system

Abstract

The system according to this embodiment aims to efficiently provide support for people with disabilities to travel safely to their destinations. [Solution] The system according to the embodiment comprises a reception unit, a route search unit, an assignment unit, and a support provision unit. The reception unit receives requests from users. The route search unit searches for a route based on the information received by the reception unit. The assignment unit assigns support based on the route search unit. The support provision unit provides the support assigned by the assignment unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to efficiently provide support for people with disabilities to move safely to their destinations.

[0005] The system according to the embodiment aims to efficiently provide support for people with disabilities to move safely to their destinations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a route search unit, an assignment unit, and a support provision unit. The reception unit receives requests from users. The route search unit searches for a route based on the information received by the reception unit. The assignment unit assigns support based on the route search unit. The support provision unit provides the support assigned by the assignment unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide support for people with disabilities to travel safely to their destinations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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 mobility support system according to an embodiment of the present invention is a system that supports people with disabilities so that they can travel safely to their destination. When a user requests to travel to a station, the AI ​​agent searches for a route to that destination. At the same time, it assigns people who can provide support according to the type of disability that has been registered in advance. In addition to the current methods of voice guidance on smartphones and movement using tactile paving, this system assigns the most suitable personnel when assistance is needed, such as when getting on and off trains, to support safe travel to the destination. First, the user requests to travel to a station. At this time, the user only needs to input the departure point and destination. For example, the user might input, "I want to go from my home to the station." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and searches for the optimal route from the current location to the destination. The AI ​​agent calculates the optimal route based on map data and generates guidance along that route. For example, if the user inputs a route from their home to the station, guidance along that route will be generated. Furthermore, the AI ​​agent assigns people who can provide support according to the type of disability that has been registered in advance. For example, in the case of a visually impaired person, visual support is required, so people who can provide that support are assigned. Similarly, in the case of a hearing impaired person, auditory support is required, so people who can provide that support are assigned. This system allows users to travel safely to their destination. For example, if boarding or alighting from a train is necessary, people who can provide support are assigned, allowing the user to travel with peace of mind. Route guidance to the destination is also provided, so users can reach their destination without getting lost. This system supports people with disabilities in living social lives just like able-bodied people, and can provide optimal support to users who have difficulty achieving their goals on their own. For example, when a visually impaired person is traveling to a train station, an AI agent will guide them along the optimal route, and if visual support is needed, people who can provide that support will be assigned, allowing the visually impaired person to travel safely.Furthermore, when hearing-impaired individuals travel to their destination, the AI ​​agent guides them along the optimal route and assigns individuals who can provide auditory support if needed, ensuring their safe journey. In this way, by utilizing the AI ​​agent, a system can be provided that supports people with disabilities in safely traveling to their destinations. Thus, the mobility assistance system enables people with disabilities to travel safely to their destinations.

[0029] The mobility support system according to this embodiment comprises a reception unit, a route search unit, an assignment unit, and a support provision unit. The reception unit receives requests from users. User requests include, for example, information on the departure point and destination, but are not limited to such examples. The reception unit receives requests from users, for example, through a smartphone application. The reception unit can also receive requests via voice input using voice recognition technology. For example, if a user requests by voice, "I want to go to XX station," the reception unit recognizes the voice and analyzes the content of the request. Furthermore, the reception unit can also receive requests via text input. For example, if a user enters the departure point and destination in text, the reception unit analyzes the text and understands the content of the request. The route search unit searches for a route based on the information received by the reception unit. The route search unit calculates the optimal route based on map data, for example. The route search unit searches for the shortest route from the current location to the destination by referring to a map database, for example. The route search unit can also optimize the route by considering traffic conditions and weather information. For example, it proposes the optimal route based on real-time traffic congestion information. Furthermore, the route search unit can calculate the optimal route based on the user's type of disability. For example, for visually impaired users, it prioritizes routes with ample tactile paving and audio guidance. The assignment unit assigns support based on the route search unit. The assignment unit assigns support according to the type of disability registered in advance, for example. For example, in the case of a visually impaired person, the assignment unit assigns people who can support visually impaired people, as visual support is required. Similarly, for a hearing impaired person, the assignment unit can assign people who can support hearing impaired people, as auditory support is required. In addition, the assignment unit can assign support considering the user's current situation and location information. For example, if the user needs support in a location close to their current location, it assigns the most suitable support provider. The support provision unit provides the support assigned by the assignment unit. For example, the support provision unit provides support such as getting on and off trains.The support unit can, for example, provide visual support to a visually impaired person when they board a train. It can also provide auditory support to a hearing-impaired person when they board a train. Furthermore, the support unit may include a monitoring unit to track the progress of the support. For example, the support unit can monitor the progress of the support in real time and adjust the support content as needed. This allows the mobility assistance system according to this embodiment to enable people with disabilities to travel safely to their destinations.

[0030] The reception desk receives requests from users. These requests may include, but are not limited to, information about the departure and destination locations. The reception desk may, for example, receive requests through a smartphone app. The smartphone app has an easy-to-use interface, including input fields for departure and destination locations, as well as a voice input button. When a user launches the app and enters their departure and destination locations, this information is immediately sent to the reception desk. The reception desk can also accept requests via voice input using speech recognition technology. For example, if a user says, "I want to go to XX station," the reception desk recognizes the voice and analyzes the request. Speech recognition technology converts the user's speech into text data and analyzes that text data to identify the departure and destination locations. Furthermore, the reception desk can also accept requests via text input. For example, if a user enters their departure and destination locations in text, the reception desk analyzes the text and understands the request. Text input is a method where the user uses a keyboard to input information, and is particularly effective in environments or situations where voice input is difficult. This allows the reception department to handle diverse request methods from users and accept requests flexibly. Furthermore, the reception department provides a foundation for centrally managing user requests and processing them appropriately. For example, the reception department stores received requests in a database, making them accessible to subsequent processing departments. This enables the reception department to receive user requests quickly and accurately, improving the overall efficiency of the system.

[0031] The route search unit searches for a route based on the information received by the reception unit. For example, the route search unit calculates the optimal route based on map data. Map data includes detailed road information and traffic regulation information, and the route search unit utilizes this information to calculate the optimal route. For example, the route search unit refers to a map database to search for the shortest route from the current location to the destination. Route search algorithms such as Dijkstra's algorithm and A* algorithm are used to calculate the shortest route. The route search unit can also optimize the route by considering traffic conditions and weather information. For example, it can suggest the optimal route based on real-time traffic congestion information. Traffic congestion information is collected from traffic sensors and cameras and updated in real time. The route search unit takes this information into account and calculates a route that avoids congestion. Furthermore, the route search unit can also calculate the optimal route based on the type of disability the user has. For example, it prioritizes routes with ample tactile paving and audio guidance for visually impaired users. The location information of tactile paving and the installation locations of audio guidance are registered in the map database, and the route search unit calculates the route considering this information. This allows the route search unit to provide the optimal route tailored to the user's needs, improving the safety and comfort of travel. Furthermore, the route search unit is equipped with an interface for presenting search results to the user, allowing for visual display of the results. For example, the searched route can be displayed on a map on the smartphone app screen, allowing the user to confirm the route. In this way, the route search unit can provide users with intuitive and easy-to-understand route guidance, improving the convenience of travel.

