system

Abstract

The system according to this embodiment aims to analyze the mobility patterns of elderly people, propose and provide the most suitable means of transportation, and monitor their usage. [Solution] The system according to the embodiment comprises a proposal unit, a provision unit, a monitoring unit, and a notification unit. The proposal unit analyzes the elderly person's travel patterns and proposes the optimal means of transportation. The provision unit provides the means of transportation proposed by the proposal unit. The monitoring unit monitors the usage status of the means of transportation provided by the provision unit. The notification unit detects abnormalities based on the usage status monitored by the monitoring unit and notifies the family.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the optimal means of transportation for the movement of the elderly and the monitoring of its usage situation have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the movement pattern of the elderly, propose and provide the optimal means of transportation, and monitor its usage situation.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a proposal unit, a provision unit, a monitoring unit, and a notification unit. The proposal unit analyzes the elderly person's travel patterns and proposes the optimal mode of transportation. The provision unit provides the mode of transportation proposed by the proposal unit. The monitoring unit monitors the usage status of the mode of transportation provided by the provision unit. The notification unit detects abnormalities based on the usage status monitored by the monitoring unit and notifies the family. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the movement patterns of elderly people, propose and provide the most suitable means of transportation, and monitor their usage. [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 manages 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 comprehensive management service for the elderly according to an embodiment of the present invention is a comprehensive management service that supports the health of the elderly by providing diverse means of transportation, thereby reducing minor inconveniences and compromises in shopping and daily life, and adding stimulation to their daily lives. This comprehensive management service for the elderly comprehensively supports the challenges and anxieties faced by the elderly, including the provision of means of transportation, communication and monitoring with family, suggestions for entertainment, suggestions for healthy meal plans, schedule management, and reminder functions. For example, in terms of providing means of transportation, the AI ​​analyzes the elderly person's travel patterns and suggests the most suitable means of transportation. The AI ​​can analyze past travel history and suggest the use of taxis or buses. Next, in terms of communication and monitoring with family, the AI ​​analyzes the elderly person's situation and notifies the family if there is an abnormality. The AI ​​can monitor the elderly person's activity level and notify the family if there is an abnormality. Furthermore, in terms of suggesting entertainment, the AI ​​analyzes the elderly person's interests and preferences and suggests the most suitable entertainment. The AI ​​can analyze past viewing history and suggest movies or music. In terms of suggesting healthy meal plans, the AI ​​analyzes the elderly person's health condition and suggests the most suitable meal plan. The AI ​​can analyze the results of health checkups and suggest nutritionally balanced menus. Finally, as a schedule management and reminder function, the AI ​​analyzes the elderly person's schedule and sends reminders. The AI ​​can also analyze and remind about medical appointments. By managing these functions in one place, it is possible to alleviate the challenges and anxieties that the elderly face and support them in leading healthy and fulfilling lives. In this way, the integrated management service for the elderly can comprehensively support the lives of the elderly and provide them with healthy and fulfilling lives.

[0029] The integrated management service for the elderly according to this embodiment comprises a proposal unit, a provision unit, a monitoring unit, and a notification unit. The proposal unit analyzes the elderly person's movement patterns and proposes the optimal mode of transportation. For example, the proposal unit analyzes past movement history and proposes the optimal mode of transportation. The proposal unit can use AI to analyze the elderly person's movement patterns and propose the optimal mode of transportation. The provision unit provides the mode of transportation proposed by the proposal unit. For example, the provision unit provides modes of transportation such as taxis, buses, and electric wheelchairs. The provision unit can use AI to provide the proposed mode of transportation. The monitoring unit monitors the usage status of the mode of transportation provided by the provision unit. For example, the monitoring unit monitors the elderly person's activity level. The monitoring unit can use AI to monitor the usage status of the provided mode of transportation. The notification unit detects abnormalities based on the usage status monitored by the monitoring unit and notifies the family. For example, the notification unit notifies the family when an abnormality is detected. The notification unit can use AI to detect abnormalities and notify the family. As a result, the integrated management service for the elderly according to this embodiment can support the lives of the elderly person.

[0030] The proposal department analyzes the travel patterns of elderly people and suggests the most suitable mode of transportation. Specifically, the department collects the past travel history of elderly people and analyzes this data using AI. The AI ​​learns the travel patterns of elderly people using machine learning algorithms and understands travel trends at specific times of day and on specific days of the week. For example, it can analyze the travel patterns of elderly people who go to the hospital every Tuesday and suggest the most suitable mode of transportation. Furthermore, the proposal department also takes real-time data such as weather and traffic conditions into consideration. For example, it can suggest a taxi on a rainy day and a bus or electric wheelchair on a sunny day. Based on this data, the proposal department can suggest the most suitable mode of transportation to elderly people, reducing the burden of travel. The proposal department can also take into account the health condition and physical strength of elderly people. For example, it can suggest a more comfortable and less burdensome mode of transportation to elderly people with reduced physical strength. In this way, the proposal department can suggest the most suitable mode of transportation that meets the individual needs of elderly people, improving the safety and comfort of travel.

[0031] The service provider will provide the transportation options suggested by the suggestion provider. Specifically, the service provider will arrange and provide transportation options such as taxis, buses, and electric wheelchairs to the elderly. The service provider can use AI to quickly and efficiently arrange the suggested transportation options. For example, if the suggestion provider suggests a taxi, the service provider will automatically make a reservation with a partner taxi company and arrange for a taxi to take the elderly person to their home. If a bus is suggested, the service provider will check the bus schedule and guide the elderly person to the most suitable bus route. If an electric wheelchair is suggested, the service provider will deliver the electric wheelchair to the elderly person's home and provide instructions on how to use it. Furthermore, the service provider will also provide support regarding the use of transportation. For example, they can arrange for staff to assist with getting in and out of taxis or provide an app to assist with bus transfers. In this way, the service provider can support the elderly so that they can use the suggested transportation options with peace of mind and reduce the burden of travel.

[0032] The monitoring unit monitors the usage of transportation provided by the service provider. Specifically, the monitoring unit monitors the activity levels and travel patterns of elderly individuals in real time. Using AI, it can analyze the usage of the provided transportation and detect abnormal patterns and risks. For example, if an elderly person deviates from their planned route or travels for a longer period than usual, the monitoring unit will detect this and recognize it as an anomaly. The monitoring unit can use GPS data and sensor information to determine the current location and travel speed of elderly individuals. Furthermore, the monitoring unit can also monitor changes in the health status and physical condition of elderly individuals. For example, it collects data on heart rate and blood pressure, and takes immediate action if an abnormality is detected. In this way, the monitoring unit can ensure the safety of elderly individuals and respond quickly if an abnormality occurs.

[0033] The notification unit detects anomalies based on usage data monitored by the monitoring unit and notifies the family. Specifically, the notification unit uses AI to analyze monitoring data and notifies the family if an anomaly is detected. For example, if an elderly person deviates from their planned route or travels for a longer period than usual, the notification unit detects this as an anomaly and notifies the family. Notifications are made via smartphone apps, SMS, email, etc. Furthermore, the notification unit can customize the content of notifications according to the type and urgency of the anomaly. For example, for minor anomalies, only app notifications are sent, while for more urgent cases, phone notifications are sent. In addition, the notification unit can notify not only the family but also care staff and medical institutions. This allows the notification unit to ensure the safety of the elderly person and respond quickly if an anomaly occurs. Furthermore, the notification unit saves the notification history for later review. This allows family members and care staff to understand the elderly person's movement patterns and the occurrence of anomalies, enabling them to take appropriate action.

