system

Abstract

We provide the system. [Solution] A means of registering travel information provided by users, A means of collecting station facility information, train information in operation, and congestion status in real time, A means for generating an optimal support plan based on the aforementioned movement information and the aforementioned collected information, A means of notifying the user of the aforementioned support plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 as a 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 order for wheelchair users to smoothly board and alight in railway travel, the assistance of station staff and crew is essential. However, there is a problem that sufficient services may not be provided due to staff shortages and the progress of operational efficiency improvement. In particular, in small and medium-sized railway companies and local lines, these problems are prominent due to resource constraints. Therefore, there is a need for a method in which users register travel information in advance and optimal assistance is provided by comprehensively using staff allocation and facility information.

Means for Solving the Problems

[0005] This invention provides a system that registers travel information provided by users and collects station facility information, operating train information, and congestion status in real time. Furthermore, it generates an optimal support plan based on this information and notifies the user of that support plan. In addition, by effectively deploying support personnel based on the support plan and providing real-time guidance, it realizes an environment in which users can use the railway with peace of mind.

[0006] "Users" refers to people who travel by train. In particular, in this invention, this includes people who use wheelchairs.

[0007] "Travel information" refers to data provided by the user, such as the date and time of travel, departure station, arrival station, desired travel time, and required support.

[0008] "Facility information" refers to data on barrier-free facilities at railway stations and on trains, as well as data on vehicles currently in operation.

[0009] "Congestion status" refers to real-time data showing the density and degree of congestion of people at a specified time and place.

[0010] A "support plan" refers to a plan that includes the optimal placement of vehicles, boarding / alighting points, and support staff to support the smooth movement of users.

[0011] "Support personnel" refers to individuals such as station staff and volunteers who are stationed at stations and on trains to support users' rail travel. [Brief explanation of the drawing]

[0012] [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] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] In implementing this invention, a series of systems are configured to improve user convenience and maximize the efficiency of railway operators. This system consists of three main components: users, servers, and terminals.

[0034] First, users register their travel information using a dedicated application or web portal. This information includes the date and time of travel, departure point, destination, and the type of assistance required. This allows the server to understand the user's specific needs.

[0035] Next, the server collects real-time information on station and train facilities, congestion levels, and operational status. This information is obtained through APIs and data feeds provided by the railway company. The server then uses this data to generate an optimized support plan for users. This plan includes which train cars and doors should be used, when support is needed, and also provides instructions for the efficient deployment of support personnel.

[0036] After a support plan is generated, the server notifies the user via a terminal. This terminal can be a mobile device or a personal computer, allowing the user to review the plan and use it to plan their journey. This notification includes specific boarding locations, available accessible routes, and predicted congestion information.

[0037] During actual travel, the terminal continuously provides users with real-time information. This includes the current location of the train, delay information, and equipment usage status. Users will also be notified of scheduled support personnel as needed, ensuring they can travel with peace of mind.

[0038] This system will enable the effective use of limited human resources and create an environment where wheelchair users can use the railway with peace of mind. Furthermore, it will allow users to travel more systematically and efficiently, providing significant benefits to railway operators in terms of operations and service provision.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users access a dedicated app or web portal and enter detailed travel information. This includes the travel date, departure station, arrival station, desired departure time, and required assistance. This information is sent to the server and registered in the database.

[0042] Step 2:

[0043] The server uses APIs provided by railway companies to collect real-time information such as accessibility facilities at each station, train operation status, and congestion levels. This ensures that the system is always updated with the latest information.

[0044] Step 3:

[0045] The server uses an AI algorithm to generate the optimal support plan based on travel information provided by the user and collected real-time data. This includes recommending a suitable vehicle, the appropriate boarding and alighting locations, and assigning appropriate support personnel.

[0046] Step 4:

[0047] The server notifies the user's device of the generated support plan. The notification includes specific vehicle information, the barrier-free route for the day, congestion forecasts, and information on the deployment of support staff.

[0048] Step 5:

[0049] On the day of travel, the terminal will continuously provide real-time updates. Users can receive the latest information through the terminal regarding train delays, equipment usage, and the deployment status of support personnel.

[0050] Step 6:

[0051] After the transfer is complete, users can send feedback on the assistance provided to the system to help improve future services. This feedback is stored on the server and used to improve future assistance plans.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] In modern society, as the use of public transportation continues to increase, there is a growing need to provide smooth and safe transportation for users who have difficulty traveling. However, current systems lack sufficient real-time information provision and appropriate allocation of human resources, making it difficult to support efficient travel.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for registering travel information provided by the user, means for collecting transportation facility information, vehicle information in operation, and congestion status in real time, means for generating an optimal support plan based on the collected information, and means for providing real-time information during travel via the user's terminal. This makes it possible to provide an optimal travel plan tailored to the user's needs and to provide reliable real-time information during travel.

[0057] "Travel information provided by users" refers to information provided by users regarding their travel, including the date and time of their trip, departure point, destination, and the type of assistance they require.

[0058] "Transportation facility information" refers to data regarding the operational status and availability of various facilities installed at stations and on trains.

[0059] "Information on vehicles in operation" refers to data regarding the current location and operating schedule of public transportation such as trains and buses.