[0032] The assignment unit assigns support based on the route search unit. The assignment unit assigns support according to, for example, the type of disability registered in advance. For instance, in the case of a visually impaired person, the assignment unit assigns people who can provide visual support, as visual support is necessary. Visual support includes explaining the surroundings and guiding the visually impaired person to ensure safe movement. Similarly, in the case of a hearing-impaired person, the assignment unit can assign people who can provide auditory support, as auditory support is necessary. Auditory support includes communication assistance through sign language interpretation or written communication. Furthermore, the assignment unit can assign support considering the user's current situation and location. For example, if the user needs support near their current location, the assignment unit assigns the most suitable support provider. The assignment unit manages the location and schedule of support providers in real time, enabling it to quickly assign the most suitable support provider. This allows the assignment unit to provide users with prompt and appropriate support, improving the safety and comfort of their journey. Additionally, the assignment unit can provide optimal support by considering the skills and experience of the support providers. For example, to support visually impaired individuals, support providers who have received specialized training in assisting the mobility of visually impaired people are assigned. This allows the assignment department to provide users with high-quality support, improving the safety and comfort of their travel.

[0033] The support provision department provides the support assigned by the assignment department. For example, the support provision department provides support such as getting on and off trains. For example, the support provision department provides visual support when a visually impaired person is getting on a train. Visual support includes holding the person's hand and guiding them when getting on and off the train, and explaining the surrounding situation. The support provision department can also provide auditory support when a hearing impaired person is getting on a train. Auditory support includes communication support through sign language interpretation or written communication. Furthermore, the support provision department may have a monitoring department to monitor the progress of the support. For example, the support provision department monitors the progress of the support in real time and adjusts the support content as needed. The monitoring department grasps the location information of the support provider and the progress of the support in real time and checks whether the support is progressing smoothly. This allows the support provision department to maintain the quality of support and provide high-quality support to users. Furthermore, the support provision department can collect feedback from users and use it to improve the support content. For example, it can collect evaluations and opinions from users after providing support and reflect them in the training of support providers and the review of support content. This allows the support department to continuously improve the quality of support and provide better service to users. Furthermore, the support department can efficiently manage the schedules and resources of support providers, providing a foundation for delivering optimal support. As a result, the support department can provide users with prompt and appropriate support, improving the safety and comfort of their travel.

[0034] The support provision unit may include a monitoring unit to monitor the progress of support. The monitoring unit may, for example, monitor the progress of support in real time. The monitoring unit may monitor progress based on, for example, the location information and work status of the support provider. The monitoring unit may also monitor progress based on feedback from the support provider. For example, it may monitor progress based on information when the support provider reports on the progress. Furthermore, the monitoring unit may estimate the user's emotions and adjust the progress monitoring method based on the estimated user emotions. For example, if the user is feeling anxious, it may report progress more frequently. This allows the user to move forward with peace of mind by monitoring the progress of support.

[0035] The support provision unit may include a recording unit for recording the results of the support. The recording unit may, for example, record the results of the support in detail. The recording unit may, for example, record the results of the support based on feedback from the support provider. Furthermore, the recording unit may estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is feeling anxious, a detailed record may be kept for later review. This allows for later review and improvement by recording the results of the support.

[0036] The route search unit can calculate the optimal route based on map data. For example, it can refer to a map database to find the shortest route from the current location to the destination. The route search unit can also optimize the route by considering factors such as traffic conditions and weather information. For example, it can suggest the optimal route based on real-time traffic congestion information. Furthermore, the route search unit can calculate the optimal route based on the type of disability the user has. For example, it can prioritize routes with ample tactile paving and audio guidance for visually impaired users. By calculating the optimal route based on map data, the user can travel more efficiently.

[0037] The assignment unit can assign support according to the type of disability registered in advance. For example, if a user is visually impaired, the assignment unit will assign people who can support visually impaired individuals, as they require visual support. Similarly, if a user is hearing impaired, the assignment unit can assign people who can support hearing impaired individuals, as they require auditory support. Furthermore, the assignment unit can also assign support considering the user's current situation and location information. For example, if a user needs support in a location close to their current location, the assignment unit will assign the most suitable support provider. This ensures that appropriate support is provided by assigning support according to the type of disability.

[0038] The support service can provide assistance with boarding and alighting from trains. For example, the support service can provide visual assistance to visually impaired passengers when they board trains. It can also provide auditory assistance to hearing-impaired passengers when they board trains. Furthermore, the support service can provide barrier-free assistance to wheelchair users when they board trains. For example, the support service can assist wheelchair users in using elevators and ramps to ensure smooth boarding and alighting. By providing assistance with boarding and alighting from trains, users can travel safely.

[0039] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the user has frequently made in the past as suggestions. For example, the reception desk can prioritize suggesting reception methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest requests that the user will use during specific time periods based on their past request history. This improves user convenience by providing the most suitable reception method based on past request history.

[0040] The reception desk can filter requests based on the user's current situation and type of disability. For example, if a user has a visual impairment, the reception desk will prioritize providing audio guidance. If a user has a hearing impairment, the reception desk will prioritize providing text guidance. The reception desk can also filter people who can provide the most suitable support based on the user's current location. This ensures that appropriate support is provided by filtering according to the user's situation and type of disability.

[0041] The reception desk can prioritize requests based on the user's geographical location when receiving requests. For example, it can prioritize requests for travel to locations close to the user's current location. If the user is in a specific area, it can prioritize requests related to that area. The reception desk can also prioritize assigning people who can provide the most suitable support based on the user's location. In this way, by considering the user's geographical location, it is possible to prioritize requests that are highly relevant.

[0042] The reception department can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception department can understand the user's current situation and needs from their social media posts and prioritize accepting relevant requests. For example, the reception department can prioritize accepting requests related to specific events based on the user's social media activity. The reception department can also analyze a user's social media activity and assign people who can provide the most suitable support. This allows for the prioritization of relevant requests by analyzing the user's social media activity.

[0043] The route search unit can calculate the optimal route based on the type of disability during route searching. For example, for visually impaired users, the route search unit prioritizes routes with ample tactile paving and audio guidance. For hearing impaired users, the route search unit prioritizes routes with ample visual guidance. The route search unit can also prioritize barrier-free routes for wheelchair users. This allows users to travel safely by providing the optimal route according to the type of disability.