[0034] The proposal unit can analyze past travel history and suggest the most suitable mode of transportation. For example, the proposal unit can analyze past travel history and suggest the most suitable mode of transportation. The proposal unit can use AI to analyze past travel history and suggest the most suitable mode of transportation. For example, the proposal unit collects and analyzes GPS data and transportation usage history as past travel history. The proposal unit can use AI to analyze the collected data and suggest the most suitable mode of transportation. This allows for the suggestion of more appropriate modes of transportation by analyzing past travel history.

[0035] The service provider can offer transportation options such as taxis, buses, and electric wheelchairs. For example, the service provider can arrange taxis to support the mobility of elderly people. The service provider can also provide bus routes to support the mobility of elderly people. The service provider can also rent out electric wheelchairs to support the mobility of elderly people. The service provider can offer transportation options suggested using AI. This allows the service provider to support the mobility of elderly people by offering a variety of transportation options.

[0036] The monitoring unit can monitor the activity levels of elderly individuals. For example, the monitoring unit can monitor the number of steps taken by elderly individuals to understand their activity level. The monitoring unit can also monitor the distance traveled to understand their activity level. The monitoring unit can also monitor calories burned to understand their activity level. The monitoring unit can use AI to monitor the activity levels of elderly individuals. This allows for early detection of abnormalities by monitoring the activity levels of elderly individuals.

[0037] The notification unit can notify family members if an abnormality is detected. For example, the notification unit can notify family members if an abnormality is detected in the activity level of an elderly person. The notification unit can use AI to detect abnormalities and notify family members. The notification unit can notify family members by phone or email, for example. The notification unit can also notify family members using app notifications. This allows for a quick response by notifying family members when an abnormality is detected.

[0038] The suggestion function can estimate the emotions of elderly people and adjust the suggested transportation options based on those emotions. For example, if an elderly person is feeling anxious, the suggestion function will prioritize suggesting taxis to provide a sense of security. If an elderly person is relaxed, the suggestion function can also suggest public transportation options such as buses or electric wheelchairs. If an elderly person is in a hurry, the suggestion function can also suggest the fastest transportation option. The suggestion function can use AI to estimate the emotions of elderly people and adjust the suggested transportation options based on those emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to suggest transportation options that are tailored to the emotions of elderly people.

[0039] The suggestion function can analyze the past travel history of elderly people and propose the most suitable mode of transportation, taking into account seasonal and weather variations. For example, in winter, the suggestion function can suggest a taxi to avoid the cold. In rainy weather, the suggestion function can also suggest a route closer to a bus stop. In summer, the suggestion function can suggest a mode of transportation that allows travel during cooler hours. The suggestion function can use AI to analyze the past travel history of elderly people and propose the most suitable mode of transportation, taking into account seasonal and weather variations. This makes it possible to suggest modes of transportation that are appropriate for the season and weather.

[0040] The proposal department can analyze the travel patterns of elderly people, evaluate the cost-effectiveness of proposed transportation methods, and make optimal recommendations. For example, the proposal department can propose the most cost-effective transportation method in accordance with the elderly person's budget. The proposal department can also propose transportation methods that allow the use of season tickets or discount coupons for regular travel. The proposal department can also propose transportation methods that offer discounts during specific time slots. The proposal department can use AI to analyze the travel patterns of elderly people, evaluate the cost-effectiveness of proposed transportation methods, and make optimal recommendations. This makes it possible to propose cost-effective transportation methods.

[0041] The suggestion unit can estimate the emotions of elderly people and determine the priority of suggested modes of transportation based on those estimated emotions. For example, if an elderly person is feeling stressed, the suggestion unit will prioritize suggesting the most comfortable mode of transportation. If an elderly person is enjoying themselves, the suggestion unit may also prioritize suggesting modes of transportation that take scenic routes. If an elderly person is tired, the suggestion unit may also prioritize suggesting the fastest mode of transportation. The suggestion unit can use AI to estimate the emotions of elderly people and determine the priority of suggested modes of transportation based on those estimated emotions. This makes it possible to prioritize modes of transportation according to the emotions of elderly people.

[0042] The proposal department can analyze the mobility patterns of elderly people, evaluate the environmental impact of proposed modes of transportation, and make optimal suggestions. For example, the proposal department can propose environmentally friendly electric wheelchairs. The proposal department can also propose reducing the environmental impact by using public transportation. For short distances, the proposal department can also suggest walking or cycling. The proposal department can use AI to analyze the mobility patterns of elderly people, evaluate the environmental impact of proposed modes of transportation, and make optimal suggestions. This makes it possible to propose modes of transportation that take environmental impact into consideration.

[0043] The suggestion department can analyze the travel patterns of elderly people, evaluate the safety of proposed modes of transportation, and make optimal recommendations. For example, the suggestion department can suggest taxis that take safe routes for nighttime travel. If elderly people have difficulty walking, the suggestion department can also suggest electric wheelchairs. The suggestion department can also suggest public transportation during off-peak hours to avoid congestion. The suggestion department can use AI to analyze the travel patterns of elderly people, evaluate the safety of proposed modes of transportation, and make optimal recommendations. This makes it possible to suggest modes of transportation that take safety into consideration.

[0044] The service provider can estimate the emotions of elderly individuals and adjust the type of transportation offered based on those estimates. For example, if an elderly person is feeling anxious, the service provider will prioritize providing a taxi. If an elderly person is relaxed, the service provider may also offer a bus or an electric wheelchair. If an elderly person is in a hurry, the service provider may also offer the fastest possible mode of transportation. The service provider can use AI to estimate the emotions of elderly individuals and adjust the type of transportation offered based on those estimates. This makes it possible to provide transportation that is tailored to the emotions of elderly individuals.

[0045] The service provider can monitor the usage status of the transportation methods they provide in real time and adjust the provision of transportation as needed. For example, if an elderly person is using a taxi, the service provider can monitor the arrival time in real time and provide alternative transportation if a delay occurs. The service provider can also monitor the status of buses in real time and provide alternative transportation if a delay occurs. The service provider can monitor the battery level of electric wheelchairs and guide users to charging stations as needed. The service provider can use AI to monitor the usage status of the transportation methods they provide in real time and adjust the provision of transportation as needed. This enables real-time adjustment of transportation provision.

[0046] The service provider can monitor the maintenance status of the transportation services they provide and perform maintenance at the optimal time. For example, the service provider can monitor the maintenance schedule of taxis and perform maintenance as needed. The service provider can also monitor the maintenance status of buses and perform regular inspections. The service provider can monitor the condition of batteries and tires of electric wheelchairs and replace or repair them as needed. The service provider can use AI to monitor the maintenance status of the transportation services they provide and perform maintenance at the optimal time. This makes it possible to perform maintenance on transportation services at the optimal time.

