system

Abstract

The system according to this embodiment aims to quickly and accurately determine the location of an elderly person who gets lost and to take appropriate action. [Solution] The system according to the embodiment comprises a collection unit, a notification unit, a confirmation unit, and a guidance unit. The collection unit collects location information of the elderly person. The notification unit notifies family members or caregivers of the location information collected by the collection unit. The confirmation unit confirms the location of the elderly person based on the location information notified by the notification unit. The guidance unit guides the elderly person based on the location confirmed by the confirmation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] [[ID=2{3]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to quickly and accurately grasp the location of an elderly person when they got lost and take appropriate measures.

[0005] The system according to the embodiment aims to quickly and accurately grasp the location of an elderly person when they get lost and take appropriate measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a notification unit, a confirmation unit, and a guidance unit. The collection unit collects location information of the elderly person. The notification unit notifies family members or caregivers of the location information collected by the collection unit. The confirmation unit confirms the elderly person's location based on the location information notified by the notification unit. The guidance unit guides the elderly person based on the location confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately determine the location of an elderly person who gets lost and take appropriate action. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The Safe Navigator System according to an embodiment of the present invention is a system for providing an environment in which elderly people can go out with peace of mind. This Safe Navigator System has the following three functions: The Safe Navigator System has a function to notify family members or caregivers using the location information of a smartphone if an elderly person gets lost. For example, if an elderly person leaves a specific area, the smartphone automatically sends location information to family members or caregivers to inform them of their current location. This function allows family members or caregivers to check the elderly person's whereabouts in real time and respond quickly. The Safe Navigator System has a function to take a picture of a person with a smartphone if the elderly person cannot recognize the person in front of them, and the AI ​​analyzes the image to provide person information. For example, if an elderly person has forgotten the face of an acquaintance, they can take a picture of that person with a smartphone, and the AI ​​will display the person's name and relationship. This function allows elderly people to interact with others with peace of mind. The Safe Navigator System has a function to provide voice directions and person introductions when needed. Even if visual information cannot be confirmed, voice guidance can be received. For example, if an elderly person gets lost, they can say "I want to go to XX" to their smartphone, and the AI ​​will provide voice directions. Furthermore, if the system cannot recognize the person in front of it, it provides information about that person via voice. This function allows elderly people to act with confidence even without visual information. These functions provide an environment where elderly people can go out with peace of mind, and families and caregivers can also check on the safety of elderly people in real time, allowing them to watch over them with peace of mind. In addition, advances in AI technology are enabling more accurate facial recognition and location information services, so it is expected to play an important role in the aging society of the future. In this way, the Safe Navigator System can provide an environment where elderly people can go out with peace of mind.

[0029] The safety navigator system according to the embodiment comprises a collection unit, a notification unit, a confirmation unit, and a guidance unit. The collection unit collects location information of the elderly person. The collection unit identifies the elderly person's current location using, for example, GPS data. The collection unit can also identify the elderly person's location using Wi-Fi location information. Furthermore, the collection unit can also identify the elderly person's location using Bluetooth® beacons. For example, the collection unit acquires GPS data and grasps the elderly person's location in real time. Wi-Fi location information identifies the elderly person's location based on the location of the Wi-Fi access point. Bluetooth beacons identify the elderly person's location based on the strength of the beacon signal. The notification unit notifies family members or caregivers of the location information collected by the collection unit. The notification unit notifies location information using, for example, SMS. Furthermore, the notification unit can also notify location information using email. Furthermore, the notification unit can also notify location information using a dedicated app. For example, the notification unit sends the elderly person's current location to family members via SMS. Email notification sends an email containing location information to family members. The dedicated app displays location information in real time and notifies family members or caregivers. The confirmation unit verifies the elderly person's location based on the location information notified by the notification unit. The confirmation unit verifies the elderly person's location using, for example, a map application. The confirmation unit can also verify the elderly person's movement history based on the location information. Furthermore, the confirmation unit can verify the accuracy of the location information. For example, the confirmation unit displays the elderly person's current location using a map application. Movement history displays past location information in chronological order. Location information accuracy is verified by checking the accuracy of GPS data. The guidance unit guides the elderly person based on the location verified by the confirmation unit. The guidance unit guides the elderly person using, for example, voice guidance. The guidance unit can also guide the elderly person using visual guidance. Furthermore, the guidance unit can also guide the elderly person using vibration guidance. For example, the guidance unit gives voice instructions such as "Turn right." Visual guidance displays the route on a map. Vibration guidance indicates direction by vibrating the smartphone.As a result, the safety navigator system according to this embodiment can collect location information of the elderly person and notify family members or caregivers, thereby confirming the elderly person's whereabouts and providing guidance.

[0030] The data collection unit collects location information of elderly individuals. For example, the unit uses GPS data to determine the elderly person's current location. Specifically, a GPS module built into the elderly person's device receives signals from satellites, analyzes those signals, and obtains latitude and longitude information. This information is transmitted in real time to a central server and stored in a database. The data collection unit can also determine the elderly person's location using Wi-Fi location information. Wi-Fi location information is a technology that measures the signal strength of surrounding Wi-Fi access points and estimates the location based on that information. For example, by measuring the signal strength of multiple Wi-Fi access points near where the elderly person is located and comparing that data with the location information of known access points, the unit can determine the elderly person's location. Furthermore, the data collection unit can also determine the elderly person's location using Bluetooth beacons. A Bluetooth beacon is a device that emits a signal within a specific range, and its location is estimated based on the strength of that signal. For example, when an elderly person passes near a Bluetooth beacon, the signal strength of that beacon is recorded on the elderly person's device, and their location is determined based on that information. This allows the data collection unit to accurately pinpoint the location of elderly individuals by combining GPS, Wi-Fi, and Bluetooth beacon technologies.

[0031] The notification unit notifies family members and caregivers of location information collected by the collection unit. The notification unit notifies location information, for example, using SMS. Specifically, when the elderly person's location information is collected, the notification unit sends this information as an SMS message to the family member's or caregiver's mobile phone. The SMS message includes the elderly person's current latitude and longitude information and a map link. The notification unit can also notify location information via email. In email notifications, an email containing the location information is sent to the family member's or caregiver's email address. The email may include detailed location information, past movement history, and additional information about the current situation. Furthermore, the notification unit can also notify location information using a dedicated app. The dedicated app is installed on smartphones and tablets and has the function of displaying the elderly person's location information in real time. The app displays the elderly person's current location on a map, allowing family members and caregivers to intuitively understand the elderly person's location. The app also has a notification function that can issue alerts when the elderly person enters or leaves a specific area. This allows the notification unit to quickly and reliably notify family members and caregivers of the elderly person's location information using SMS, email, and the dedicated app.

[0032] The confirmation unit verifies the elderly person's location based on the location information notified by the notification unit. For example, the confirmation unit uses a map application to verify the elderly person's location. Specifically, it inputs the notified location information into the map application and displays the elderly person's current location on the map. The map application provides detailed map information, allowing the user to check the elderly person's surroundings and nearby facilities. The confirmation unit can also check the elderly person's movement history based on the location information. The movement history displays past location information in chronological order, allowing the user to understand the routes the elderly person has taken. This allows the user to check the elderly person's behavior patterns and whether they have stayed in a particular location for an extended period. Furthermore, the confirmation unit can verify the accuracy of the location information. The accuracy of the location information is evaluated based on the accuracy of GPS data, the signal strength of Wi-Fi location information, and the signal strength of Bluetooth beacons. For example, if the accuracy of GPS data is low, Wi-Fi location information and Bluetooth beacon data can be used supplementarily to improve the accuracy of the location information. This allows the confirmation unit to accurately grasp the elderly person's location and respond quickly as needed.

[0033] The guidance unit directs elderly individuals based on their location, which has been confirmed by the verification unit. For example, the guidance unit can use voice guidance. Specifically, it can issue voice instructions such as "Turn right" or "Go straight" from the elderly person's device, ensuring they reach their destination without getting lost. The guidance unit can also use visual guidance. Visual guidance displays a map on the device screen, visually showing the route from the current location to the destination. This allows elderly individuals to confirm their direction of travel by looking at the map. Furthermore, the guidance unit can also use vibration guidance. Vibration guidance uses the device to indicate direction. For example, the device vibrates on the right side when it's time to turn right, prompting the elderly person to make the turn. This allows elderly individuals with visual or hearing impairments to understand direction through vibration. Thus, the guidance unit can appropriately guide elderly individuals using voice, visual, and vibration methods, reducing the risk of them getting lost. Additionally, the guidance unit can update location information in real time and adjust the guidance content as needed according to the elderly person's movement. This allows elderly people to receive guidance based on the latest information at all times, enabling them to reach their destinations safely.