[0044] The route search unit can optimize routes by considering traffic conditions and weather information during route searches. For example, the route search unit can suggest the optimal route based on real-time traffic congestion information. For example, the route search unit can suggest a route with a roof in rainy weather based on real-time weather information. Furthermore, the route search unit can also suggest the optimal route by considering the real-time operating status of public transportation. In this way, it can provide the best route by considering traffic conditions and weather information.

[0045] The route search unit can suggest the optimal route by referring to the user's past travel history during route searching. For example, the route search unit can suggest the optimal route based on routes the user has used in the past. For example, the route search unit can suggest a route that avoids congestion based on the user's past travel history. Furthermore, the route search unit can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by providing the optimal route based on past travel history.

[0046] The route search unit can provide the optimal route by considering the user's device information during route searching. For example, if the user is using a smartphone, the route search unit provides route guidance that is adapted to the screen size. If the user is using a tablet, the route search unit provides route guidance optimized for a larger screen. Furthermore, if the user is using a smartwatch, the route search unit can provide concise and highly visible route guidance. In this way, by considering the user's device information, the optimal route guidance can be provided.

[0047] The assignment department can select the most suitable personnel during the assignment process, taking into account the skills and experience of the support providers. For example, the assignment department can select a support provider skilled in visual support for a visually impaired person. For example, the assignment department can select a support provider skilled in auditory support for a hearing impaired person. Furthermore, the assignment department can select a support provider with knowledge of barrier-free design for a wheelchair user. By considering the skills and experience of the support providers, appropriate support can be provided.

[0048] The assignment unit can make assignments while considering the current status and location information of support providers. For example, the assignment unit can assign the nearest support provider based on the support provider's current location information. The assignment unit can also make assignments while considering the support provider's current status (such as availability). Furthermore, the assignment unit can make assignments at the optimal time by considering the support provider's current task status. This ensures that support is provided quickly and appropriately by considering the support provider's current status and location information.

[0049] The assignment unit can select the most suitable support provider by referring to the user's past support history during the assignment process. For example, the assignment unit can prioritize selecting support providers the user has used in the past. For example, the assignment unit can select a specific support provider based on the user's past support history. The assignment unit can also analyze the user's past support history and select the most suitable support provider. This improves user convenience by selecting the most suitable support provider based on past support history.

[0050] The assignment department can analyze the social media activity of support providers during the assignment process and provide relevant support. For example, the assignment department can understand the current situation and skills of support providers from their social media activity and provide optimal support. For example, the assignment department can provide support related to a specific event based on the support provider's social media activity. Furthermore, the assignment department can analyze the support provider's social media activity and provide support at the optimal time. In this way, by analyzing the support provider's social media activity, optimal support can be provided.

[0051] The support department can provide optimal support by referring to the user's past support history when providing support. For example, the support department can provide optimal support based on the support the user has used in the past. For example, the support department can prioritize providing specific support based on the user's past support history. Furthermore, the support department can analyze the user's past support history and provide the most appropriate support. This improves user convenience by providing optimal support based on past support history.

[0052] The support department can customize support based on the user's current situation and type of disability. For example, if a user has a visual impairment, the support department will prioritize providing visual support. If a user has a hearing impairment, the support department will prioritize providing auditory support. The support department can also provide optimal support based on the user's current situation (such as location information). This ensures that appropriate support is provided by tailoring support to the user's situation and type of disability.

[0053] The support department can provide optimal support by considering the user's geographical location. For example, if the user needs support in a location close to their current location, the support department will provide the most suitable support. For example, if the user is in a specific area, the support department will provide support relevant to that area. The support department can also assign the most suitable people to provide support based on the user's location. This allows for the provision of optimal support by considering the user's geographical location.

[0054] The support department can analyze a user's social media activity and provide relevant support when providing support. For example, the support department can understand the user's current situation and needs from their social media posts and provide relevant support. For example, the support department can provide support related to a specific event based on the user's social media activity. Furthermore, the support department can analyze a user's social media activity and provide support at the optimal time. This allows for the provision of optimal support by analyzing the user's social media activity.

[0055] The monitoring unit can perform monitoring while considering the support provider's current status and location information. For example, the monitoring unit can monitor progress at the optimal time based on the support provider's current location information. The monitoring unit can also monitor while considering the support provider's current status (such as free time). Furthermore, the monitoring unit can monitor progress at the optimal time by considering the support provider's current task status. This allows for monitoring progress at the appropriate time by considering the support provider's current status and location information.

[0056] The monitoring unit can select the optimal monitoring method by referring to the user's past support history during monitoring. For example, the monitoring unit can select the optimal monitoring method based on the support the user has used in the past. For example, the monitoring unit can prioritize selecting a specific monitoring method based on the user's past support history. The monitoring unit can also analyze the user's past support history and select the most suitable monitoring method. This improves user convenience by providing the optimal monitoring method based on past support history.

[0057] The recording unit can take support provider feedback into consideration when recording. For example, the recording unit can create detailed records based on support provider feedback. For example, the recording unit can prioritize recording specific support content based on support provider feedback. The recording unit can also analyze support provider feedback and record the most important information. This allows for detailed and appropriate records to be made by considering support provider feedback.

[0058] The recording unit can select the optimal recording method by referring to the user's past support history during recording. For example, the recording unit can select the optimal recording method based on the support content the user has used in the past. For example, the recording unit can prioritize selecting a specific recording method from the user's past support history. The recording unit can also analyze the user's past support history and select the most suitable recording method. This improves user convenience by providing the optimal recording method based on past support history.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The mobility assistance system can suggest the optimal route by referring to the user's past travel history. For example, it can suggest the best route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by providing the optimal route based on past travel history.

[0061] Mobility assistance systems can provide optimal route guidance by considering the user's device information. For example, if the user is using a smartphone, the system can provide route guidance optimized for the screen size. If the user is using a tablet, it can provide route guidance optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide concise and highly visible route guidance. In this way, by considering the user's device information, the system can provide optimal route guidance.

[0062] Mobility assistance systems can select the most suitable personnel by considering the skills and experience of support providers. For example, for visually impaired individuals, support providers skilled in visual support can be selected. Similarly, for hearing-impaired individuals, support providers skilled in auditory support can be selected. Furthermore, for wheelchair users, support providers with knowledge of barrier-free design can be selected. This ensures that appropriate support is provided by considering the skills and experience of the support providers.

[0063] The mobility assistance system can prioritize requests based on the user's geographical location. For example, it can prioritize requests for travel to locations close to the user's current location. It can also prioritize requests related to a specific area if the user is in that area. Furthermore, it can prioritize assigning the most suitable support personnel based on the user's location. This allows for the prioritization of highly relevant requests by considering the user's geographical location.

[0064] The mobility assistance system can analyze a user's social media activity and accept relevant requests. For example, it can understand the user's current situation and needs from their social media posts and prioritize requests related to those situations. It can also prioritize requests related to specific events based on the user's social media activity. Furthermore, it can analyze the user's social media activity and assign people who can provide the most suitable support. In this way, by analyzing the user's social media activity, it can prioritize requests related to those situations.