[0047] The service provider can estimate the emotions of elderly individuals and adjust the duration of transportation based on those estimated emotions. For example, if an elderly person is tired, the service provider can provide transportation that allows for a short travel time. If an elderly person is relaxed, the service provider can also provide transportation that allows for a slower travel time. If an elderly person is in a hurry, the service provider can also provide the fastest possible transportation. The service provider can use AI to estimate the emotions of elderly individuals and adjust the duration of transportation based on those estimated emotions. This makes it possible to adjust the duration of transportation according to the emotions of elderly individuals.

[0048] The service provider can collect user feedback on the transportation methods they offer and use it to improve their services. For example, they can collect feedback from elderly people after they use a taxi and use it to improve the service. They can also collect feedback from bus users and use it to improve the operating schedule. They can also collect feedback from electric wheelchair users and use it to improve the functionality. The service provider can collect user feedback on the transportation methods they offer using AI and use it to improve their services. This makes it possible to improve services by collecting user feedback.

[0049] The service provider can monitor the health status of users of the transportation services it provides and offer health support as needed. For example, the service provider can monitor the health status of elderly people using taxis and contact medical institutions if there are any abnormalities. The service provider can also monitor the health status of bus users and notify the driver if there are any abnormalities. The service provider can monitor the health status of electric wheelchair users and notify their families if there are any abnormalities. The service provider can use AI to monitor the health status of users of the transportation services it provides and offer health support as needed. This makes it possible to monitor the health status of users and provide health support as needed.

[0050] The monitoring unit can estimate the emotions of elderly individuals and adjust the monitoring frequency based on the estimated emotions. For example, if an elderly person is feeling anxious, the monitoring unit will increase the monitoring frequency. If an elderly person is relaxed, the monitoring unit can also decrease the monitoring frequency. If an elderly person is in a hurry, the monitoring unit can also adjust the monitoring frequency. The monitoring unit uses AI to estimate the emotions of elderly individuals and adjusts the monitoring frequency based on the estimated emotions. This makes it possible to adjust the monitoring frequency in accordance with the emotions of elderly individuals.

[0051] The monitoring unit can optimize algorithms for monitoring the activity levels of elderly individuals and detecting abnormal patterns. For example, the monitoring unit can monitor an elderly individual's walking pattern and notify if an abnormality is detected. It can also monitor an elderly individual's heart rate and notify if an abnormality is detected. Furthermore, it can monitor an elderly individual's sleep pattern and notify if an abnormality is detected. The monitoring unit can use AI to optimize algorithms for monitoring the activity levels of elderly individuals and detecting abnormal patterns, thereby improving the accuracy of abnormal pattern detection.

[0052] The monitoring unit can monitor the activity levels of elderly individuals, learn their daily activity patterns, and detect abnormalities early. For example, the monitoring unit can learn an elderly individual's daily walking pattern and notify them if an abnormality is detected. The monitoring unit can also learn an elderly individual's daily heart rate pattern and notify them if an abnormality is detected. The monitoring unit can also learn an elderly individual's daily sleep pattern and notify them if an abnormality is detected. The monitoring unit uses AI to monitor the activity levels of elderly individuals, learn their daily activity patterns, and detect abnormalities early. This allows for early detection of abnormalities by learning their daily activity patterns.

[0053] The monitoring unit can estimate the emotions of elderly individuals and adjust the monitoring targets based on those estimated emotions. For example, if an elderly individual is feeling anxious, the monitoring unit will focus on monitoring heart rate and blood pressure. If an elderly individual is relaxed, the monitoring unit can focus on monitoring activity levels. If an elderly individual is in a hurry, the monitoring unit can focus on monitoring movement speed. The monitoring unit uses AI to estimate the emotions of elderly individuals and adjusts the monitoring targets based on those estimated emotions. This makes it possible to adjust the monitoring targets according to the emotions of the elderly individual.

[0054] The monitoring unit can implement a system that monitors the activity levels of elderly individuals and automatically provides first aid if an abnormality is detected. For example, the monitoring unit can implement a system that automatically provides first aid if an elderly person falls. The monitoring unit can also implement a system that automatically provides first aid if an elderly person's heart rate rises abnormally. The monitoring unit can also implement a system that automatically provides first aid if an elderly person's blood pressure drops abnormally. The monitoring unit can implement a system that uses AI to monitor the activity levels of elderly individuals and automatically provides first aid if an abnormality is detected. This makes it possible to automatically provide first aid when an abnormality is detected.

[0055] The monitoring unit can monitor the activity levels of elderly individuals and, if an abnormality is detected, can coordinate with medical institutions to take appropriate action. For example, if an elderly person falls, the monitoring unit will contact a medical institution for assistance. The monitoring unit can also contact a medical institution for assistance if an elderly person's heart rate increases abnormally. The monitoring unit can also contact a medical institution for assistance if an elderly person's blood pressure drops abnormally. The monitoring unit uses AI to monitor the activity levels of elderly individuals and, if an abnormality is detected, can coordinate with medical institutions to take appropriate action. This makes it possible to coordinate with medical institutions to take appropriate action when an abnormality is detected.

[0056] The notification unit can estimate the emotions of elderly individuals and adjust the content and timing of notifications based on those estimated emotions. For example, if an elderly person is feeling anxious, the notification unit will send a reassuring notification. If an elderly person is relaxed, the notification unit can reduce the frequency of notifications and only send important information. If an elderly person is in a hurry, the notification unit can send a notification that allows for a quick response. The notification unit uses AI to estimate the emotions of elderly individuals and adjusts the content and timing of notifications based on those estimated emotions. This makes it possible to adjust the content and timing of notifications according to the emotions of elderly individuals.

[0057] The notification unit can be enhanced to notify not only family members but also nearby caregivers when an abnormality is detected. For example, if an elderly person falls, the notification unit will simultaneously notify family members and nearby caregivers. The notification unit can also simultaneously notify family members and nearby caregivers if an elderly person's heart rate increases abnormally. The notification unit can also simultaneously notify family members and nearby caregivers if an elderly person's blood pressure drops abnormally. The notification unit can be enhanced to notify not only family members but also nearby caregivers when an abnormality is detected using AI. This makes it possible to notify not only family members but also nearby caregivers when an abnormality is detected.

[0058] The notification unit can suggest appropriate countermeasures based on the content of the notification when an abnormality is detected. For example, if an elderly person falls, the notification unit can suggest first aid methods. If an elderly person's heart rate is abnormally elevated, the notification unit can also suggest ways to keep them at rest. If an elderly person's blood pressure is abnormally low, the notification unit can also suggest ways to rehydrate them. The notification unit can use AI to detect abnormalities and suggest appropriate countermeasures based on the content of the notification. This makes it possible to suggest appropriate countermeasures when an abnormality is detected.

[0059] The notification unit can estimate the emotions of elderly individuals and determine notification priorities based on those estimated emotions. For example, if an elderly person is feeling anxious, the notification unit will prioritize important notifications. If an elderly person is relaxed, the notification unit can also lower the priority of notifications. If an elderly person is in a hurry, the notification unit can also prioritize notifications that require a quick response. The notification unit uses AI to estimate the emotions of elderly individuals and determines notification priorities based on those estimated emotions. This makes it possible to prioritize notifications in accordance with the emotions of elderly individuals.