[0034] The analysis unit photographs the person in front of the elderly person, and the AI ​​analyzes the image to provide person information. The analysis unit can, for example, use a smartphone camera to photograph the person. The analysis unit can also use the AI ​​to analyze the captured image and provide person information. For example, the analysis unit inputs an image taken with a smartphone camera into the AI, and the AI ​​analyzes the image to display the person's name and relationship. The analysis unit can, for example, identify the person using facial recognition technology. The analysis unit can also provide person information using machine learning algorithms. For example, the analysis unit uses facial recognition technology to identify the person from the captured image and displays their name and relationship. The machine learning algorithm learns from past data and provides person information. This allows the analysis unit to provide person information even when the elderly person cannot recognize the person in front of them.

[0035] The guide unit provides voice directions and character introductions when needed. For example, it can provide voice directions to an elderly person who has gotten lost. It can also provide voice introductions to people in front of it if the user cannot recognize them. For example, if an elderly person says, "I want to go to XX," the guide unit will provide voice directions. The guide unit uses voice synthesis technology to provide directions. It can also respond to questions from elderly people using voice recognition technology. For example, the guide unit uses voice synthesis technology to say, "Turn right." Voice recognition technology recognizes the elderly person's question and provides an appropriate response. As a result, the guide unit can provide voice directions even when visual information cannot be confirmed.

[0036] The data collection unit can collect location information when an elderly person leaves a designated area. For example, the unit acquires GPS data when an elderly person leaves a set area. The data collection unit can also detect when an elderly person has left an area using Wi-Fi location information. For example, when the data collection unit detects that an elderly person has left an area, it collects location information in real time. The data collection unit can use geofencing technology to detect when an elderly person has left an area. Geofencing technology is a technology that sets a specific area and detects movement inside and outside that area. This allows the data collection unit to respond quickly by collecting location information when an elderly person leaves a designated area.

[0037] The analysis unit can analyze images taken with a smartphone and provide information about people. For example, the analysis unit inputs an image taken with a smartphone camera into an AI, which analyzes the image and displays the names and relationships of the people. The analysis unit can identify people from images using facial recognition technology, for example. The analysis unit can also provide information about people using machine learning algorithms. For example, the analysis unit identifies people from images taken with facial recognition technology and displays their names and relationships. The machine learning algorithm learns from past data and provides information about people. In this way, the analysis unit can provide information about people by analyzing images taken with a smartphone.

[0038] The guide unit can provide voice guidance even when visual information cannot be confirmed. For example, the guide unit can provide voice directions to elderly people who cannot see visual information. The guide unit can also provide voice introductions to people in front of them if they cannot recognize them. For example, if an elderly person says, "I want to go to XX," the guide unit will provide voice directions. The guide unit provides directions using, for example, speech synthesis technology. The guide unit can also respond to questions from elderly people using speech recognition technology. For example, the guide unit uses speech synthesis technology to say, "Turn right." Speech recognition technology recognizes the elderly person's question and provides an appropriate response. As a result, the guide unit can provide voice guidance even when visual information cannot be confirmed.

[0039] The data collection unit can analyze the elderly person's past travel history and select the optimal collection timing. For example, the data collection unit can set the collection timing based on places the elderly person has frequently visited in the past. The data collection unit can also analyze the elderly person's past travel patterns and select collection timing to avoid congestion. For example, the data collection unit can increase the collection frequency during specific time periods based on the elderly person's past travel history. In this way, the data collection unit can select the optimal collection timing by analyzing the elderly person's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past travel history data into a generating AI and have the generating AI select the optimal collection timing.

[0040] The data collection unit can filter location information based on the elderly person's current activity level. For example, the unit collects location information more frequently when the elderly person is walking. Conversely, the unit can reduce the frequency of location information collection when the elderly person is resting. For example, if the elderly person is in a vehicle, the unit adjusts the collection frequency according to the speed of travel. This allows the unit to collect more accurate location information by filtering it based on the elderly person's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's activity level data into a generating AI and have the generating AI perform location information filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the elderly person when collecting location information. For example, if the elderly person is in an urban area, the data collection unit can prioritize the collection of information on nearby landmarks. Similarly, if the elderly person is in a suburban area, the data collection unit can prioritize the collection of information on major roads. For example, if the elderly person is in a tourist area, the data collection unit can prioritize the collection of location information on tourist spots. This allows the data collection unit to provide more useful location information by prioritizing the collection of highly relevant information by considering the geographical location of the elderly person. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0042] The data collection unit can analyze the social media activities of elderly individuals and collect relevant information when collecting location information. For example, the data collection unit can collect location information of places where elderly individuals have checked in on social media. The data collection unit can also collect location information of photos that elderly individuals have shared on social media. For example, the data collection unit can collect location information of places where elderly individuals have been tagged on social media. In this way, the data collection unit can collect relevant location information by analyzing the social media activities of elderly individuals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the social media activity data of elderly individuals into a generating AI and have the generating AI perform the collection of relevant information.

[0043] The notification unit can adjust the level of detail of a notification based on the importance of the location information. For example, the notification unit will provide a detailed notification for important location information. It can also provide a concise notification for general location information. For example, in the case of emergency location information, the notification unit will provide a notification that can be quickly understood. In this way, the notification unit can appropriately notify more important information by adjusting the level of detail of the notification based on the importance of the location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0044] The notification unit can apply different notification algorithms depending on the category of location information when sending a notification. For example, if an elderly person is in an urban area, the notification unit can send a notification that includes information on nearby landmarks. If the elderly person is in a suburban area, the notification unit can also send a notification that includes information on major roads. For example, if the elderly person is in a tourist area, the notification unit can send a notification that includes information on tourist spots. In this way, the notification unit can provide more appropriate notifications by applying different notification algorithms depending on the category of location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location information category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0045] The notification unit can determine the priority of notifications based on when location information was acquired. For example, the notification unit prioritizes notifying the most recent location information. The notification unit can also determine the priority of notifications by referring to past location information. For example, the notification unit prioritizes notifying emergency location information. In this way, the notification unit can appropriately notify more important information by determining the priority of notifications based on when location information was acquired. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location information acquisition time data into a generating AI and have the generating AI perform the determination of notification priorities.

[0046] The notification unit can adjust the order of notifications based on the relevance of location information when sending notifications. For example, the notification unit prioritizes notifying elderly people of the information most relevant to their current location. The notification unit can also adjust the order of notifications based on the elderly person's past travel history. For example, the notification unit can adjust the order of notifications based on the elderly person's schedule. In this way, the notification unit can appropriately notify more important information by adjusting the order of notifications based on the relevance of location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location relevance data into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0047] The verification unit can improve the accuracy of verification by considering the interrelationships of location information during verification. For example, the verification unit can improve the accuracy of verification by comparing the elderly person's current location with their past travel history. The verification unit can also improve the accuracy of verification by comparing the elderly person's current location with their schedule. For example, the verification unit can improve the accuracy of verification by comparing the elderly person's current location with surrounding landmark information. In this way, the verification unit can improve the accuracy of verification by considering the interrelationships of location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input interrelationship data of location information into a generating AI and have the generating AI perform the improvement of verification accuracy.

[0048] The verification unit can perform verification while considering the attribute information of the location information provider. For example, the verification unit can prioritize verification of location information provided by the elderly person's family. It can also prioritize verification of location information provided by the elderly person's caregiver. For example, the verification unit can prioritize verification of location information provided by the elderly person themselves. This allows the verification unit to perform more appropriate verification by considering the attribute information of the location information provider. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the provider's attribute information data into a generating AI and have the generating AI perform the verification.

[0049] The verification unit can perform verification while considering the geographical distribution of location information. For example, the verification unit can perform verification by comparing the elderly person's current location with surrounding geographical information. The verification unit can also perform verification by comparing the elderly person's past travel history with its geographical distribution. For example, the verification unit can perform verification by comparing the elderly person's schedule with its geographical distribution. In this way, the verification unit can perform more appropriate verification by considering the geographical distribution of location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input geographical distribution data into a generating AI and have the generating AI perform the verification.