[0065] The mobility assistance system can provide optimal support by referring to the user's past support history. For example, it can provide optimal support based on the support the user has received in the past. It can also prioritize providing specific support based on the user's past support history. Furthermore, it can analyze the user's past support history and provide the most suitable support. This improves user convenience by providing optimal support based on past support history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives requests from users. User requests may include, for example, information about the departure and destination locations, but are not limited to these examples. The reception desk may receive requests from users, for example, through a smartphone app. The reception desk can also accept requests via voice input using speech recognition technology. For example, if a user requests by voice, "I want to go to XX station," the reception desk will recognize the voice and analyze the request. Furthermore, the reception desk can also accept requests via text input. For example, if a user enters the departure and destination locations in text, the reception desk will analyze the text and understand the request. Step 2: The route search unit searches for a route based on the information received by the reception unit. The route search unit calculates the optimal route based on map data, for example. The route search unit searches for the shortest route from the current location to the destination by referring to a map database, for example. The route search unit can also optimize the route by considering traffic conditions and weather information. For example, it suggests the optimal route based on real-time traffic congestion information. Furthermore, the route search unit can calculate the optimal route based on the type of disability the user has. For example, it prioritizes routes with ample tactile paving and audio guidance for visually impaired users. Step 3: The assignment unit assigns support based on the route search unit. The assignment unit assigns support according to the type of disability that has been registered in advance. For example, if a user is visually impaired, the assignment unit will assign people who can support visually impaired people, as visual support is required. Similarly, if a user is hearing impaired, the assignment unit can assign people who can support hearing impaired people, as auditory support is required. Furthermore, the assignment unit can also assign support considering the user's current situation and location information. For example, if the user needs support in a location close to their current location, the assignment unit will assign the most suitable support provider. Step 4: The support provision unit provides the support assigned by the assignment unit. The support provision unit provides support such as getting on and off trains. For example, the support provision unit provides visual support when a visually impaired person is getting on a train. The support provision unit can also provide auditory support when a hearing impaired person is getting on a train. Furthermore, the support provision unit may be equipped with a monitoring unit to monitor the progress of the support. For example, the support provision unit monitors the progress of the support in real time and adjusts the support content as needed.

[0068] (Example of form 2) The mobility support system according to an embodiment of the present invention is a system that supports people with disabilities so that they can travel safely to their destination. When a user requests to travel to a station, the AI ​​agent searches for a route to that destination. At the same time, it assigns people who can provide support according to the type of disability that has been registered in advance. In addition to the current methods of voice guidance on smartphones and movement using tactile paving, this system assigns the most suitable personnel when assistance is needed, such as when getting on and off trains, to support safe travel to the destination. First, the user requests to travel to a station. At this time, the user only needs to input the departure point and destination. For example, the user might input, "I want to go from my home to the station." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information and searches for the optimal route from the current location to the destination. The AI ​​agent calculates the optimal route based on map data and generates guidance along that route. For example, if the user inputs a route from their home to the station, guidance along that route will be generated. Furthermore, the AI ​​agent assigns people who can provide support according to the type of disability that has been registered in advance. For example, in the case of a visually impaired person, visual support is required, so people who can provide that support are assigned. Similarly, in the case of a hearing impaired person, auditory support is required, so people who can provide that support are assigned. This system allows users to travel safely to their destination. For example, if boarding or alighting from a train is necessary, people who can provide support are assigned, allowing the user to travel with peace of mind. Route guidance to the destination is also provided, so users can reach their destination without getting lost. This system supports people with disabilities in living social lives just like able-bodied people, and can provide optimal support to users who have difficulty achieving their goals on their own. For example, when a visually impaired person is traveling to a train station, an AI agent will guide them along the optimal route, and if visual support is needed, people who can provide that support will be assigned, allowing the visually impaired person to travel safely.Furthermore, when hearing-impaired individuals travel to their destination, the AI ​​agent guides them along the optimal route and assigns individuals who can provide auditory support if needed, ensuring their safe journey. In this way, by utilizing the AI ​​agent, a system can be provided that supports people with disabilities in safely traveling to their destinations. Thus, the mobility assistance system enables people with disabilities to travel safely to their destinations.

[0069] The mobility support system according to this embodiment comprises a reception unit, a route search unit, an assignment unit, and a support provision unit. The reception unit receives requests from users. User requests include, for example, information on the departure point and destination, but are not limited to such examples. The reception unit receives requests from users, for example, through a smartphone application. The reception unit can also receive requests via voice input using voice recognition technology. For example, if a user requests by voice, "I want to go to XX station," the reception unit recognizes the voice and analyzes the content of the request. Furthermore, the reception unit can also receive requests via text input. For example, if a user enters the departure point and destination in text, the reception unit analyzes the text and understands the content of the request. The route search unit searches for a route based on the information received by the reception unit. The route search unit calculates the optimal route based on map data, for example. The route search unit searches for the shortest route from the current location to the destination by referring to a map database, for example. The route search unit can also optimize the route by considering traffic conditions and weather information. For example, it proposes the optimal route based on real-time traffic congestion information. Furthermore, the route search unit can calculate the optimal route based on the user's type of disability. For example, for visually impaired users, it prioritizes routes with ample tactile paving and audio guidance. The assignment unit assigns support based on the route search unit. The assignment unit assigns support according to the type of disability registered in advance, for example. For example, in the case of a visually impaired person, the assignment unit assigns people who can support visually impaired people, as visual support is required. Similarly, for a hearing impaired person, the assignment unit can assign people who can support hearing impaired people, as auditory support is required. In addition, the assignment unit can assign support considering the user's current situation and location information. For example, if the user needs support in a location close to their current location, it assigns the most suitable support provider. The support provision unit provides the support assigned by the assignment unit. For example, the support provision unit provides support such as getting on and off trains.The support unit can, for example, provide visual support to a visually impaired person when they board a train. It can also provide auditory support to a hearing-impaired person when they board a train. Furthermore, the support unit may include a monitoring unit to track the progress of the support. For example, the support unit can monitor the progress of the support in real time and adjust the support content as needed. This allows the mobility assistance system according to this embodiment to enable people with disabilities to travel safely to their destinations.

[0070] The reception desk receives requests from users. These requests may include, but are not limited to, information about the departure and destination locations. The reception desk may, for example, receive requests through a smartphone app. The smartphone app has an easy-to-use interface, including input fields for departure and destination locations, as well as a voice input button. When a user launches the app and enters their departure and destination locations, this information is immediately sent to the reception desk. The reception desk can also accept requests via voice input using speech recognition technology. For example, if a user says, "I want to go to XX station," the reception desk recognizes the voice and analyzes the request. Speech recognition technology converts the user's speech into text data and analyzes that text data to identify the departure and destination locations. Furthermore, the reception desk can also accept requests via text input. For example, if a user enters their departure and destination locations in text, the reception desk analyzes the text and understands the request. Text input is a method where the user uses a keyboard to input information, and is particularly effective in environments or situations where voice input is difficult. This allows the reception department to handle diverse request methods from users and accept requests flexibly. Furthermore, the reception department provides a foundation for centrally managing user requests and processing them appropriately. For example, the reception department stores received requests in a database, making them accessible to subsequent processing departments. This enables the reception department to receive user requests quickly and accurately, improving the overall efficiency of the system.