[0060] The notification unit can provide notifications in a format easily understood by the elderly when an abnormality is detected. For example, if an elderly person falls, the notification unit will provide a notification in simple and easy-to-understand language. If an elderly person's heart rate is abnormally elevated, the notification unit can also provide a notification using a visually easy-to-understand graph. If an elderly person's blood pressure is abnormally low, the notification unit can also provide a notification using an illustration. The notification unit can use AI to detect abnormalities and provide notifications in a format easily understood by the elderly. This makes it possible to provide notifications in a format easily understood by the elderly when an abnormality is detected.

[0061] The notification unit can be enhanced with the ability to provide notifications in multiple languages ​​when an abnormality is detected. For example, if an elderly person falls, the notification unit can provide notifications in multiple languages. The notification unit can also provide notifications in multiple languages ​​if an elderly person's heart rate increases abnormally. The notification unit can also provide notifications in multiple languages ​​if an elderly person's blood pressure drops abnormally. The notification unit can be enhanced with the ability to provide notifications in multiple languages ​​when an abnormality is detected using AI. This makes it possible to provide notifications in multiple languages ​​when an abnormality is detected.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The proposal department can analyze the past travel history of elderly individuals and consider information such as local events and festivals when suggesting the most suitable mode of transportation. For example, if a local festival is being held, it can suggest transportation that provides good access to the location. During periods with many local events, it can also recommend the use of public transportation. Furthermore, it can suggest special transportation options for attending specific events. This makes it possible to suggest transportation options tailored to local events and festivals.

[0064] The service provider can analyze the mobility patterns of elderly people, evaluate eco-friendly options for proposed transportation methods, and make optimal recommendations. For example, it can suggest environmentally friendly transportation methods such as electric wheelchairs and electric motorcycles. It can also suggest reducing environmental impact by recommending the use of public transportation. Furthermore, it can suggest walking or cycling for short distances. This enables the proposal of eco-friendly transportation methods.

[0065] The monitoring unit can implement a system that monitors the activity levels of elderly individuals and automatically contacts a medical institution if an abnormality is detected. For example, if an elderly person falls, a system can be implemented to automatically contact a medical institution. If an elderly person's heart rate increases abnormally, a system can be implemented to automatically contact a medical institution. Furthermore, if an elderly person's blood pressure drops abnormally, a system can be implemented to automatically contact a medical institution. This makes it possible to automatically contact a medical institution when an abnormality is detected.

[0066] The notification unit can be enhanced with a function to notify not only family members but also nearby caregivers when an abnormality is detected. For example, if an elderly person falls, both family members and nearby caregivers can be notified simultaneously. If an elderly person's heart rate increases abnormally, both family members and nearby caregivers can be notified simultaneously. Similarly, if an elderly person's blood pressure drops abnormally, both family members and nearby caregivers can be notified simultaneously. This makes it possible to notify not only family members but also nearby caregivers when an abnormality is detected.

[0067] The service provider can collect user feedback on the transportation methods they offer and use it to improve their services. For example, they can collect feedback from elderly people after they use a taxi and use it to improve the service. They can also collect feedback from bus users and use it to improve the operating schedule. Furthermore, they can collect feedback from electric wheelchair users and use it to improve the functionality. In this way, collecting user feedback makes it possible to improve services.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The proposal department analyzes the elderly person's travel patterns and suggests the most suitable mode of transportation. The proposal department analyzes past travel history and uses AI to analyze the elderly person's travel patterns and suggests the most suitable mode of transportation. Step 2: The provisioning department provides the transportation methods proposed by the proposaling department. The provisioning department can provide transportation methods such as taxis, buses, and electric wheelchairs, and can also provide transportation methods proposed using AI. Step 3: The monitoring unit monitors the usage of transportation provided by the service provider. The monitoring unit can monitor the activity levels of elderly individuals and use AI to monitor the usage of transportation provided. Step 4: The notification unit detects anomalies based on usage patterns monitored by the monitoring unit and notifies the family. The notification unit can notify the family when an anomaly is detected, and can use AI to detect anomalies and notify the family.

[0070] (Example of form 2) The comprehensive management service for the elderly according to an embodiment of the present invention is a comprehensive management service that supports the health of the elderly by providing diverse means of transportation, thereby reducing minor inconveniences and compromises in shopping and daily life, and adding stimulation to their daily lives. This comprehensive management service for the elderly comprehensively supports the challenges and anxieties faced by the elderly, including the provision of means of transportation, communication and monitoring with family, suggestions for entertainment, suggestions for healthy meal plans, schedule management, and reminder functions. For example, in terms of providing means of transportation, the AI ​​analyzes the elderly person's travel patterns and suggests the most suitable means of transportation. The AI ​​can analyze past travel history and suggest the use of taxis or buses. Next, in terms of communication and monitoring with family, the AI ​​analyzes the elderly person's situation and notifies the family if there is an abnormality. The AI ​​can monitor the elderly person's activity level and notify the family if there is an abnormality. Furthermore, in terms of suggesting entertainment, the AI ​​analyzes the elderly person's interests and preferences and suggests the most suitable entertainment. The AI ​​can analyze past viewing history and suggest movies or music. In terms of suggesting healthy meal plans, the AI ​​analyzes the elderly person's health condition and suggests the most suitable meal plan. The AI ​​can analyze the results of health checkups and suggest nutritionally balanced menus. Finally, as a schedule management and reminder function, the AI ​​analyzes the elderly person's schedule and sends reminders. The AI ​​can also analyze and remind about medical appointments. By managing these functions in one place, it is possible to alleviate the challenges and anxieties that the elderly face and support them in leading healthy and fulfilling lives. In this way, the integrated management service for the elderly can comprehensively support the lives of the elderly and provide them with healthy and fulfilling lives.

[0071] The integrated management service for the elderly according to this embodiment comprises a proposal unit, a provision unit, a monitoring unit, and a notification unit. The proposal unit analyzes the elderly person's movement patterns and proposes the optimal mode of transportation. For example, the proposal unit analyzes past movement history and proposes the optimal mode of transportation. The proposal unit can use AI to analyze the elderly person's movement patterns and propose the optimal mode of transportation. The provision unit provides the mode of transportation proposed by the proposal unit. For example, the provision unit provides modes of transportation such as taxis, buses, and electric wheelchairs. The provision unit can use AI to provide the proposed mode of transportation. The monitoring unit monitors the usage status of the mode of transportation provided by the provision unit. For example, the monitoring unit monitors the elderly person's activity level. The monitoring unit can use AI to monitor the usage status of the provided mode of transportation. The notification unit detects abnormalities based on the usage status monitored by the monitoring unit and notifies the family. For example, the notification unit notifies the family when an abnormality is detected. The notification unit can use AI to detect abnormalities and notify the family. As a result, the integrated management service for the elderly according to this embodiment can support the lives of the elderly person.