[0050] The verification unit can improve the accuracy of the verification by referring to relevant literature on location information during the verification process. For example, the verification unit can improve the accuracy of the verification by referring to relevant literature on the elderly person's current location. The verification unit can also improve the accuracy of the verification by referring to relevant literature on the elderly person's past travel history. For example, the verification unit can improve the accuracy of the verification by referring to relevant literature on the elderly person's schedule. In this way, the verification unit can improve the accuracy of the verification by referring to relevant literature on location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the verification accuracy.

[0051] The guidance unit can optimize the current guidance by referring to past guidance data during guidance. For example, the guidance unit can suggest the optimal guidance method based on guidance data previously used by elderly people. The guidance unit can also suggest guidance methods that avoid congestion based on the elderly person's past guidance history. For example, the guidance unit can analyze the elderly person's past guidance history and suggest the most efficient guidance method. In this way, the guidance unit can optimize the current guidance by referring to past guidance data. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past guidance data into a generating AI and have the generating AI perform the optimization of the current guidance.

[0052] The guidance unit can apply different guidance methods depending on the category of location information during guidance. For example, if an elderly person is in an urban area, the guidance unit can provide guidance that includes information on nearby landmarks. If an elderly person is in a suburban area, the guidance unit can also provide guidance that includes information on major roads. For example, if an elderly person is in a tourist area, the guidance unit can provide guidance that includes information on tourist spots. In this way, the guidance unit can provide more appropriate guidance by applying different guidance methods depending on the category of location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input location information category data into a generating AI and have the generating AI execute the application of guidance methods.

[0053] The guidance unit can analyze changes in guidance based on the timing of location information acquisition during guidance. For example, the guidance unit can analyze changes in guidance based on the most recent location information. The guidance unit can also analyze changes in guidance by referring to past location information. For example, the guidance unit can quickly analyze changes in guidance based on emergency location information. As a result, the guidance unit can provide more appropriate guidance by analyzing changes in guidance based on the timing of location information acquisition. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input location information acquisition timing data into a generating AI and have the generating AI perform the analysis of changes in guidance.

[0054] The guidance unit can analyze guidance by referring to relevant market data related to location information during guidance. For example, the guidance unit can analyze guidance by referring to relevant market data regarding the current location of an elderly person. The guidance unit can also analyze guidance by referring to relevant market data regarding the elderly person's past travel history. For example, the guidance unit can analyze guidance by referring to relevant market data regarding the elderly person's schedule. This allows the guidance unit to provide more appropriate guidance by referring to relevant market data related to location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input relevant market data into a generating AI and have the generating AI perform the guidance analysis.

[0055] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of person information during the analysis process. For example, the analysis unit can analyze the interrelationships of person information based on information about the elderly person's family and friends. The analysis unit can also analyze the interrelationships of person information based on the elderly person's past interaction history. For example, the analysis unit can analyze the interrelationships of person information based on information from the elderly person's social media. By doing so, the analysis unit can improve the accuracy of its analysis by considering the interrelationships of person information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of person information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0056] The analysis unit can perform analysis while considering the attribute information of the provider of the personal information. For example, the analysis unit may prioritize the analysis of personal information provided by the family of an elderly person. It can also prioritize the analysis of personal information provided by the caregiver of an elderly person. For example, the analysis unit may prioritize the analysis of personal information provided by the elderly person themselves. This allows the analysis unit to perform more appropriate analysis by considering the attribute information of the provider of the personal information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the provider's attribute information data into a generating AI and have the generating AI perform the analysis.

[0057] The analysis unit can perform analysis while considering the geographical distribution of person information. For example, the analysis unit can perform analysis by comparing the current location of an elderly person with information about people in the surrounding area. The analysis unit can also perform analysis by comparing the past travel history of an elderly person with its geographical distribution. For example, the analysis unit can perform analysis by comparing the elderly person's plans with its geographical distribution. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of person information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0058] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the person's information during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the elderly person's current location. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the elderly person's past travel history. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the elderly person's schedule. Thus, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the person's information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0059] The guiding unit can select the optimal guiding method by referring to past guiding data during guiding. For example, the guiding unit can propose the optimal guiding method based on guiding data previously used by elderly people. The guiding unit can also propose a guiding method that avoids congestion based on the elderly person's past guiding history. For example, the guiding unit can analyze the elderly person's past guiding history and propose the most efficient guiding method. In this way, the guiding unit can select the optimal guiding method by referring to past guiding data. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input past guiding data into a generating AI and have the generating AI perform the selection of the optimal guiding method.

[0060] The guiding unit can apply different guiding methods depending on the category of location information during guiding. For example, if an elderly person is in an urban area, the guiding unit can provide guidance that includes information on nearby landmarks. If an elderly person is in a suburban area, the guiding unit can also provide guidance that includes information on major roads. For example, if an elderly person is in a tourist area, the guiding unit can provide guidance that includes information on tourist spots. In this way, the guiding unit can provide more appropriate guidance by applying different guiding methods depending on the category of location information. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input location information category data into a generating AI and have the generating AI execute the application of guiding methods.

[0061] The guiding unit can analyze changes in the guide based on the timing of location information acquisition during guiding. For example, the guiding unit can analyze changes in the guide based on the most recent location information. The guiding unit can also analyze changes in the guide by referring to past location information. For example, the guiding unit can quickly analyze changes in the guide based on emergency location information. As a result, the guiding unit can provide more appropriate guidance by analyzing changes in the guide based on the timing of location information acquisition. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input location information acquisition timing data into a generating AI and have the generating AI perform the analysis of changes in the guide.

[0062] The guiding unit can analyze the guidance by referring to relevant market data on location information during the guidance process. For example, the guiding unit can analyze the guidance by referring to relevant market data on the current location of an elderly person. The guiding unit can also analyze the guidance by referring to relevant market data on the elderly person's past travel history. For example, the guiding unit can analyze the guidance by referring to relevant market data on the elderly person's schedule. This allows the guiding unit to provide more appropriate guidance by referring to relevant market data on location information. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input relevant market data into a generating AI and have the generating AI perform the guide analysis.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit monitors the health status of elderly individuals and can collect location information if an abnormality is detected. For example, the unit can collect location information if it detects abnormalities in heart rate or blood pressure. It can also collect location information if it detects a fall. Furthermore, it can collect location information if it detects an abnormality in body temperature. This allows the unit to collect location information based on the health status of elderly individuals, enabling a rapid response.

[0065] The analysis unit can analyze the behavioral patterns of elderly individuals and send notifications if it detects abnormal behavior. For example, it can send notifications if an elderly person goes out at an unusual time of day. It can also send notifications if an elderly person takes an unusual route. Furthermore, it can send notifications if an elderly person stays in an unusual location for an extended period of time. This allows the analysis unit to detect abnormalities based on the elderly person's behavioral patterns and respond quickly.

[0066] The guide service can provide guidance based on the hobbies and interests of elderly individuals. For example, the guide service can take them to parks or cafes that they enjoy. They can also guide them to events and exhibitions that interest them. Furthermore, they can guide them to workshops and courses that they would like to participate in. In this way, the guide service can support more fulfilling outings for elderly individuals by providing guidance based on their hobbies and interests.

[0067] The sound collection unit can collect ambient sounds around elderly individuals and send notifications if it detects an anomaly. For example, the unit will send a notification if the ambient noise level rises sharply. It can also send a notification if it detects a cry for help from the surrounding sounds. Furthermore, it can send a notification if it detects an unusual sound from the ambient sounds. This allows the unit to detect anomalies based on the ambient sounds around elderly individuals and enable a rapid response.