[0071] The route search unit searches for a route based on the information received by the reception unit. For example, the route search unit calculates the optimal route based on map data. Map data includes detailed road information and traffic regulation information, and the route search unit utilizes this information to calculate the optimal route. For example, the route search unit refers to a map database to search for the shortest route from the current location to the destination. Route search algorithms such as Dijkstra's algorithm and A* algorithm are used to calculate the shortest route. The route search unit can also optimize the route by considering traffic conditions and weather information. For example, it can suggest the optimal route based on real-time traffic congestion information. Traffic congestion information is collected from traffic sensors and cameras and updated in real time. The route search unit takes this information into account and calculates a route that avoids congestion. Furthermore, the route search unit can also calculate the optimal route based on the type of disability the user has. For example, it prioritizes routes with ample tactile paving and audio guidance for visually impaired users. The location information of tactile paving and the installation locations of audio guidance are registered in the map database, and the route search unit calculates the route considering this information. This allows the route search unit to provide the optimal route tailored to the user's needs, improving the safety and comfort of travel. Furthermore, the route search unit is equipped with an interface for presenting search results to the user, allowing for visual display of the results. For example, the searched route can be displayed on a map on the smartphone app screen, allowing the user to confirm the route. In this way, the route search unit can provide users with intuitive and easy-to-understand route guidance, improving the convenience of travel.

[0072] The assignment unit assigns support based on the route search unit. The assignment unit assigns support according to, for example, the type of disability registered in advance. For instance, in the case of a visually impaired person, the assignment unit assigns people who can provide visual support, as visual support is necessary. Visual support includes explaining the surroundings and guiding the visually impaired person to ensure safe movement. Similarly, in the case of a hearing-impaired person, the assignment unit can assign people who can provide auditory support, as auditory support is necessary. Auditory support includes communication assistance through sign language interpretation or written communication. Furthermore, the assignment unit can assign support considering the user's current situation and location. For example, if the user needs support near their current location, the assignment unit assigns the most suitable support provider. The assignment unit manages the location and schedule of support providers in real time, enabling it to quickly assign the most suitable support provider. This allows the assignment unit to provide users with prompt and appropriate support, improving the safety and comfort of their journey. Additionally, the assignment unit can provide optimal support by considering the skills and experience of the support providers. For example, to support visually impaired individuals, support providers who have received specialized training in assisting the mobility of visually impaired people are assigned. This allows the assignment department to provide users with high-quality support, improving the safety and comfort of their travel.

[0073] The support provision department provides the support assigned by the assignment department. For example, the support provision department provides support such as getting on and off trains. For example, the support provision department provides visual support when a visually impaired person is getting on a train. Visual support includes holding the person's hand and guiding them when getting on and off the train, and explaining the surrounding situation. The support provision department can also provide auditory support when a hearing impaired person is getting on a train. Auditory support includes communication support through sign language interpretation or written communication. Furthermore, the support provision department may have a monitoring department to monitor the progress of the support. For example, the support provision department monitors the progress of the support in real time and adjusts the support content as needed. The monitoring department grasps the location information of the support provider and the progress of the support in real time and checks whether the support is progressing smoothly. This allows the support provision department to maintain the quality of support and provide high-quality support to users. Furthermore, the support provision department can collect feedback from users and use it to improve the support content. For example, it can collect evaluations and opinions from users after providing support and reflect them in the training of support providers and the review of support content. This allows the support department to continuously improve the quality of support and provide better service to users. Furthermore, the support department can efficiently manage the schedules and resources of support providers, providing a foundation for delivering optimal support. As a result, the support department can provide users with prompt and appropriate support, improving the safety and comfort of their travel.

[0074] The support provision unit may include a monitoring unit to monitor the progress of support. The monitoring unit may, for example, monitor the progress of support in real time. The monitoring unit may monitor progress based on, for example, the location information and work status of the support provider. The monitoring unit may also monitor progress based on feedback from the support provider. For example, it may monitor progress based on information when the support provider reports on the progress. Furthermore, the monitoring unit may estimate the user's emotions and adjust the progress monitoring method based on the estimated user emotions. For example, if the user is feeling anxious, it may report progress more frequently. This allows the user to move forward with peace of mind by monitoring the progress of support.

[0075] The support provision unit may include a recording unit for recording the results of the support. The recording unit may, for example, record the results of the support in detail. The recording unit may, for example, record the results of the support based on feedback from the support provider. Furthermore, the recording unit may estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is feeling anxious, a detailed record may be kept for later review. This allows for later review and improvement by recording the results of the support.

[0076] The route search unit can calculate the optimal route based on map data. For example, it can refer to a map database to find the shortest route from the current location to the destination. The route search unit can also optimize the route by considering factors such as traffic conditions and weather information. For example, it can suggest the optimal route based on real-time traffic congestion information. Furthermore, the route search unit can calculate the optimal route based on the type of disability the user has. For example, it can prioritize routes with ample tactile paving and audio guidance for visually impaired users. By calculating the optimal route based on map data, the user can travel more efficiently.

[0077] The assignment unit can assign support according to the type of disability registered in advance. For example, if a user is visually impaired, the assignment unit will assign people who can support visually impaired individuals, as they require visual support. Similarly, if a user is hearing impaired, the assignment unit can assign people who can support hearing impaired individuals, as they require auditory support. Furthermore, the assignment unit can also assign support considering the user's current situation and location information. For example, if a user needs support in a location close to their current location, the assignment unit will assign the most suitable support provider. This ensures that appropriate support is provided by assigning support according to the type of disability.

[0078] The support service can provide assistance with boarding and alighting from trains. For example, the support service can provide visual assistance to visually impaired passengers when they board trains. It can also provide auditory assistance to hearing-impaired passengers when they board trains. Furthermore, the support service can provide barrier-free assistance to wheelchair users when they board trains. For example, the support service can assist wheelchair users in using elevators and ramps to ensure smooth boarding and alighting. By providing assistance with boarding and alighting from trains, users can travel safely.

[0079] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a reassuring voice guidance in a gentle tone. If the user is in a hurry, the reception desk can provide a concise interface to process the request quickly. If the user is relaxed, the reception desk can provide an interface with detailed explanations to ensure the user is satisfied before making a request. By providing a reception method that is tailored to the user's emotions, the system can help users feel confident making requests. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the user has frequently made in the past as suggestions. For example, the reception desk can prioritize suggesting reception methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest requests that the user will use during specific time periods based on their past request history. This improves user convenience by providing the most suitable reception method based on past request history.