[0072] The proposal department analyzes the travel patterns of elderly people and suggests the most suitable mode of transportation. Specifically, the department collects the past travel history of elderly people and analyzes this data using AI. The AI ​​learns the travel patterns of elderly people using machine learning algorithms and understands travel trends at specific times of day and on specific days of the week. For example, it can analyze the travel patterns of elderly people who go to the hospital every Tuesday and suggest the most suitable mode of transportation. Furthermore, the proposal department also takes real-time data such as weather and traffic conditions into consideration. For example, it can suggest a taxi on a rainy day and a bus or electric wheelchair on a sunny day. Based on this data, the proposal department can suggest the most suitable mode of transportation to elderly people, reducing the burden of travel. The proposal department can also take into account the health condition and physical strength of elderly people. For example, it can suggest a more comfortable and less burdensome mode of transportation to elderly people with reduced physical strength. In this way, the proposal department can suggest the most suitable mode of transportation that meets the individual needs of elderly people, improving the safety and comfort of travel.

[0073] The service provider will provide the transportation options suggested by the suggestion provider. Specifically, the service provider will arrange and provide transportation options such as taxis, buses, and electric wheelchairs to the elderly. The service provider can use AI to quickly and efficiently arrange the suggested transportation options. For example, if the suggestion provider suggests a taxi, the service provider will automatically make a reservation with a partner taxi company and arrange for a taxi to take the elderly person to their home. If a bus is suggested, the service provider will check the bus schedule and guide the elderly person to the most suitable bus route. If an electric wheelchair is suggested, the service provider will deliver the electric wheelchair to the elderly person's home and provide instructions on how to use it. Furthermore, the service provider will also provide support regarding the use of transportation. For example, they can arrange for staff to assist with getting in and out of taxis or provide an app to assist with bus transfers. In this way, the service provider can support the elderly so that they can use the suggested transportation options with peace of mind and reduce the burden of travel.

[0074] The monitoring unit monitors the usage of transportation provided by the service provider. Specifically, the monitoring unit monitors the activity levels and travel patterns of elderly individuals in real time. Using AI, it can analyze the usage of the provided transportation and detect abnormal patterns and risks. For example, if an elderly person deviates from their planned route or travels for a longer period than usual, the monitoring unit will detect this and recognize it as an anomaly. The monitoring unit can use GPS data and sensor information to determine the current location and travel speed of elderly individuals. Furthermore, the monitoring unit can also monitor changes in the health status and physical condition of elderly individuals. For example, it collects data on heart rate and blood pressure, and takes immediate action if an abnormality is detected. In this way, the monitoring unit can ensure the safety of elderly individuals and respond quickly if an abnormality occurs.

[0075] The notification unit detects anomalies based on usage data monitored by the monitoring unit and notifies the family. Specifically, the notification unit uses AI to analyze monitoring data and notifies the family if an anomaly is detected. For example, if an elderly person deviates from their planned route or travels for a longer period than usual, the notification unit detects this as an anomaly and notifies the family. Notifications are made via smartphone apps, SMS, email, etc. Furthermore, the notification unit can customize the content of notifications according to the type and urgency of the anomaly. For example, for minor anomalies, only app notifications are sent, while for more urgent cases, phone notifications are sent. In addition, the notification unit can notify not only the family but also care staff and medical institutions. This allows the notification unit to ensure the safety of the elderly person and respond quickly if an anomaly occurs. Furthermore, the notification unit saves the notification history for later review. This allows family members and care staff to understand the elderly person's movement patterns and the occurrence of anomalies, enabling them to take appropriate action.

[0076] The proposal unit can analyze past travel history and suggest the most suitable mode of transportation. For example, the proposal unit can analyze past travel history and suggest the most suitable mode of transportation. The proposal unit can use AI to analyze past travel history and suggest the most suitable mode of transportation. For example, the proposal unit collects and analyzes GPS data and transportation usage history as past travel history. The proposal unit can use AI to analyze the collected data and suggest the most suitable mode of transportation. This allows for the suggestion of more appropriate modes of transportation by analyzing past travel history.

[0077] The service provider can offer transportation options such as taxis, buses, and electric wheelchairs. For example, the service provider can arrange taxis to support the mobility of elderly people. The service provider can also provide bus routes to support the mobility of elderly people. The service provider can also rent out electric wheelchairs to support the mobility of elderly people. The service provider can offer transportation options suggested using AI. This allows the service provider to support the mobility of elderly people by offering a variety of transportation options.

[0078] The monitoring unit can monitor the activity levels of elderly individuals. For example, the monitoring unit can monitor the number of steps taken by elderly individuals to understand their activity level. The monitoring unit can also monitor the distance traveled to understand their activity level. The monitoring unit can also monitor calories burned to understand their activity level. The monitoring unit can use AI to monitor the activity levels of elderly individuals. This allows for early detection of abnormalities by monitoring the activity levels of elderly individuals.

[0079] The notification unit can notify family members if an abnormality is detected. For example, the notification unit can notify family members if an abnormality is detected in the activity level of an elderly person. The notification unit can use AI to detect abnormalities and notify family members. The notification unit can notify family members by phone or email, for example. The notification unit can also notify family members using app notifications. This allows for a quick response by notifying family members when an abnormality is detected.

[0080] The suggestion function can estimate the emotions of elderly people and adjust the suggested transportation options based on those emotions. For example, if an elderly person is feeling anxious, the suggestion function will prioritize suggesting taxis to provide a sense of security. If an elderly person is relaxed, the suggestion function can also suggest public transportation options such as buses or electric wheelchairs. If an elderly person is in a hurry, the suggestion function can also suggest the fastest transportation option. The suggestion function can use AI to estimate the emotions of elderly people and adjust the suggested transportation options based on those emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to suggest transportation options that are tailored to the emotions of elderly people.

[0081] The suggestion function can analyze the past travel history of elderly people and propose the most suitable mode of transportation, taking into account seasonal and weather variations. For example, in winter, the suggestion function can suggest a taxi to avoid the cold. In rainy weather, the suggestion function can also suggest a route closer to a bus stop. In summer, the suggestion function can suggest a mode of transportation that allows travel during cooler hours. The suggestion function can use AI to analyze the past travel history of elderly people and propose the most suitable mode of transportation, taking into account seasonal and weather variations. This makes it possible to suggest modes of transportation that are appropriate for the season and weather.

[0082] The proposal department can analyze the travel patterns of elderly people, evaluate the cost-effectiveness of proposed transportation methods, and make optimal recommendations. For example, the proposal department can propose the most cost-effective transportation method in accordance with the elderly person's budget. The proposal department can also propose transportation methods that allow the use of season tickets or discount coupons for regular travel. The proposal department can also propose transportation methods that offer discounts during specific time slots. The proposal department can use AI to analyze the travel patterns of elderly people, evaluate the cost-effectiveness of proposed transportation methods, and make optimal recommendations. This makes it possible to propose cost-effective transportation methods.

[0083] The suggestion unit can estimate the emotions of elderly people and determine the priority of suggested modes of transportation based on those estimated emotions. For example, if an elderly person is feeling stressed, the suggestion unit will prioritize suggesting the most comfortable mode of transportation. If an elderly person is enjoying themselves, the suggestion unit may also prioritize suggesting modes of transportation that take scenic routes. If an elderly person is tired, the suggestion unit may also prioritize suggesting the fastest mode of transportation. The suggestion unit can use AI to estimate the emotions of elderly people and determine the priority of suggested modes of transportation based on those estimated emotions. This makes it possible to prioritize modes of transportation according to the emotions of elderly people.