[0068] The analysis unit can analyze the dietary content of elderly individuals and evaluate their nutritional balance. For example, it can analyze photographs of meals to assess nutrient intake. It can also evaluate nutritional balance based on inputted meal content. Furthermore, it can evaluate nutritional balance based on past meal history. This allows the analysis unit to evaluate nutritional balance based on the dietary content of elderly individuals and support their health management.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The collection unit collects location information of the elderly person. The collection unit identifies the elderly person's current location using, for example, GPS data, Wi-Fi location information, and Bluetooth beacons. GPS data allows the elderly person to know their location in real time, Wi-Fi location information identifies the elderly person's location based on the location of the Wi-Fi access point, and Bluetooth beacons identify the elderly person's location based on the strength of the beacon signal. Step 2: The notification unit notifies family members or caregivers of the location information collected by the collection unit. The notification unit notifies location information using, for example, SMS, email, or a dedicated app. SMS sends the elderly person's current location to family members, email notifications send an email containing location information to family members, and a dedicated app displays location information in real time and notifies family members or caregivers. Step 3: The verification unit confirms the elderly person's location based on the location information notified by the notification unit. The verification unit confirms the elderly person's location using, for example, a map application, checks the elderly person's movement history based on the location information, and verifies the accuracy of the location information. The map application displays the elderly person's current location, the movement history displays past location information in chronological order, and the accuracy of the location information verifies the accuracy of the GPS data. Step 4: The guidance unit guides the elderly person based on the location confirmed by the verification unit. The guidance unit guides the elderly person using, for example, voice guidance, visual guidance, and vibration guidance. Voice guidance will say, "Turn right," visual guidance will show the route on a map, and vibration guidance will indicate the direction by vibrating the smartphone.

[0071] (Example of form 2) The Safe Navigator System according to an embodiment of the present invention is a system for providing an environment in which elderly people can go out with peace of mind. This Safe Navigator System has the following three functions: The Safe Navigator System has a function to notify family members or caregivers using the location information of a smartphone if an elderly person gets lost. For example, if an elderly person leaves a specific area, the smartphone automatically sends location information to family members or caregivers to inform them of their current location. This function allows family members or caregivers to check the elderly person's whereabouts in real time and respond quickly. The Safe Navigator System has a function to take a picture of a person with a smartphone if the elderly person cannot recognize the person in front of them, and the AI ​​analyzes the image to provide person information. For example, if an elderly person has forgotten the face of an acquaintance, they can take a picture of that person with a smartphone, and the AI ​​will display the person's name and relationship. This function allows elderly people to interact with others with peace of mind. The Safe Navigator System has a function to provide voice directions and person introductions when needed. Even if visual information cannot be confirmed, voice guidance can be received. For example, if an elderly person gets lost, they can say "I want to go to XX" to their smartphone, and the AI ​​will provide voice directions. Furthermore, if the system cannot recognize the person in front of it, it provides information about that person via voice. This function allows elderly people to act with confidence even without visual information. These functions provide an environment where elderly people can go out with peace of mind, and families and caregivers can also check on the safety of elderly people in real time, allowing them to watch over them with peace of mind. In addition, advances in AI technology are enabling more accurate facial recognition and location information services, so it is expected to play an important role in the aging society of the future. In this way, the Safe Navigator System can provide an environment where elderly people can go out with peace of mind.

[0072] The safety navigator system according to the embodiment comprises a collection unit, a notification unit, a confirmation unit, and a guidance unit. The collection unit collects location information of the elderly person. The collection unit identifies the elderly person's current location using, for example, GPS data. The collection unit can also identify the elderly person's location using Wi-Fi location information. Furthermore, the collection unit can also identify the elderly person's location using Bluetooth beacons. For example, the collection unit acquires GPS data and grasps the elderly person's location in real time. Wi-Fi location information identifies the elderly person's location based on the location of the Wi-Fi access point. Bluetooth beacons identify the elderly person's location based on the strength of the beacon signal. The notification unit notifies family members or caregivers of the location information collected by the collection unit. The notification unit notifies location information using, for example, SMS. Furthermore, the notification unit can also notify location information using email. Furthermore, the notification unit can also notify location information using a dedicated app. For example, the notification unit sends the elderly person's current location to family members via SMS. Email notification sends an email containing location information to family members. The dedicated app displays location information in real time and notifies family members or caregivers. The confirmation unit verifies the elderly person's location based on the location information notified by the notification unit. The confirmation unit verifies the elderly person's location using, for example, a map application. The confirmation unit can also verify the elderly person's movement history based on the location information. Furthermore, the confirmation unit can verify the accuracy of the location information. For example, the confirmation unit displays the elderly person's current location using a map application. Movement history displays past location information in chronological order. Location information accuracy is verified by checking the accuracy of GPS data. The guidance unit guides the elderly person based on the location verified by the confirmation unit. The guidance unit guides the elderly person using, for example, voice guidance. The guidance unit can also guide the elderly person using visual guidance. Furthermore, the guidance unit can also guide the elderly person using vibration guidance. For example, the guidance unit gives voice instructions such as "Turn right." Visual guidance displays the route on a map. Vibration guidance indicates direction by vibrating the smartphone.As a result, the safety navigator system according to this embodiment can collect location information of the elderly person and notify family members or caregivers, thereby confirming the elderly person's whereabouts and providing guidance.

[0073] The data collection unit collects location information of elderly individuals. For example, the unit uses GPS data to determine the elderly person's current location. Specifically, a GPS module built into the elderly person's device receives signals from satellites, analyzes those signals, and obtains latitude and longitude information. This information is transmitted in real time to a central server and stored in a database. The data collection unit can also determine the elderly person's location using Wi-Fi location information. Wi-Fi location information is a technology that measures the signal strength of surrounding Wi-Fi access points and estimates the location based on that information. For example, by measuring the signal strength of multiple Wi-Fi access points near where the elderly person is located and comparing that data with the location information of known access points, the unit can determine the elderly person's location. Furthermore, the data collection unit can also determine the elderly person's location using Bluetooth beacons. A Bluetooth beacon is a device that emits a signal within a specific range, and its location is estimated based on the strength of that signal. For example, when an elderly person passes near a Bluetooth beacon, the signal strength of that beacon is recorded on the elderly person's device, and their location is determined based on that information. This allows the data collection unit to accurately pinpoint the location of elderly individuals by combining GPS, Wi-Fi, and Bluetooth beacon technologies.

[0074] The notification unit notifies family members and caregivers of location information collected by the collection unit. The notification unit notifies location information, for example, using SMS. Specifically, when the elderly person's location information is collected, the notification unit sends this information as an SMS message to the family member's or caregiver's mobile phone. The SMS message includes the elderly person's current latitude and longitude information and a map link. The notification unit can also notify location information via email. In email notifications, an email containing the location information is sent to the family member's or caregiver's email address. The email may include detailed location information, past movement history, and additional information about the current situation. Furthermore, the notification unit can also notify location information using a dedicated app. The dedicated app is installed on smartphones and tablets and has the function of displaying the elderly person's location information in real time. The app displays the elderly person's current location on a map, allowing family members and caregivers to intuitively understand the elderly person's location. The app also has a notification function that can issue alerts when the elderly person enters or leaves a specific area. This allows the notification unit to quickly and reliably notify family members and caregivers of the elderly person's location information using SMS, email, and the dedicated app.

[0075] The confirmation unit verifies the elderly person's location based on the location information notified by the notification unit. For example, the confirmation unit uses a map application to verify the elderly person's location. Specifically, it inputs the notified location information into the map application and displays the elderly person's current location on the map. The map application provides detailed map information, allowing the user to check the elderly person's surroundings and nearby facilities. The confirmation unit can also check the elderly person's movement history based on the location information. The movement history displays past location information in chronological order, allowing the user to understand the routes the elderly person has taken. This allows the user to check the elderly person's behavior patterns and whether they have stayed in a particular location for an extended period. Furthermore, the confirmation unit can verify the accuracy of the location information. The accuracy of the location information is evaluated based on the accuracy of GPS data, the signal strength of Wi-Fi location information, and the signal strength of Bluetooth beacons. For example, if the accuracy of GPS data is low, Wi-Fi location information and Bluetooth beacon data can be used supplementarily to improve the accuracy of the location information. This allows the confirmation unit to accurately grasp the elderly person's location and respond quickly as needed.