[0081] The reception desk can filter requests based on the user's current situation and type of disability. For example, if a user has a visual impairment, the reception desk will prioritize providing audio guidance. If a user has a hearing impairment, the reception desk will prioritize providing text guidance. The reception desk can also filter people who can provide the most suitable support based on the user's current location. This ensures that appropriate support is provided by filtering according to the user's situation and type of disability.

[0082] The reception desk can estimate the user's emotions and determine the priority of requests based on those emotions. For example, if a user has an urgent request, the reception desk will prioritize it over other requests. If a user is relaxed, the reception desk will prioritize their request at the normal priority level. If a user is stressed, the reception desk will raise the priority level to ensure a quick response. This allows for a rapid response to urgent requests by determining priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reception desk can prioritize requests based on the user's geographical location when receiving requests. For example, it can prioritize requests for travel to locations close to the user's current location. If the user is in a specific area, it can prioritize requests related to that area. The reception desk can also prioritize assigning people who can provide the most suitable support based on the user's location. In this way, by considering the user's geographical location, it is possible to prioritize requests that are highly relevant.

[0084] The reception department can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception department can understand the user's current situation and needs from their social media posts and prioritize accepting relevant requests. For example, the reception department can prioritize accepting requests related to specific events based on the user's social media activity. The reception department can also analyze a user's social media activity and assign people who can provide the most suitable support. This allows for the prioritization of relevant requests by analyzing the user's social media activity.

[0085] The route search unit can estimate the user's emotions and adjust the way the route search is presented based on those emotions. For example, if the user is feeling anxious, the route search unit will provide route guidance that includes detailed explanations. If the user is in a hurry, the route search unit will provide concise and to-the-point route guidance. If the user is relaxed, the route search unit will provide visually easy-to-understand route guidance. By providing route search presentation methods that match the user's emotions, the user can travel with peace of mind. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The route search unit can calculate the optimal route based on the type of disability during route searching. For example, for visually impaired users, the route search unit prioritizes routes with ample tactile paving and audio guidance. For hearing impaired users, the route search unit prioritizes routes with ample visual guidance. The route search unit can also prioritize barrier-free routes for wheelchair users. This allows users to travel safely by providing the optimal route according to the type of disability.

[0087] The route search unit can optimize routes by considering traffic conditions and weather information during route searches. For example, the route search unit can suggest the optimal route based on real-time traffic congestion information. For example, the route search unit can suggest a route with a roof in rainy weather based on real-time weather information. Furthermore, the route search unit can also suggest the optimal route by considering the real-time operating status of public transportation. In this way, it can provide the best route by considering traffic conditions and weather information.

[0088] The route search unit can estimate the user's emotions and determine the priority of the route search based on the estimated emotions. For example, if the user has an urgent request, the route search unit will prioritize it over other requests. If the user is relaxed, the route search unit will perform the route search with normal priority. If the user is stressed, the route search unit will raise the priority to respond quickly. In this way, by determining priorities according to the user's emotions, urgent requests can be responded to quickly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The route search unit can suggest the optimal route by referring to the user's past travel history during route searching. For example, the route search unit can suggest the optimal route based on routes the user has used in the past. For example, the route search unit can suggest a route that avoids congestion based on the user's past travel history. Furthermore, the route search unit can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by providing the optimal route based on past travel history.

[0090] The route search unit can provide the optimal route by considering the user's device information during route searching. For example, if the user is using a smartphone, the route search unit provides route guidance that is adapted to the screen size. If the user is using a tablet, the route search unit provides route guidance optimized for a larger screen. Furthermore, if the user is using a smartwatch, the route search unit can provide concise and highly visible route guidance. In this way, by considering the user's device information, the optimal route guidance can be provided.

[0091] The assignment unit can estimate the user's emotions and adjust the support assignment method based on the estimated emotions. For example, if the user is feeling anxious, the assignment unit will prioritize assigning an experienced support provider. If the user is in a hurry, the assignment unit will assign a support provider who can respond quickly. If the user is relaxed, the assignment unit will select a support provider using the normal assignment method. This provides an assignment method that matches the user's emotions, allowing the user to receive support with peace of mind. 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.

[0092] The assignment department can select the most suitable personnel during the assignment process, taking into account the skills and experience of the support providers. For example, the assignment department can select a support provider skilled in visual support for a visually impaired person. For example, the assignment department can select a support provider skilled in auditory support for a hearing impaired person. Furthermore, the assignment department can select a support provider with knowledge of barrier-free design for a wheelchair user. By considering the skills and experience of the support providers, appropriate support can be provided.

[0093] The assignment unit can make assignments while considering the current status and location information of support providers. For example, the assignment unit can assign the nearest support provider based on the support provider's current location information. The assignment unit can also make assignments while considering the support provider's current status (such as availability). Furthermore, the assignment unit can make assignments at the optimal time by considering the support provider's current task status. This ensures that support is provided quickly and appropriately by considering the support provider's current status and location information.

[0094] The assignment unit can estimate the user's emotions and determine assignment priorities based on those emotions. For example, if a user has an urgent request, the assignment unit will prioritize it over other requests. If a user is relaxed, the assignment unit will assign it with normal priority. If a user is stressed, the assignment unit will raise the priority to ensure a quick response. This allows for a quick response to urgent requests by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The assignment unit can select the most suitable support provider by referring to the user's past support history during the assignment process. For example, the assignment unit can prioritize selecting support providers the user has used in the past. For example, the assignment unit can select a specific support provider based on the user's past support history. The assignment unit can also analyze the user's past support history and select the most suitable support provider. This improves user convenience by selecting the most suitable support provider based on past support history.

[0096] The assignment department can analyze the social media activity of support providers during the assignment process and provide relevant support. For example, the assignment department can understand the current situation and skills of support providers from their social media activity and provide optimal support. For example, the assignment department can provide support related to a specific event based on the support provider's social media activity. Furthermore, the assignment department can analyze the support provider's social media activity and provide support at the optimal time. In this way, by analyzing the support provider's social media activity, optimal support can be provided.

[0097] The support provider can estimate the user's emotions and adjust the support delivery method based on the estimated emotions. For example, if the user is feeling anxious, the support provider will provide support in a gentle tone to provide reassurance. If the user is in a hurry, the support provider will provide concise support to respond quickly. If the user is relaxed, the support provider will provide support that includes detailed explanations. This allows users to receive support with peace of mind by providing support methods that match their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The support department can provide optimal support by referring to the user's past support history when providing support. For example, the support department can provide optimal support based on the support the user has used in the past. For example, the support department can prioritize providing specific support based on the user's past support history. Furthermore, the support department can analyze the user's past support history and provide the most appropriate support. This improves user convenience by providing optimal support based on past support history.