[0084] The proposal department can analyze the mobility patterns of elderly people, evaluate the environmental impact of proposed modes of transportation, and make optimal suggestions. For example, the proposal department can propose environmentally friendly electric wheelchairs. The proposal department can also propose reducing the environmental impact by using public transportation. For short distances, the proposal department can also suggest walking or cycling. The proposal department can use AI to analyze the mobility patterns of elderly people, evaluate the environmental impact of proposed modes of transportation, and make optimal suggestions. This makes it possible to propose modes of transportation that take environmental impact into consideration.

[0085] The suggestion department can analyze the travel patterns of elderly people, evaluate the safety of proposed modes of transportation, and make optimal recommendations. For example, the suggestion department can suggest taxis that take safe routes for nighttime travel. If elderly people have difficulty walking, the suggestion department can also suggest electric wheelchairs. The suggestion department can also suggest public transportation during off-peak hours to avoid congestion. The suggestion department can use AI to analyze the travel patterns of elderly people, evaluate the safety of proposed modes of transportation, and make optimal recommendations. This makes it possible to suggest modes of transportation that take safety into consideration.

[0086] The service provider can estimate the emotions of elderly individuals and adjust the type of transportation offered based on those estimates. For example, if an elderly person is feeling anxious, the service provider will prioritize providing a taxi. If an elderly person is relaxed, the service provider may also offer a bus or an electric wheelchair. If an elderly person is in a hurry, the service provider may also offer the fastest possible mode of transportation. The service provider can use AI to estimate the emotions of elderly individuals and adjust the type of transportation offered based on those estimates. This makes it possible to provide transportation that is tailored to the emotions of elderly individuals.

[0087] The service provider can monitor the usage status of the transportation methods they provide in real time and adjust the provision of transportation as needed. For example, if an elderly person is using a taxi, the service provider can monitor the arrival time in real time and provide alternative transportation if a delay occurs. The service provider can also monitor the status of buses in real time and provide alternative transportation if a delay occurs. The service provider can monitor the battery level of electric wheelchairs and guide users to charging stations as needed. The service provider can use AI to monitor the usage status of the transportation methods they provide in real time and adjust the provision of transportation as needed. This enables real-time adjustment of transportation provision.

[0088] The service provider can monitor the maintenance status of the transportation services they provide and perform maintenance at the optimal time. For example, the service provider can monitor the maintenance schedule of taxis and perform maintenance as needed. The service provider can also monitor the maintenance status of buses and perform regular inspections. The service provider can monitor the condition of batteries and tires of electric wheelchairs and replace or repair them as needed. The service provider can use AI to monitor the maintenance status of the transportation services they provide and perform maintenance at the optimal time. This makes it possible to perform maintenance on transportation services at the optimal time.

[0089] The service provider can estimate the emotions of elderly individuals and adjust the duration of transportation based on those estimated emotions. For example, if an elderly person is tired, the service provider can provide transportation that allows for a short travel time. If an elderly person is relaxed, the service provider can also provide transportation that allows for a slower travel time. If an elderly person is in a hurry, the service provider can also provide the fastest possible transportation. The service provider can use AI to estimate the emotions of elderly individuals and adjust the duration of transportation based on those estimated emotions. This makes it possible to adjust the duration of transportation according to the emotions of elderly individuals.

[0090] The service provider can collect user feedback on the transportation methods they offer and use it to improve their services. For example, they can collect feedback from elderly people after they use a taxi and use it to improve the service. They can also collect feedback from bus users and use it to improve the operating schedule. They can also collect feedback from electric wheelchair users and use it to improve the functionality. The service provider can collect user feedback on the transportation methods they offer using AI and use it to improve their services. This makes it possible to improve services by collecting user feedback.

[0091] The service provider can monitor the health status of users of the transportation services it provides and offer health support as needed. For example, the service provider can monitor the health status of elderly people using taxis and contact medical institutions if there are any abnormalities. The service provider can also monitor the health status of bus users and notify the driver if there are any abnormalities. The service provider can monitor the health status of electric wheelchair users and notify their families if there are any abnormalities. The service provider can use AI to monitor the health status of users of the transportation services it provides and offer health support as needed. This makes it possible to monitor the health status of users and provide health support as needed.

[0092] The monitoring unit can estimate the emotions of elderly individuals and adjust the monitoring frequency based on the estimated emotions. For example, if an elderly person is feeling anxious, the monitoring unit will increase the monitoring frequency. If an elderly person is relaxed, the monitoring unit can also decrease the monitoring frequency. If an elderly person is in a hurry, the monitoring unit can also adjust the monitoring frequency. The monitoring unit uses AI to estimate the emotions of elderly individuals and adjusts the monitoring frequency based on the estimated emotions. This makes it possible to adjust the monitoring frequency in accordance with the emotions of elderly individuals.

[0093] The monitoring unit can optimize algorithms for monitoring the activity levels of elderly individuals and detecting abnormal patterns. For example, the monitoring unit can monitor an elderly individual's walking pattern and notify if an abnormality is detected. It can also monitor an elderly individual's heart rate and notify if an abnormality is detected. Furthermore, it can monitor an elderly individual's sleep pattern and notify if an abnormality is detected. The monitoring unit can use AI to optimize algorithms for monitoring the activity levels of elderly individuals and detecting abnormal patterns, thereby improving the accuracy of abnormal pattern detection.

[0094] The monitoring unit can monitor the activity levels of elderly individuals, learn their daily activity patterns, and detect abnormalities early. For example, the monitoring unit can learn an elderly individual's daily walking pattern and notify them if an abnormality is detected. The monitoring unit can also learn an elderly individual's daily heart rate pattern and notify them if an abnormality is detected. The monitoring unit can also learn an elderly individual's daily sleep pattern and notify them if an abnormality is detected. The monitoring unit uses AI to monitor the activity levels of elderly individuals, learn their daily activity patterns, and detect abnormalities early. This allows for early detection of abnormalities by learning their daily activity patterns.

[0095] The monitoring unit can estimate the emotions of elderly individuals and adjust the monitoring targets based on those estimated emotions. For example, if an elderly individual is feeling anxious, the monitoring unit will focus on monitoring heart rate and blood pressure. If an elderly individual is relaxed, the monitoring unit can focus on monitoring activity levels. If an elderly individual is in a hurry, the monitoring unit can focus on monitoring movement speed. The monitoring unit uses AI to estimate the emotions of elderly individuals and adjusts the monitoring targets based on those estimated emotions. This makes it possible to adjust the monitoring targets according to the emotions of the elderly individual.

[0096] The monitoring unit can implement a system that monitors the activity levels of elderly individuals and automatically provides first aid if an abnormality is detected. For example, the monitoring unit can implement a system that automatically provides first aid if an elderly person falls. The monitoring unit can also implement a system that automatically provides first aid if an elderly person's heart rate rises abnormally. The monitoring unit can also implement a system that automatically provides first aid if an elderly person's blood pressure drops abnormally. The monitoring unit can implement a system that uses AI to monitor the activity levels of elderly individuals and automatically provides first aid if an abnormality is detected. This makes it possible to automatically provide first aid when an abnormality is detected.