[0076] The guidance unit directs elderly individuals based on their location, which has been confirmed by the verification unit. For example, the guidance unit can use voice guidance. Specifically, it can issue voice instructions such as "Turn right" or "Go straight" from the elderly person's device, ensuring they reach their destination without getting lost. The guidance unit can also use visual guidance. Visual guidance displays a map on the device screen, visually showing the route from the current location to the destination. This allows elderly individuals to confirm their direction of travel by looking at the map. Furthermore, the guidance unit can also use vibration guidance. Vibration guidance uses the device to indicate direction. For example, the device vibrates on the right side when it's time to turn right, prompting the elderly person to make the turn. This allows elderly individuals with visual or hearing impairments to understand direction through vibration. Thus, the guidance unit can appropriately guide elderly individuals using voice, visual, and vibration methods, reducing the risk of them getting lost. Additionally, the guidance unit can update location information in real time and adjust the guidance content as needed according to the elderly person's movement. This allows elderly people to receive guidance based on the latest information at all times, enabling them to reach their destinations safely.

[0077] The analysis unit photographs the person in front of the elderly person, and the AI ​​analyzes the image to provide person information. The analysis unit can, for example, use a smartphone camera to photograph the person. The analysis unit can also use the AI ​​to analyze the captured image and provide person information. For example, the analysis unit inputs an image taken with a smartphone camera into the AI, and the AI ​​analyzes the image to display the person's name and relationship. The analysis unit can, for example, identify the person using facial recognition technology. The analysis unit can also provide person information using machine learning algorithms. For example, the analysis unit uses facial recognition technology to identify the person from the captured image and displays their name and relationship. The machine learning algorithm learns from past data and provides person information. This allows the analysis unit to provide person information even when the elderly person cannot recognize the person in front of them.

[0078] The guide unit provides voice directions and character introductions when needed. For example, it can provide voice directions to an elderly person who has gotten lost. It can also provide voice introductions to people in front of it if the user cannot recognize them. For example, if an elderly person says, "I want to go to XX," the guide unit will provide voice directions. The guide unit uses voice synthesis technology to provide directions. It can also respond to questions from elderly people using voice recognition technology. For example, the guide unit uses voice synthesis technology to say, "Turn right." Voice recognition technology recognizes the elderly person's question and provides an appropriate response. As a result, the guide unit can provide voice directions even when visual information cannot be confirmed.

[0079] The data collection unit can collect location information when an elderly person leaves a designated area. For example, the unit acquires GPS data when an elderly person leaves a set area. The data collection unit can also detect when an elderly person has left an area using Wi-Fi location information. For example, when the data collection unit detects that an elderly person has left an area, it collects location information in real time. The data collection unit can use geofencing technology to detect when an elderly person has left an area. Geofencing technology is a technology that sets a specific area and detects movement inside and outside that area. This allows the data collection unit to respond quickly by collecting location information when an elderly person leaves a designated area.

[0080] The analysis unit can analyze images taken with a smartphone and provide information about people. For example, the analysis unit inputs an image taken with a smartphone camera into an AI, which analyzes the image and displays the names and relationships of the people. The analysis unit can identify people from images using facial recognition technology, for example. The analysis unit can also provide information about people using machine learning algorithms. For example, the analysis unit identifies people from images taken with facial recognition technology and displays their names and relationships. The machine learning algorithm learns from past data and provides information about people. In this way, the analysis unit can provide information about people by analyzing images taken with a smartphone.

[0081] The guide unit can provide voice guidance even when visual information cannot be confirmed. For example, the guide unit can provide voice directions to elderly people who cannot see visual information. The guide unit can also provide voice introductions to people in front of them if they cannot recognize them. For example, if an elderly person says, "I want to go to XX," the guide unit will provide voice directions. The guide unit provides directions using, for example, speech synthesis technology. The guide unit can also respond to questions from elderly people using speech recognition technology. For example, the guide unit uses speech synthesis technology to say, "Turn right." Speech recognition technology recognizes the elderly person's question and provides an appropriate response. As a result, the guide unit can provide voice guidance even when visual information cannot be confirmed.

[0082] The data collection unit can estimate the emotions of elderly individuals and adjust the frequency of location data collection based on the estimated emotions. For example, if an elderly person is feeling anxious, the data collection unit can increase the collection frequency to collect location data in real time. Conversely, if an elderly person is relaxed, the data collection unit can decrease the collection frequency to conserve battery power. For example, if an elderly person is in a hurry, the data collection unit can set the collection frequency to a moderate level and collect location data at appropriate intervals. This allows the data collection unit to collect more appropriate location data by adjusting the collection frequency based on the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the emotional data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0083] The data collection unit can analyze the elderly person's past travel history and select the optimal collection timing. For example, the data collection unit can set the collection timing based on places the elderly person has frequently visited in the past. The data collection unit can also analyze the elderly person's past travel patterns and select collection timing to avoid congestion. For example, the data collection unit can increase the collection frequency during specific time periods based on the elderly person's past travel history. In this way, the data collection unit can select the optimal collection timing by analyzing the elderly person's past travel history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's past travel history data into a generating AI and have the generating AI select the optimal collection timing.

[0084] The data collection unit can filter location information based on the elderly person's current activity level. For example, the unit collects location information more frequently when the elderly person is walking. Conversely, the unit can reduce the frequency of location information collection when the elderly person is resting. For example, if the elderly person is in a vehicle, the unit adjusts the collection frequency according to the speed of travel. This allows the unit to collect more accurate location information by filtering it based on the elderly person's current activity level. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's activity level data into a generating AI and have the generating AI perform location information filtering.

[0085] The data collection unit can estimate the emotions of elderly individuals and determine the priority of location information to collect based on the estimated emotions. For example, if an elderly individual is feeling anxious, the data collection unit will prioritize collecting detailed location information of their current location. Conversely, if an elderly individual is relaxed, the data collection unit can also collect broader location information. For example, if an elderly individual is in a hurry, the data collection unit will prioritize collecting location information of their main travel route. This allows the data collection unit to prioritize the collection of more important location information by determining the priority of location information to collect based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the emotional data of elderly individuals into a generative AI and have the generative AI determine the priority of location information.

[0086] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the elderly person when collecting location information. For example, if the elderly person is in an urban area, the data collection unit can prioritize the collection of information on nearby landmarks. Similarly, if the elderly person is in a suburban area, the data collection unit can prioritize the collection of information on major roads. For example, if the elderly person is in a tourist area, the data collection unit can prioritize the collection of location information on tourist spots. This allows the data collection unit to provide more useful location information by prioritizing the collection of highly relevant information by considering the geographical location of the elderly person. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0087] The data collection unit can analyze the social media activities of elderly individuals and collect relevant information when collecting location information. For example, the data collection unit can collect location information of places where elderly individuals have checked in on social media. The data collection unit can also collect location information of photos that elderly individuals have shared on social media. For example, the data collection unit can collect location information of places where elderly individuals have been tagged on social media. In this way, the data collection unit can collect relevant location information by analyzing the social media activities of elderly individuals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the social media activity data of elderly individuals into a generating AI and have the generating AI perform the collection of relevant information.

[0088] The notification unit can estimate the emotions of elderly individuals and adjust the way notifications are expressed based on the estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit will use a reassuring expression. Conversely, if an elderly individual is relaxed, the notification unit can use a concise expression. For example, if an elderly individual is in a hurry, the notification unit will use an expression that can be quickly understood. This allows the notification unit to provide more appropriate notifications by adjusting the expression based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the way notifications are expressed.

[0089] The notification unit can adjust the level of detail of a notification based on the importance of the location information. For example, the notification unit will provide a detailed notification for important location information. It can also provide a concise notification for general location information. For example, in the case of emergency location information, the notification unit will provide a notification that can be quickly understood. In this way, the notification unit can appropriately notify more important information by adjusting the level of detail of the notification based on the importance of the location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0090] The notification unit can apply different notification algorithms depending on the category of location information when sending a notification. For example, if an elderly person is in an urban area, the notification unit can send a notification that includes information on nearby landmarks. If the elderly person is in a suburban area, the notification unit can also send a notification that includes information on major roads. For example, if the elderly person is in a tourist area, the notification unit can send a notification that includes information on tourist spots. In this way, the notification unit can provide more appropriate notifications by applying different notification algorithms depending on the category of location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location information category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0091] The notification unit can estimate the emotions of elderly individuals and adjust the length of the notification based on the estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit will provide a detailed notification. Conversely, if an elderly individual is relaxed, the notification unit can provide a concise notification. For example, if an elderly individual is in a hurry, the notification unit will provide a short notification that can be quickly understood. This allows the notification unit to provide more appropriate notifications by adjusting the length of the notification based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the length of the notification.