[0099] The support department can customize support based on the user's current situation and type of disability. For example, if a user has a visual impairment, the support department will prioritize providing visual support. If a user has a hearing impairment, the support department will prioritize providing auditory support. The support department can also provide optimal support based on the user's current situation (such as location information). This ensures that appropriate support is provided by tailoring support to the user's situation and type of disability.

[0100] The support system can estimate a user's emotions and prioritize support based on those emotions. For example, if a user needs urgent support, the support system will prioritize that support over others. If a user is relaxed, the support system will provide support at the normal priority level. If a user is stressed, the support system will raise the priority level to respond quickly. This allows for a rapid response to urgent support requests by prioritizing support according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The support department can provide optimal support by considering the user's geographical location. For example, if the user needs support in a location close to their current location, the support department will provide the most suitable support. For example, if the user is in a specific area, the support department will provide support relevant to that area. The support department can also assign the most suitable people to provide support based on the user's location. This allows for the provision of optimal support by considering the user's geographical location.

[0102] The support department can analyze a user's social media activity and provide relevant support when providing support. For example, the support department can understand the user's current situation and needs from their social media posts and provide relevant support. For example, the support department can provide support related to a specific event based on the user's social media activity. Furthermore, the support department can analyze a user's social media activity and provide support at the optimal time. This allows for the provision of optimal support by analyzing the user's social media activity.

[0103] The monitoring unit can estimate the user's emotions and adjust the support progress monitoring method based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit will report progress frequently. For example, if the user is relaxed, the monitoring unit will report progress at a normal frequency. Also, if the user is in a hurry, the monitoring unit will report progress quickly. This provides a progress monitoring method that is tailored to the user's emotions, allowing the user to receive support with peace of mind. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The monitoring unit can perform monitoring while considering the support provider's current status and location information. For example, the monitoring unit can monitor progress at the optimal time based on the support provider's current location information. The monitoring unit can also monitor while considering the support provider's current status (such as free time). Furthermore, the monitoring unit can monitor progress at the optimal time by considering the support provider's current task status. This allows for monitoring progress at the appropriate time by considering the support provider's current status and location information.

[0105] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user needs urgent support, the monitoring unit will prioritize monitoring over other support. For example, if the user is relaxed, the monitoring unit will monitor at the normal priority. Furthermore, if the user is stressed, the monitoring unit will raise the priority to respond quickly. This allows for a rapid response to urgent support by determining priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The monitoring unit can select the optimal monitoring method by referring to the user's past support history during monitoring. For example, the monitoring unit can select the optimal monitoring method based on the support the user has used in the past. For example, the monitoring unit can prioritize selecting a specific monitoring method based on the user's past support history. The monitoring unit can also analyze the user's past support history and select the most suitable monitoring method. This improves user convenience by providing the optimal monitoring method based on past support history.

[0107] The recording unit can estimate the user's emotions and adjust the support recording method based on the estimated emotions. For example, if the user is feeling anxious, the recording unit will keep a detailed record for later review. If the user is relaxed, the recording unit will record using a normal recording method. If the user is in a hurry, the recording unit will record using a concise method. This provides a recording method that suits the user's emotions, allowing them to receive support with peace of mind. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The recording unit can take support provider feedback into consideration when recording. For example, the recording unit can create detailed records based on support provider feedback. For example, the recording unit can prioritize recording specific support content based on support provider feedback. The recording unit can also analyze support provider feedback and record the most important information. This allows for detailed and appropriate records to be made by considering support provider feedback.

[0109] The recording unit can estimate the user's emotions and determine recording priorities based on those estimated emotions. For example, if the user needs urgent support, the recording unit will prioritize recording over other recordings. If the user is relaxed, the recording unit will record with normal priority. If the user is stressed, the recording unit will raise the priority to respond quickly. This allows for a quick response to urgent support by determining priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The recording unit can select the optimal recording method by referring to the user's past support history during recording. For example, the recording unit can select the optimal recording method based on the support content the user has used in the past. For example, the recording unit can prioritize selecting a specific recording method from the user's past support history. The recording unit can also analyze the user's past support history and select the most suitable recording method. This improves user convenience by providing the optimal recording method based on past support history.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The mobility assistance system can estimate the user's emotions and adjust the way route guidance is presented based on those emotions. For example, if the user is feeling anxious, it can provide route guidance that includes detailed explanations. If the user is in a hurry, it can provide concise and to-the-point route guidance. Furthermore, if the user is relaxed, it can provide visually easy-to-understand route guidance. By providing route guidance that is tailored to the user's emotions, it can help users travel with peace of mind.

[0113] The mobility assistance system can suggest the optimal route by referring to the user's past travel history. For example, it can suggest the best route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by providing the optimal route based on past travel history.

[0114] Mobility assistance systems can provide optimal route guidance by considering the user's device information. For example, if the user is using a smartphone, the system can provide route guidance optimized for the screen size. If the user is using a tablet, it can provide route guidance optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide concise and highly visible route guidance. In this way, by considering the user's device information, the system can provide optimal route guidance.

[0115] The mobility assistance system can estimate the user's emotions and adjust the way support is provided based on those emotions. For example, if the user is feeling anxious, it can provide support in a gentle, reassuring tone. If the user is in a hurry, it can provide concise support to respond quickly. Furthermore, if the user is relaxed, it can provide support that includes detailed explanations. By providing support tailored to the user's emotions, the system ensures that the user feels comfortable receiving assistance.

[0116] Mobility assistance systems can select the most suitable personnel by considering the skills and experience of support providers. For example, for visually impaired individuals, support providers skilled in visual support can be selected. Similarly, for hearing-impaired individuals, support providers skilled in auditory support can be selected. Furthermore, for wheelchair users, support providers with knowledge of barrier-free design can be selected. This ensures that appropriate support is provided by considering the skills and experience of the support providers.

[0117] The mobility assistance system can estimate the user's emotions and prioritize support based on those emotions. For example, if a user needs urgent support, it can be prioritized over other support. If the user is relaxed, support can be provided at the normal priority level. Furthermore, if the user is stressed, their priority can be increased to ensure a quick response. This allows for a rapid response to urgent support requests by prioritizing based on the user's emotions.

[0118] The mobility assistance system can prioritize requests based on the user's geographical location. For example, it can prioritize requests for travel to locations close to the user's current location. It can also prioritize requests related to a specific area if the user is in that area. Furthermore, it can prioritize assigning the most suitable support personnel based on the user's location. This allows for the prioritization of highly relevant requests by considering the user's geographical location.

[0119] The mobility assistance system can analyze a user's social media activity and accept relevant requests. For example, it can understand the user's current situation and needs from their social media posts and prioritize requests related to those situations. It can also prioritize requests related to specific events based on the user's social media activity. Furthermore, it can analyze the user's social media activity and assign people who can provide the most suitable support. In this way, by analyzing the user's social media activity, it can prioritize requests related to those situations.