[0097] The monitoring unit can monitor the activity levels of elderly individuals and, if an abnormality is detected, can coordinate with medical institutions to take appropriate action. For example, if an elderly person falls, the monitoring unit will contact a medical institution for assistance. The monitoring unit can also contact a medical institution for assistance if an elderly person's heart rate increases abnormally. The monitoring unit can also contact a medical institution for assistance if an elderly person's blood pressure drops abnormally. The monitoring unit uses AI to monitor the activity levels of elderly individuals and, if an abnormality is detected, can coordinate with medical institutions to take appropriate action. This makes it possible to coordinate with medical institutions to take appropriate action when an abnormality is detected.

[0098] The notification unit can estimate the emotions of elderly individuals and adjust the content and timing of notifications based on those estimated emotions. For example, if an elderly person is feeling anxious, the notification unit will send a reassuring notification. If an elderly person is relaxed, the notification unit can reduce the frequency of notifications and only send important information. If an elderly person is in a hurry, the notification unit can send a notification that allows for a quick response. The notification unit uses AI to estimate the emotions of elderly individuals and adjusts the content and timing of notifications based on those estimated emotions. This makes it possible to adjust the content and timing of notifications according to the emotions of elderly individuals.

[0099] The notification unit can be enhanced to notify not only family members but also nearby caregivers when an abnormality is detected. For example, if an elderly person falls, the notification unit will simultaneously notify family members and nearby caregivers. The notification unit can also simultaneously notify family members and nearby caregivers if an elderly person's heart rate increases abnormally. The notification unit can also simultaneously notify family members and nearby caregivers if an elderly person's blood pressure drops abnormally. The notification unit can be enhanced to notify not only family members but also nearby caregivers when an abnormality is detected using AI. This makes it possible to notify not only family members but also nearby caregivers when an abnormality is detected.

[0100] The notification unit can suggest appropriate countermeasures based on the content of the notification when an abnormality is detected. For example, if an elderly person falls, the notification unit can suggest first aid methods. If an elderly person's heart rate is abnormally elevated, the notification unit can also suggest ways to keep them at rest. If an elderly person's blood pressure is abnormally low, the notification unit can also suggest ways to rehydrate them. The notification unit can use AI to detect abnormalities and suggest appropriate countermeasures based on the content of the notification. This makes it possible to suggest appropriate countermeasures when an abnormality is detected.

[0101] The notification unit can estimate the emotions of elderly individuals and determine notification priorities based on those estimated emotions. For example, if an elderly person is feeling anxious, the notification unit will prioritize important notifications. If an elderly person is relaxed, the notification unit can also lower the priority of notifications. If an elderly person is in a hurry, the notification unit can also prioritize notifications that require a quick response. The notification unit uses AI to estimate the emotions of elderly individuals and determines notification priorities based on those estimated emotions. This makes it possible to prioritize notifications in accordance with the emotions of elderly individuals.

[0102] The notification unit can provide notifications in a format easily understood by the elderly when an abnormality is detected. For example, if an elderly person falls, the notification unit will provide a notification in simple and easy-to-understand language. If an elderly person's heart rate is abnormally elevated, the notification unit can also provide a notification using a visually easy-to-understand graph. If an elderly person's blood pressure is abnormally low, the notification unit can also provide a notification using an illustration. The notification unit can use AI to detect abnormalities and provide notifications in a format easily understood by the elderly. This makes it possible to provide notifications in a format easily understood by the elderly when an abnormality is detected.

[0103] The notification unit can be enhanced with the ability to provide notifications in multiple languages ​​when an abnormality is detected. For example, if an elderly person falls, the notification unit can provide notifications in multiple languages. The notification unit can also provide notifications in multiple languages ​​if an elderly person's heart rate increases abnormally. The notification unit can also provide notifications in multiple languages ​​if an elderly person's blood pressure drops abnormally. The notification unit can be enhanced with the ability to provide notifications in multiple languages ​​when an abnormality is detected using AI. This makes it possible to provide notifications in multiple languages ​​when an abnormality is detected.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The suggestion function can estimate the emotions of elderly individuals and adjust the suggested entertainment content based on those estimated emotions. For example, if an elderly person feels lonely, it can suggest community activities or entertainment that can be enjoyed in groups. If an elderly person is stressed, it can suggest relaxing music or movies. Furthermore, if an elderly person is energetic and active, it can suggest events and activities that they can enjoy going out. This makes it possible to suggest entertainment that is tailored to the emotions of elderly individuals.

[0106] The service provider can estimate the emotions of elderly individuals and adjust the suggested healthy menu based on those emotions. For example, if an elderly person is tired, the service can suggest a menu that is highly nutritious and easy to prepare. If the elderly person is energetic and active, a variety of menus can be suggested. Furthermore, if an elderly person has a poor appetite, menus with an appetizing appearance and aroma can be suggested. This makes it possible to suggest healthy menus that are tailored to the emotions of elderly individuals.

[0107] The monitoring unit can estimate the emotions of elderly individuals and adjust the frequency of monitoring based on those estimated emotions. For example, if an elderly person is feeling anxious, the frequency of monitoring can be increased. If an elderly person is relaxed, the frequency of monitoring can be decreased. Furthermore, if an elderly person is in a hurry, the frequency of monitoring can also be adjusted. This allows for adjustment of the monitoring frequency in accordance with the emotions of the elderly person.

[0108] The notification unit can estimate the emotions of elderly individuals and adjust the content of notifications sent to their families based on those estimates. For example, if an elderly person is feeling lonely, it can send a notification encouraging them to contact their family more frequently. If an elderly person is feeling stressed, it can send a notification suggesting that their family create a relaxing environment. If an elderly person is energetic and active, it can send a notification encouraging their family to go out together. This makes it possible to adjust the content of notifications sent to families according to the emotions of elderly individuals.

[0109] The proposed system can estimate the emotions of elderly individuals and adjust the content of schedule management and reminder functions based on those estimates. For example, if an elderly person is feeling anxious, important appointments can be reminded more frequently. If an elderly person is relaxed, the frequency of reminders can be reduced. Also, if an elderly person is in a hurry, the timing of reminders can be adjusted to allow for a quicker response. This makes it possible to adjust the content of schedule management and reminder functions according to the emotions of elderly individuals.

[0110] The proposal department can analyze the past travel history of elderly individuals and consider information such as local events and festivals when suggesting the most suitable mode of transportation. For example, if a local festival is being held, it can suggest transportation that provides good access to the location. During periods with many local events, it can also recommend the use of public transportation. Furthermore, it can suggest special transportation options for attending specific events. This makes it possible to suggest transportation options tailored to local events and festivals.

[0111] The service provider can analyze the mobility patterns of elderly people, evaluate eco-friendly options for proposed transportation methods, and make optimal recommendations. For example, it can suggest environmentally friendly transportation methods such as electric wheelchairs and electric motorcycles. It can also suggest reducing environmental impact by recommending the use of public transportation. Furthermore, it can suggest walking or cycling for short distances. This enables the proposal of eco-friendly transportation methods.