[0092] The notification unit can determine the priority of notifications based on when location information was acquired. For example, the notification unit prioritizes notifying the most recent location information. The notification unit can also determine the priority of notifications by referring to past location information. For example, the notification unit prioritizes notifying emergency location information. In this way, the notification unit can appropriately notify more important information by determining the priority of notifications based on when location information was acquired. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location information acquisition time data into a generating AI and have the generating AI perform the determination of notification priorities.

[0093] The notification unit can adjust the order of notifications based on the relevance of location information when sending notifications. For example, the notification unit prioritizes notifying elderly people of the information most relevant to their current location. The notification unit can also adjust the order of notifications based on the elderly person's past travel history. For example, the notification unit can adjust the order of notifications based on the elderly person's schedule. In this way, the notification unit can appropriately notify more important information by adjusting the order of notifications based on the relevance of location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input location relevance data into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0094] The verification unit can estimate the emotions of the elderly person and adjust the verification criteria based on the estimated emotions. For example, if the elderly person is feeling anxious, the verification unit will perform a detailed verification. It can also perform a brief verification if the elderly person is relaxed. For example, if the elderly person is in a hurry, the verification unit will perform a quick verification. This allows the verification unit to perform a more appropriate verification by adjusting the verification criteria based on the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the verification unit may be performed using AI, or not. For example, the verification unit can input the elderly person's emotion data into the generative AI and have the generative AI adjust the verification criteria.

[0095] The verification unit can improve the accuracy of verification by considering the interrelationships of location information during verification. For example, the verification unit can improve the accuracy of verification by comparing the elderly person's current location with their past travel history. The verification unit can also improve the accuracy of verification by comparing the elderly person's current location with their schedule. For example, the verification unit can improve the accuracy of verification by comparing the elderly person's current location with surrounding landmark information. In this way, the verification unit can improve the accuracy of verification by considering the interrelationships of location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input interrelationship data of location information into a generating AI and have the generating AI perform the improvement of verification accuracy.

[0096] The verification unit can perform verification while considering the attribute information of the location information provider. For example, the verification unit can prioritize verification of location information provided by the elderly person's family. It can also prioritize verification of location information provided by the elderly person's caregiver. For example, the verification unit can prioritize verification of location information provided by the elderly person themselves. This allows the verification unit to perform more appropriate verification by considering the attribute information of the location information provider. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the provider's attribute information data into a generating AI and have the generating AI perform the verification.

[0097] The verification unit can estimate the emotions of elderly individuals and adjust the order in which the verification results are displayed based on the estimated emotions. For example, if an elderly individual is feeling anxious, the verification unit may prioritize displaying important information. It may also prioritize displaying concise information if the elderly individual is relaxed. For example, if an elderly individual is in a hurry, the verification unit may prioritize displaying information that can be quickly understood. This allows the verification unit to provide more appropriate information by adjusting the order in which the verification results are displayed based on the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the verification unit may be performed using AI, or not. For example, the verification unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the display order of the verification results.

[0098] The verification unit can perform verification while considering the geographical distribution of location information. For example, the verification unit can perform verification by comparing the elderly person's current location with surrounding geographical information. The verification unit can also perform verification by comparing the elderly person's past travel history with its geographical distribution. For example, the verification unit can perform verification by comparing the elderly person's schedule with its geographical distribution. In this way, the verification unit can perform more appropriate verification by considering the geographical distribution of location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input geographical distribution data into a generating AI and have the generating AI perform the verification.

[0099] The verification unit can improve the accuracy of the verification by referring to relevant literature on location information during the verification process. For example, the verification unit can improve the accuracy of the verification by referring to relevant literature on the elderly person's current location. The verification unit can also improve the accuracy of the verification by referring to relevant literature on the elderly person's past travel history. For example, the verification unit can improve the accuracy of the verification by referring to relevant literature on the elderly person's schedule. In this way, the verification unit can improve the accuracy of the verification by referring to relevant literature on location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the verification accuracy.

[0100] The guidance unit can estimate the emotions of elderly people and adjust the way guidance is displayed based on the estimated emotions. For example, if an elderly person is feeling anxious, the guidance unit will provide guidance in a way that provides reassurance. Conversely, if an elderly person is relaxed, the guidance unit can provide guidance in a concise way. For example, if an elderly person is in a hurry, the guidance unit will provide guidance in a way that can be quickly understood. In this way, the guidance unit can provide more appropriate guidance by adjusting the way guidance is displayed based on the emotions of elderly people. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, or not. For example, the guidance unit can input elderly people's emotion data into a generative AI and have the generative AI adjust the way guidance is displayed.

[0101] The guidance unit can optimize the current guidance by referring to past guidance data during guidance. For example, the guidance unit can suggest the optimal guidance method based on guidance data previously used by elderly people. The guidance unit can also suggest guidance methods that avoid congestion based on the elderly person's past guidance history. For example, the guidance unit can analyze the elderly person's past guidance history and suggest the most efficient guidance method. In this way, the guidance unit can optimize the current guidance by referring to past guidance data. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past guidance data into a generating AI and have the generating AI perform the optimization of the current guidance.

[0102] The guidance unit can apply different guidance methods depending on the category of location information during guidance. For example, if an elderly person is in an urban area, the guidance unit can provide guidance that includes information on nearby landmarks. If an elderly person is in a suburban area, the guidance unit can also provide guidance that includes information on major roads. For example, if an elderly person is in a tourist area, the guidance unit can provide guidance that includes information on tourist spots. In this way, the guidance unit can provide more appropriate guidance by applying different guidance methods depending on the category of location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input location information category data into a generating AI and have the generating AI execute the application of guidance methods.

[0103] The guidance unit can estimate the emotions of elderly individuals and adjust the importance of the guidance based on the estimated emotions. For example, if an elderly person is feeling anxious, the guidance unit will prioritize providing important information. Conversely, if an elderly person is relaxed, the guidance unit can prioritize providing concise information. For example, if an elderly person is in a hurry, the guidance unit will prioritize providing information that can be quickly understood. This allows the guidance unit to provide more appropriate guidance by adjusting the importance of the guidance based on the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, or not. For example, the guidance unit can input elderly individuals' emotion data into a generative AI and have the generative AI adjust the importance of the guidance.

[0104] The guidance unit can analyze changes in guidance based on the timing of location information acquisition during guidance. For example, the guidance unit can analyze changes in guidance based on the most recent location information. The guidance unit can also analyze changes in guidance by referring to past location information. For example, the guidance unit can quickly analyze changes in guidance based on emergency location information. As a result, the guidance unit can provide more appropriate guidance by analyzing changes in guidance based on the timing of location information acquisition. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input location information acquisition timing data into a generating AI and have the generating AI perform the analysis of changes in guidance.

[0105] The guidance unit can analyze guidance by referring to relevant market data related to location information during guidance. For example, the guidance unit can analyze guidance by referring to relevant market data regarding the current location of an elderly person. The guidance unit can also analyze guidance by referring to relevant market data regarding the elderly person's past travel history. For example, the guidance unit can analyze guidance by referring to relevant market data regarding the elderly person's schedule. This allows the guidance unit to provide more appropriate guidance by referring to relevant market data related to location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input relevant market data into a generating AI and have the generating AI perform the guidance analysis.

[0106] The analysis unit can estimate the emotions of elderly individuals and determine the priority of analysis based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit will prioritize analyzing important information. Conversely, if an elderly individual is relaxed, the analysis unit can prioritize analyzing concise information. For example, if an elderly individual is in a hurry, the analysis unit will prioritize analyzing information that can be quickly understood. This allows the analysis unit to prioritize the analysis of more important information by determining the priority of analysis based on the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the elderly individual's emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0107] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of person information during the analysis process. For example, the analysis unit can analyze the interrelationships of person information based on information about the elderly person's family and friends. The analysis unit can also analyze the interrelationships of person information based on the elderly person's past interaction history. For example, the analysis unit can analyze the interrelationships of person information based on information from the elderly person's social media. By doing so, the analysis unit can improve the accuracy of its analysis by considering the interrelationships of person information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of person information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0108] The analysis unit can perform analysis while considering the attribute information of the provider of the personal information. For example, the analysis unit may prioritize the analysis of personal information provided by the family of an elderly person. It can also prioritize the analysis of personal information provided by the caregiver of an elderly person. For example, the analysis unit may prioritize the analysis of personal information provided by the elderly person themselves. This allows the analysis unit to perform more appropriate analysis by considering the attribute information of the provider of the personal information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the provider's attribute information data into a generating AI and have the generating AI perform the analysis.

