server analyzes this voice data and generates appropriate responses.

[2193] 6. Emotion Recognition: Using an emotion analysis engine and facial recognition system, the system detects the user's emotions and generates appropriate responses. For example, if the user appears anxious, the system generates a reassuring response.

[2194] Specific examples

[2195] Example 1: If an elderly person gets into an autonomous vehicle and says, "I want to go to the supermarket," the system will calculate the optimal route based on their past behavioral patterns and current traffic information, and reflect this in the autonomous vehicle. If the user seems anxious, the system will respond by saying, "Don't worry, we'll proceed slowly."

[2196] Example 2: If an elderly person falls while out, pressing the emergency button will send their current location information to the server, which will automatically notify emergency contacts. The server will then generate a message such as "Help is on the way, so don't worry," and the message will be played by voice on the device.

[2197] Prompt Sentence Examples

[2198] The following prompt sentence can be used to have a generative AI model create a detailed response about support systems for the elderly:

[2199] text

[2200] Please explain the support system for elderly people using self-driving vehicles. This system supports elderly people in going out safely and securely, and has functions such as location tracking, learning behavioral patterns, suggesting optimal routes, emergency notifications, emotion recognition using an emotion engine, and advice through conversational AI. Specifically, when an elderly person says, "I want to go to the supermarket," the system calculates the optimal route and provides a sense of security.

[2201] In this way, the mode for carrying out the invention can be concretely shown.

[2202] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2203] Step 1:

[2204] The user uses the terminal to input a voice command such as "I want to go to the supermarket." This voice data is converted into text data by the terminal and sent to the server.

[2205] Step 2:

[2206] The server receives the input text data and analyzes it based on past behavioral patterns and real-time traffic information. The results of this analysis are used to calculate the optimal route. In calculating this optimal route, data such as behavioral patterns and frequently visited locations is used to process and calculate the data.

[2207] Step 3:

[2208] The server sends the calculated optimal route to the autonomous vehicle's system, which uses this information to update its route plan and safely begin driving. The output is the updated route information.

[2209] Step 4:

[2210] The terminal provides navigation information to the user through voice guidance. The user's location information is acquired continuously and transmitted to the server in real time. The output is the user's current location information.

[2211] Step 5:

[2212] The server analyzes the location information and continues to learn behavioral patterns. Specifically, it analyzes the data to identify frequently visited locations and routes. The results of this analysis are used to suggest the next route.

[2213] Step 6:

[2214] If a user encounters an emergency while out and about, they press the emergency button on their device. The device then sends their current location and emergency notification to the server. The server then receives this information and notifies pre-registered emergency contacts. The output is the information of the notified emergency contacts and their current situation.

[2215] Step 7:

[2216] The server uses voice analysis and facial recognition technology to detect the user's emotions. Specifically, it analyzes voice tone and facial expressions to identify emotional states such as anxiety or tension. The results of this analysis are sent to the conversational AI system. The output is analyzed emotional data.

[2217] Step 8:

[2218] The conversational AI system generates an appropriate response based on the user's emotional state. For example, if the user seems anxious, it generates a response such as "Don't worry." This response is sent to the terminal and notified to the user by voice. The output is the generat...

Claims

1. This is a system to prevent elderly people from getting lost when they go out. A means for acquiring a position at any time; means for transmitting the acquired position data to a central processing unit; means for recording and analyzing position data in a central processing unit; means for calculating an optimal route based on the analysis results and generating travel instructions; A means of automatically notifying emergency contacts in the event of an emergency; A system including:

2. The system of claim 1 , further comprising the capability to learn behavioral patterns and provide personalized mobility assistance based on individual routines.

3. 10. The system of claim 1, further comprising means for accepting voice input and transmitting voice data to a central processing unit for generating appropriate advice or instructions.

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