[0112] The monitoring unit can implement a system that monitors the activity levels of elderly individuals and automatically contacts a medical institution if an abnormality is detected. For example, if an elderly person falls, a system can be implemented to automatically contact a medical institution. If an elderly person's heart rate increases abnormally, a system can be implemented to automatically contact a medical institution. Furthermore, if an elderly person's blood pressure drops abnormally, a system can be implemented to automatically contact a medical institution. This makes it possible to automatically contact a medical institution when an abnormality is detected.

[0113] The notification unit can be enhanced with a function to notify not only family members but also nearby caregivers when an abnormality is detected. For example, if an elderly person falls, both family members and nearby caregivers can be notified simultaneously. If an elderly person's heart rate increases abnormally, both family members and nearby caregivers can be notified simultaneously. Similarly, if an elderly person's blood pressure drops abnormally, both family members and nearby caregivers can be notified simultaneously. This makes it possible to notify not only family members but also nearby caregivers when an abnormality is detected.

[0114] The service provider can collect user feedback on the transportation methods they offer and use it to improve their services. For example, they can collect feedback from elderly people after they use a taxi and use it to improve the service. They can also collect feedback from bus users and use it to improve the operating schedule. Furthermore, they can collect feedback from electric wheelchair users and use it to improve the functionality. In this way, collecting user feedback makes it possible to improve services.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The proposal department analyzes the elderly person's travel patterns and suggests the most suitable mode of transportation. The proposal department analyzes past travel history and uses AI to analyze the elderly person's travel patterns and suggests the most suitable mode of transportation. Step 2: The provisioning department provides the transportation methods proposed by the proposaling department. The provisioning department can provide transportation methods such as taxis, buses, and electric wheelchairs, and can also provide transportation methods proposed using AI. Step 3: The monitoring unit monitors the usage of transportation provided by the service provider. The monitoring unit can monitor the activity levels of elderly individuals and use AI to monitor the usage of transportation provided. Step 4: The notification unit detects anomalies based on usage patterns monitored by the monitoring unit and notifies the family. The notification unit can notify the family when an anomaly is detected, and can use AI to detect anomalies and notify the family.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the proposal unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14, which analyzes the elderly person's movement patterns and proposes the optimal mode of transportation. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides the proposed mode of transportation. The monitoring unit monitors the usage status of the provided mode of transportation using the camera 42 and sensors of the smart device 14, for example. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, which detects abnormalities and notifies the family. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the suggestion unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the suggestion unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the elderly person's movement patterns and suggests the optimal mode of transportation. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides the suggested mode of transportation. The monitoring unit monitors the usage status of the provided mode of transportation using the camera 42 and sensors of the smart glasses 214, for example. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, which detects abnormalities and notifies the family. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the proposal unit, provision unit, monitoring unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the elderly person's movement patterns and proposes the optimal mode of transportation. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides the proposed mode of transportation. The monitoring unit monitors the usage status of the provided mode of transportation using the camera 42 and sensors of the headset terminal 314, for example. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, which detects abnormalities and notifies the family. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the proposal unit, provision unit, monitoring unit, and notification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the robot 414, which analyzes the elderly person's movement patterns and proposes the optimal means of transportation. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides the proposed means of transportation. The monitoring unit monitors the usage status of the provided means of transportation using, for example, the camera 42 and sensors of the robot 414. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which detects abnormalities and notifies the family. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) The proposal department analyzes the mobility patterns of the elderly and suggests the most suitable means of transportation, A provision unit that provides the means of transportation proposed by the aforementioned proposal unit, A monitoring unit that monitors the usage status of the means of transport provided by the aforementioned provision unit, The system includes a notification unit that detects abnormalities based on usage status monitored by the monitoring unit and notifies family members. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We analyze your past travel history and suggest the most suitable mode of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide transportation options such as taxis, buses, and electric wheelchairs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The monitoring unit, Monitoring the activity levels of the elderly The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, If an abnormality is detected, the family will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The system estimates the emotions of elderly people and adjusts the suggested transportation options based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, When analyzing the past travel history of elderly individuals and suggesting the most suitable mode of transportation, seasonal and weather variations should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We analyze the mobility patterns of the elderly, evaluate the cost-effectiveness of proposed transportation methods, and make optimal recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, The system estimates the emotions of elderly individuals and determines the priority of suggested modes of transportation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, We analyze the mobility patterns of the elderly, evaluate the environmental impact of proposed transportation methods, and make optimal recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, We analyze the mobility patterns of the elderly, evaluate the safety of proposed transportation methods, and make optimal recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, The system estimates the emotions of older adults and adjusts the types of transportation provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, We monitor the usage of the transportation services we provide in real time and adjust the provision of transportation as needed. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, We monitor the maintenance status of the transportation services we provide and perform maintenance at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, The system estimates the emotions of elderly individuals and adjusts the timing of transportation provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, We collect user feedback on the transportation methods we provide to help improve our services. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We monitor the health status of users of the transportation services we provide and offer health support as needed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, The system estimates the emotions of older adults and adjusts the monitoring frequency based on the estimated emotions of the older adults. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, Optimizing algorithms to monitor the activity levels of older adults and detect abnormal patterns. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, The system monitors the activity levels of elderly individuals, learns their daily activity patterns, and detects abnormalities early. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, The system estimates the emotions of older adults and adjusts the monitoring target based on the estimated emotions of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, We will introduce a system that monitors the activity levels of elderly people and automatically provides emergency treatment if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, We monitor the activity levels of elderly individuals and collaborate with medical institutions to address any abnormalities detected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, The system estimates the emotions of elderly individuals and adjusts the content and timing of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, We will add a feature that notifies not only family members but also nearby caregivers if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, If an anomaly is detected, appropriate countermeasures will be proposed according to the content of the notification. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, The system estimates the emotions of older adults and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, If an abnormality is detected, the notification will be provided in a format that is easy for elderly people to understand. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, Add a feature to provide notifications in multiple languages ​​when an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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. The proposal department analyzes the mobility patterns of the elderly and suggests the most suitable means of transportation, A provision unit that provides the means of transportation proposed by the aforementioned proposal unit, A monitoring unit that monitors the usage status of the means of transport provided by the aforementioned provision unit, The system includes a notification unit that detects abnormalities based on usage status monitored by the monitoring unit and notifies family members. A system characterized by the following features.

2. The aforementioned proposal section is, We analyze your past travel history and suggest the most suitable mode of transportation. The system according to feature 1.

3. The aforementioned supply unit is, We provide transportation options such as taxis, buses, and electric wheelchairs. The system according to feature 1.

4. The monitoring unit, Monitoring the activity levels of elderly people The system according to feature 1.

5. The aforementioned notification unit, If an abnormality is detected, the family will be notified. The system according to feature 1.

6. The aforementioned proposal section is, The system estimates the emotions of elderly people and adjusts the suggested transportation options based on those estimated emotions. The system according to feature 1.

7. The aforementioned proposal section is, When analyzing the past travel history of elderly individuals and suggesting the most suitable mode of transportation, seasonal and weather variations should be taken into consideration. The system according to feature 1.

8. The aforementioned proposal section is, We analyze the mobility patterns of the elderly, evaluate the cost-effectiveness of proposed transportation methods, and make optimal recommendations. The system according to feature 1.

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