[0109] The analysis unit can estimate the emotions of elderly individuals and adjust the display method of the analysis results based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit can provide the analysis results in a reassuring display method. If the elderly individual is relaxed, the analysis unit can also provide the results in a concise display method. For example, if an elderly individual is in a hurry, the analysis unit can provide the results in a display method that can be quickly understood. This allows the analysis unit to provide more appropriate information by adjusting the display method of the analysis results based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0110] The analysis unit can perform analysis while considering the geographical distribution of person information. For example, the analysis unit can perform analysis by comparing the current location of an elderly person with information about people in the surrounding area. The analysis unit can also perform analysis by comparing the past travel history of an elderly person with its geographical distribution. For example, the analysis unit can perform analysis by comparing the elderly person's plans with its geographical distribution. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of person information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0111] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the person's information during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the elderly person's current location. Furthermore, the analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the elderly person's past travel history. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the elderly person's schedule. Thus, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the person's information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0112] The guide unit can estimate the emotions of elderly individuals and adjust its guidance method based on the estimated emotions. For example, if an elderly individual is feeling anxious, the guide unit will provide guidance in a way that provides reassurance. Conversely, if an elderly individual is relaxed, the guide unit can provide guidance in a concise manner. For example, if an elderly individual is in a hurry, the guide unit will provide guidance in a way that is easy to understand quickly. This allows the guide unit to provide more appropriate guidance by adjusting its guidance method based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the guide unit may be performed using AI, or not. For example, the guide unit can input the elderly individual's emotion data into the generative AI and have the generative AI adjust the guidance method.

[0113] The guiding unit can select the optimal guiding method by referring to past guiding data during guiding. For example, the guiding unit can propose the optimal guiding method based on guiding data previously used by elderly people. The guiding unit can also propose a guiding method that avoids congestion based on the elderly person's past guiding history. For example, the guiding unit can analyze the elderly person's past guiding history and propose the most efficient guiding method. In this way, the guiding unit can select the optimal guiding method by referring to past guiding data. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input past guiding data into a generating AI and have the generating AI perform the selection of the optimal guiding method.

[0114] The guiding unit can apply different guiding methods depending on the category of location information during guiding. For example, if an elderly person is in an urban area, the guiding unit can provide guidance that includes information on nearby landmarks. If an elderly person is in a suburban area, the guiding unit can also provide guidance that includes information on major roads. For example, if an elderly person is in a tourist area, the guiding unit can provide guidance that includes information on tourist spots. In this way, the guiding unit can provide more appropriate guidance by applying different guiding methods depending on the category of location information. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input location information category data into a generating AI and have the generating AI execute the application of guiding methods.

[0115] The guide unit can estimate the emotions of elderly individuals and determine the priority of the guide based on the estimated emotions. For example, if an elderly individual is feeling anxious, the guide unit will prioritize guiding them with important information. Conversely, if an elderly individual is relaxed, the guide unit can prioritize guiding them with concise information. For example, if an elderly individual is in a hurry, the guide unit will prioritize guiding them with information that can be quickly understood. This allows the guide unit to prioritize more important information by determining the priority of the guide based on the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the guide unit may be performed using AI, or not. For example, the guide unit can input the elderly individual's emotion data into a generative AI and have the generative AI determine the priority of the guide.

[0116] The guiding unit can analyze changes in the guide based on the timing of location information acquisition during guiding. For example, the guiding unit can analyze changes in the guide based on the most recent location information. The guiding unit can also analyze changes in the guide by referring to past location information. For example, the guiding unit can quickly analyze changes in the guide based on emergency location information. As a result, the guiding unit can provide more appropriate guidance by analyzing changes in the guide based on the timing of location information acquisition. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input location information acquisition timing data into a generating AI and have the generating AI perform the analysis of changes in the guide.

[0117] The guiding unit can analyze the guidance by referring to relevant market data on location information during the guidance process. For example, the guiding unit can analyze the guidance by referring to relevant market data on the current location of an elderly person. The guiding unit can also analyze the guidance by referring to relevant market data on the elderly person's past travel history. For example, the guiding unit can analyze the guidance by referring to relevant market data on the elderly person's schedule. This allows the guiding unit to provide more appropriate guidance by referring to relevant market data on location information. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input relevant market data into a generating AI and have the generating AI perform the guide analysis.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The data collection unit monitors the health status of elderly individuals and can collect location information if an abnormality is detected. For example, the unit can collect location information if it detects abnormalities in heart rate or blood pressure. It can also collect location information if it detects a fall. Furthermore, it can collect location information if it detects an abnormality in body temperature. This allows the unit to collect location information based on the health status of elderly individuals, enabling a rapid response.

[0120] The analysis unit can analyze the behavioral patterns of elderly individuals and send notifications if it detects abnormal behavior. For example, it can send notifications if an elderly person goes out at an unusual time of day. It can also send notifications if an elderly person takes an unusual route. Furthermore, it can send notifications if an elderly person stays in an unusual location for an extended period of time. This allows the analysis unit to detect abnormalities based on the elderly person's behavioral patterns and respond quickly.

[0121] The guide service can provide guidance based on the hobbies and interests of elderly individuals. For example, the guide service can take them to parks or cafes that they enjoy. They can also guide them to events and exhibitions that interest them. Furthermore, they can guide them to workshops and courses that they would like to participate in. In this way, the guide service can support more fulfilling outings for elderly individuals by providing guidance based on their hobbies and interests.

[0122] The sound collection unit can collect ambient sounds around elderly individuals and send notifications if it detects an anomaly. For example, the unit will send a notification if the ambient noise level rises sharply. It can also send a notification if it detects a cry for help from the surrounding sounds. Furthermore, it can send a notification if it detects an unusual sound from the ambient sounds. This allows the unit to detect anomalies based on the ambient sounds around elderly individuals and enable a rapid response.

[0123] The analysis unit can analyze the dietary content of elderly individuals and evaluate their nutritional balance. For example, it can analyze photographs of meals to assess nutrient intake. It can also evaluate nutritional balance based on inputted meal content. Furthermore, it can evaluate nutritional balance based on past meal history. This allows the analysis unit to evaluate nutritional balance based on the dietary content of elderly individuals and support their health management.

[0124] The data collection unit can estimate the emotions of elderly individuals and adjust the timing of notifications based on those estimates. For example, if an elderly person is feeling anxious, the unit will send a notification quickly. It can also delay notifications if the elderly person is relaxed. Furthermore, it can refrain from sending notifications if the elderly person is concentrating. This allows the data collection unit to provide more appropriate notifications by adjusting the timing based on the elderly person's emotions.

[0125] The analysis unit can estimate the emotions of elderly individuals and adjust the display method of the analysis results based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit can provide the analysis results in a reassuring display method. Furthermore, if an elderly individual is relaxed, the analysis unit can provide the results in a concise display method. Additionally, if an elderly individual is in a hurry, the analysis unit can provide the results in a display method that can be quickly understood. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results based on the emotions of the elderly individual.

[0126] The guide unit can estimate the emotions of elderly individuals and adjust the content of the guide based on those estimates. For example, if an elderly person is feeling anxious, the guide unit will provide reassuring content. If the elderly person is relaxed, the guide unit can provide concise content. Furthermore, if the elderly person is in a hurry, the guide unit can provide content that can be quickly understood. In this way, the guide unit can provide more appropriate guidance by adjusting the content of the guide based on the emotions of the elderly person.

[0127] The notification unit can estimate the emotions of elderly individuals and adjust the content of the notification based on those emotions. For example, if an elderly person is feeling anxious, the notification unit will send a reassuring notification. If the elderly person is relaxed, the notification unit can also send a concise notification. Furthermore, if the elderly person is in a hurry, the notification unit can send a notification that can be quickly understood. In this way, the notification unit can provide more appropriate notifications by adjusting the content based on the emotions of elderly individuals.