[0120] The mobility assistance system can estimate the user's emotions and prioritize monitoring based on those emotions. For example, if the user needs urgent support, monitoring can be prioritized over other support. If the user is relaxed, monitoring can be performed with the normal priority. Furthermore, if the user is stressed, the priority can be increased to allow for a quicker response. This allows for a rapid response to urgent support requests by prioritizing based on the user's emotions.

[0121] The mobility assistance system can provide optimal support by referring to the user's past support history. For example, it can provide optimal support based on the support the user has received in the past. It can also prioritize providing specific support based on the user's past support history. Furthermore, it can analyze the user's past support history and provide the most suitable support. This improves user convenience by providing optimal support based on past support history.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk receives requests from users. User requests may include, for example, information about the departure and destination locations, but are not limited to these examples. The reception desk may receive requests from users, for example, through a smartphone app. The reception desk can also accept requests via voice input using speech recognition technology. For example, if a user requests by voice, "I want to go to XX station," the reception desk will recognize the voice and analyze the request. Furthermore, the reception desk can also accept requests via text input. For example, if a user enters the departure and destination locations in text, the reception desk will analyze the text and understand the request. Step 2: The route search unit searches for a route based on the information received by the reception unit. The route search unit calculates the optimal route based on map data, for example. The route search unit searches for the shortest route from the current location to the destination by referring to a map database, for example. The route search unit can also optimize the route by considering traffic conditions and weather information. For example, it suggests the optimal route based on real-time traffic congestion information. Furthermore, the route search unit can calculate the optimal route based on the type of disability the user has. For example, it prioritizes routes with ample tactile paving and audio guidance for visually impaired users. Step 3: The assignment unit assigns support based on the route search unit. The assignment unit assigns support according to the type of disability that has been registered in advance. For example, if a user is visually impaired, the assignment unit will assign people who can support visually impaired people, as visual support is required. Similarly, if a user is hearing impaired, the assignment unit can assign people who can support hearing impaired people, as auditory support is required. Furthermore, the assignment unit can also assign support considering the user's current situation and location information. For example, if the user needs support in a location close to their current location, the assignment unit will assign the most suitable support provider. Step 4: The support provision unit provides the support assigned by the assignment unit. The support provision unit provides support such as getting on and off trains. For example, the support provision unit provides visual support when a visually impaired person is getting on a train. The support provision unit can also provide auditory support when a hearing impaired person is getting on a train. Furthermore, the support provision unit may be equipped with a monitoring unit to monitor the progress of the support. For example, the support provision unit monitors the progress of the support in real time and adjusts the support content as needed.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the reception unit, route search unit, assignment unit, and support provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives requests from users. The route search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for the optimal route. The assignment unit is implemented by the specific processing unit 290 of the data processing unit 12 and assigns support. The support provision unit is implemented by the control unit 46A of the smart device 14 and provides the assigned support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the reception unit, route search unit, assignment unit, and support provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives requests from users. The route search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and searches for the optimal route. The assignment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and assigns support. The support provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the assigned support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, route search unit, assignment unit, and support provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives requests from users. The route search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for the optimal route. The assignment unit is implemented by the specific processing unit 290 of the data processing unit 12 and assigns support. The support provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the assigned support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the reception unit, route search unit, assignment unit, and support provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives requests from users. The route search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for the optimal route. The assignment unit is implemented by the specific processing unit 290 of the data processing unit 12 and assigns support. The support provision unit is implemented by the control unit 46A of the robot 414 and provides the assigned support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A reception department that receives requests from users, A route search unit searches for a route based on the information received by the reception unit, An assignment unit that assigns support based on the route found by the aforementioned route search unit, The system comprises a support provision unit that provides the support assigned by the aforementioned assignment unit. A system characterized by the following features. (Note 2) The aforementioned support provision unit, It includes a monitoring unit to monitor the progress of support. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support provision unit, It includes a recording unit for recording the results of support. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned route search unit, Calculate the optimal route based on map data The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned assignment unit is, Support is assigned according to the type of disability registered in advance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support provision unit, Providing assistance with getting on and off trains. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the request acceptance method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a request, filtering is performed based on the user's current status and the type of problem. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request, the system prioritizes accepting requests that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts requests that are relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned route search unit, The system estimates the user's emotions and adjusts the way route searches are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned route search unit, When searching for a route, the system calculates the optimal route based on the type of obstacle. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned route search unit, When searching for a route, the system optimizes the route by taking into account traffic conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned route search unit, It estimates the user's emotions and determines the priority of route searches based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned route search unit, When searching for a route, the system suggests the optimal route by referencing the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned route search unit, When searching for a route, the system takes the user's device information into consideration to provide the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned assignment unit is, We estimate the user's emotions and adjust how support is assigned based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned assignment unit is, When assigning a support role, the most suitable personnel will be selected based on their skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned assignment unit is, When assigning support, the current status and location information of the support provider will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned assignment unit is, It estimates the user's emotions and determines assignment priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned assignment unit is, During the assignment process, the system selects the most suitable support provider by referring to the user's past support history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned assignment unit is, When assigning support, we analyze the social media activity of the support provider and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support provision unit, We estimate the user's emotions and adjust how support is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support provision unit, When providing support, we refer to the user's past support history to provide the most suitable support. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support provision unit, When providing support, customize the support based on the user's current situation and the type of problem. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support provision unit, The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support provision unit, When providing support, we take the user's geographical location into consideration to provide the most suitable support. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support provision unit, When providing support, we analyze the user's social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, We estimate the user's emotions and adjust how support progress is monitored based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned monitoring unit, During monitoring, the current status and location information of the support provider are taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected by referring to the user's past support history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned recording unit is We estimate the user's emotions and adjust how support is recorded based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned recording unit is When recording, take into account the feedback from the support provider. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned recording unit is The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned recording unit is During recording, the system selects the optimal recording method by referring to the user's past support history. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception department that receives requests from users, A route search unit searches for a route based on the information received by the reception unit, An assignment unit that assigns support based on the route found by the aforementioned route search unit, The system comprises a support provision unit that provides the support assigned by the aforementioned assignment unit. A system characterized by the following features.

2. The aforementioned support provision unit, It includes a monitoring unit to monitor the progress of support. The system according to feature 1.

3. The aforementioned support provision unit, It includes a recording unit for recording the results of support. The system according to feature 1.

4. The aforementioned route search unit, Calculate the optimal route based on map data The system according to feature 1.

5. The aforementioned assignment unit is, Support is assigned according to the type of disability registered in advance. The system according to feature 1.

6. The aforementioned support provision unit, Providing assistance with getting on and off trains. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the request acceptance method based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system according to feature 1.

9. The aforementioned reception unit is When receiving a request, filtering is performed based on the user's current status and the type of problem. The system according to feature 1.

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