[0128] The verification unit can estimate the emotions of the elderly person and adjust the verification method based on the estimated emotions. For example, if the elderly person is feeling anxious, the verification unit will perform a detailed verification. If the elderly person is relaxed, the verification unit can also perform a brief verification. Furthermore, if the elderly person is in a hurry, the verification unit can perform a quick verification. In this way, the verification unit can perform a more appropriate verification by adjusting the verification method based on the elderly person's emotions.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The collection unit collects location information of the elderly person. The collection unit identifies the elderly person's current location using, for example, GPS data, Wi-Fi location information, and Bluetooth beacons. GPS data allows the elderly person to know their location in real time, Wi-Fi location information identifies the elderly person's location based on the location of the Wi-Fi access point, and Bluetooth beacons identify the elderly person's location based on the strength of the beacon signal. Step 2: The notification unit notifies family members or caregivers of the location information collected by the collection unit. The notification unit notifies location information using, for example, SMS, email, or a dedicated app. SMS sends the elderly person's current location to family members, email notifications send an email containing location information to family members, and a dedicated app displays location information in real time and notifies family members or caregivers. Step 3: The verification unit confirms the elderly person's location based on the location information notified by the notification unit. The verification unit confirms the elderly person's location using, for example, a map application, checks the elderly person's movement history based on the location information, and verifies the accuracy of the location information. The map application displays the elderly person's current location, the movement history displays past location information in chronological order, and the accuracy of the location information verifies the accuracy of the GPS data. Step 4: The guidance unit guides the elderly person based on the location confirmed by the verification unit. The guidance unit guides the elderly person using, for example, voice guidance, visual guidance, and vibration guidance. Voice guidance will say, "Turn right," visual guidance will show the route on a map, and vibration guidance will indicate the direction by vibrating the smartphone.

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, notification unit, confirmation unit, guidance unit, analysis unit, and guide unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit identifies the location of the elderly person using the GPS, Wi-Fi location information, and Bluetooth beacons of the smart device 14. The notification unit notifies family members or caregivers of the location information using the communication functions of the smart device 14. The confirmation unit confirms the location of the elderly person using the map application of the smart device 14. The guidance unit guides the elderly person using the voice guidance, visual guidance, and vibration guidance of the smart device 14. The analysis unit photographs a person using the camera of the smart device 14, and the AI ​​analyzes the image to provide person information. The guide unit provides directions and introduces people using the speech synthesis technology and speech recognition technology of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, notification unit, confirmation unit, guidance unit, analysis unit, and guide unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit identifies the location of the elderly person using the GPS, Wi-Fi location information, and Bluetooth beacons of the smart glasses 214. The notification unit notifies family members or caregivers of the location information using the communication function of the smart glasses 214. The confirmation unit confirms the location of the elderly person using the map application of the smart glasses 214. The guidance unit guides the elderly person using the voice guidance, visual guidance, and vibration guidance of the smart glasses 214. The analysis unit photographs a person using the camera of the smart glasses 214, and the AI ​​analyzes the data to provide person information. The guide unit provides directions and introduces people using the voice synthesis technology and voice recognition technology of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the collection unit, notification unit, confirmation unit, guidance unit, analysis unit, and guide unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit identifies the location of the elderly person using the GPS, Wi-Fi location information, and Bluetooth beacons of the headset terminal 314. The notification unit notifies family members or caregivers of the location information using the communication function of the headset terminal 314. The confirmation unit confirms the location of the elderly person using the map application of the headset terminal 314. The guidance unit guides the elderly person using the voice guidance, visual guidance, and vibration guidance of the headset terminal 314. The analysis unit photographs a person using the camera of the headset terminal 314, and the AI ​​analyzes the image to provide person information. The guide unit provides directions and introduces people using the speech synthesis technology and speech recognition technology of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0176] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the collection unit, notification unit, confirmation unit, guidance unit, analysis unit, and guide unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit identifies the location of the elderly person using the robot 414's GPS, Wi-Fi location information, and Bluetooth beacons. The notification unit notifies family members or caregivers of the location information using the robot 414's communication function. The confirmation unit confirms the location of the elderly person using the robot 414's map application. The guidance unit guides the elderly person using the robot 414's voice guidance, visual guidance, and vibration guidance. The analysis unit photographs the person using the robot 414's camera, and the AI ​​analyzes the image to provide person information. The guide unit provides directions and introduces people using the robot 414's speech synthesis technology and speech recognition technology. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0184] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0186] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0187] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0188] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0193] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0194] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0195] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0196] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0197] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0201] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0202] (Note 1) A collection unit that collects location information of elderly people, A notification unit that notifies family members or caregivers of the location information collected by the aforementioned collection unit, A confirmation unit that confirms the location of the elderly person based on the location information notified by the notification unit, The system includes a guidance unit that guides elderly people based on their location confirmed by the aforementioned verification unit. A system characterized by the following features. (Note 2) It features an analysis unit that photographs the person in front of the elderly and uses AI to analyze the image and provide information about the person. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a guide unit that provides voice directions and character introductions when needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Location information is collected when elderly people leave a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It analyzes images taken with a smartphone and provides information about the person. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned guide section is Even if visual information cannot be confirmed, audio guidance will be provided. The system described in Appendix 3, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of elderly individuals and adjusts the frequency of location data collection based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past travel history of elderly individuals to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting location data, filtering is performed based on the elderly person's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of elderly individuals and prioritizes the collection of location data based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting location information, the system prioritizes collecting highly relevant information by considering the geographical location of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting location data, we analyze the social media activity of elderly people and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, The system estimates the emotions of elderly individuals and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, When a notification is sent, adjust the level of detail based on the importance of the location information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the location information category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, The system estimates the emotions of older adults and adjusts the length of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending a notification, the notification priority is determined based on when the location information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned verification unit is We estimate the emotions of older adults and adjust the confirmation criteria based on the estimated emotions of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is During verification, the accuracy of the verification is improved by considering the interrelationships of location information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is During verification, the attribute information of the location information provider will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is The system estimates the emotions of elderly individuals and adjusts the order in which the confirmation results are displayed based on the estimated emotions of the elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is During verification, the geographical distribution of location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is During verification, we refer to relevant literature regarding location information to improve the accuracy of the verification. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is The system estimates the emotions of elderly people and adjusts the way information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is When providing directions, the system optimizes the current directions by referring to past directions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is When providing directions, different directions will be applied depending on the category of location information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is The system estimates the emotions of elderly individuals and adjusts the importance of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is When providing directions, we analyze how the directions change based on when location information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is When providing directions, the system analyzes the directions by referring to market data related to location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and determines the priority of analysis based on these estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the interrelationships of the person's information. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned analysis unit, During the analysis, the attribute information of the person information provider will be taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and adjusts the display method of the analysis results based on the estimated emotions of the elderly individuals. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned analysis unit, During the analysis, the geographical distribution of personal information will be taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned analysis unit, During analysis, we refer to relevant literature on the person's information to improve the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned guide section is We estimate the emotions of older adults and adjust the guidance methods based on the estimated emotions of older adults. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned guide section is During guiding, the optimal guiding method is selected by referring to past guiding data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned guide section is When guiding, different guiding methods are applied for each category of location information. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned guide section is The system estimates the emotions of older adults and determines the priority of the guide based on the estimated emotions of older adults. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned guide section is During guiding, analyze changes in the guide based on when location information was acquired. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned guide section is When guiding, we analyze the guide by referring to market data related to location information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects location information of elderly people, A notification unit that notifies family members or caregivers of the location information collected by the aforementioned collection unit, A confirmation unit that confirms the location of the elderly person based on the location information notified by the notification unit, The system includes a guidance unit that guides elderly people based on their location confirmed by the aforementioned verification unit. A system characterized by the following features.

2. It features an analysis unit that photographs the person in front of the elderly person, and an AI analyzes the image to provide information about the person. The system according to feature 1.

3. It is equipped with a guide unit that provides voice directions and character introductions when needed. The system according to feature 1.

4. The aforementioned collection unit is Location information is collected when elderly people leave a specific area. The system according to feature 1.

5. The aforementioned analysis unit, It analyzes images taken with a smartphone and provides information about the person. The system according to feature 2.

6. The aforementioned guide section is Even if visual information cannot be confirmed, audio guidance will be provided. The system according to claim 3.

7. The aforementioned collection unit is The system estimates the emotions of elderly individuals and adjusts the frequency of location data collection based on these estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the past travel history of elderly individuals to select the optimal timing for data collection. The system according to feature 1.

Images