[0060] "Congestion status" refers to information regarding the degree of congestion and occupancy rate within public transportation.

[0061] "Means for generating optimal support plans" refers to a process that automatically creates specific support plans to facilitate user mobility based on collected mobility and equipment information.

[0062] "Means of notification" refers to technology that sends the generated support plan to the user's device and informs the user of its contents.

[0063] "Means of providing real-time information" refers to a system that continuously updates and provides information on the current location and status of transportation so that users can always stay informed of the latest situation while on the move.

[0064] The system of this invention mainly consists of a server, a terminal, and a user. The following specific steps are taken to implement the invention.

[0065] First, users enter their travel information using a dedicated application or web portal. This includes the date and time of travel, departure point, destination, and required assistance. To ensure accurate registration of this information, the interface employs a user-friendly design.

[0066] Next, the server uses transportation APIs and external data feeds to collect real-time information on actual train operations and station facilities in order to process the user's travel information mentioned above. The server incorporates algorithms for data collection and plan generation.

[0067] The server generates an optimal mobility assistance plan based on collected information and the user's travel data. This plan includes specific instructions such as which vehicle and which door to use, the timing to avoid congestion during travel, and the deployment of necessary support personnel.

[0068] After a support plan is generated, the server sends it to the user's device. This device can be the user's mobile device or computer. This makes it easier for the user to review the plan they receive.

[0069] The device continues to provide real-time updated information while the user is on the move, including the current train location, delay information, and station equipment usage status. It also facilitates smooth communication with support personnel based on the generated support plan.

[0070] As a concrete example, to ensure smooth movement for wheelchair users at train stations, the system includes information on whether or not elevators will be used in the plan and suggests appropriate boarding positions for the user. In this way, the system realizes the optimal travel experience within limited time and resources.

[0071] An example of a prompt message would be, "Please suggest the optimal travel plan for a wheelchair user using the train."

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] Users log in to a dedicated application or web portal and register their travel details. This information includes the date and time, departure point, destination, and required assistance. The entered data is sent to a server and securely stored in a database. Here, travel information based on the user's needs is collected in digital format.

[0075] Step 2:

[0076] The server collects real-time information on station facilities, trains in operation, and congestion levels through transportation APIs and external data feeds. Using this information as input, the server integrates and cross-references the collected data to perform analysis that determines the most suitable travel routes and timings for users. Data processing includes predicting current congestion levels and delay risks. The analyzed data is then fed into an internal analysis model as output.

[0077] Step 3:

[0078] The server generates an optimal support plan for the user's travel based on the analyzed data. This generation uses a generative AI model that automatically formulates the plan best suited to each user's needs. The input is the analyzed data obtained in step 2, and the output is a specific support plan. This support plan includes details such as which vehicle door to use, the shortest travel route, and the most appropriate time allocation.

[0079] Step 4:

[0080] The server notifies the device of the generated assistance plan. The device is the user's mobile device or computer, and upon receiving the notification, the user can view the plan details. Specifically, a real-time navigation guide is displayed on the user's screen via a push notification to the device. This notification includes the assistance plan, and the user begins their journey based on it.

[0081] Step 5:

[0082] While on the move, the terminal continuously provides the user with the latest information. This includes real-time updates on train delays, current location, and station facility usage. Input here is real-time information continuously provided by the server. Data processing involves updating environment variables as the user moves and suggesting the optimal action based on predictions. The user receives immediate notifications through the terminal, enabling them to take timely and appropriate action.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] Users of public transport in urban areas face the problem of not being able to accurately grasp real-time traffic conditions and congestion levels, making it difficult to plan their travel efficiently. This can lead to unexpected delays and congestion that disrupt comfortable travel. Furthermore, transportation providers also face the challenge of optimizing resource allocation.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for registering travel information provided by users, means for collecting information on transportation facilities, information on the operation of moving objects, and congestion status in real time, and means for generating an optimal travel plan based on the travel information and the collected information. This enables users to move efficiently within cities and allows transportation providers to optimize their resources.

[0088] "Travel information" refers to data provided by the user, such as the origin, destination, date and time of travel, and the type of support needed.

[0089] "Transportation facility information" refers to information about stations, bus stops, and train and bus facilities, including access methods and the availability of barrier-free facilities.

[0090] "Mobile transportation operation information" refers to data showing the operating status of public transportation such as trains and buses, including information such as operating times and delay status.

[0091] "Congestion status" refers to data that indicates the density of people and vehicles in public transportation and road conditions.

[0092] A "travel plan" is a plan that provides the optimal travel procedure and route based on the user's input information and real-time traffic information.

[0093] "Support personnel" are individuals assigned to facilitate the smooth movement of users, and their efficient allocation is ensured.

[0094] In implementing this invention, the server first receives travel information provided by the user through a web application or mobile application. This information includes data such as the origin, destination, date and time of travel, and the type of assistance required. The server registers this travel information in a database and collects real-time information on transportation facilities, vehicle operation information, and congestion status through a public transportation API. This includes operating hours, delay information, and accessibility status of facilities.

[0095] The server uses an AI engine based on Python and TENSORFLOW® to analyze collected data and generate an optimal travel plan for the user. This travel plan includes the optimal boarding location, available barrier-free routes, and predicted congestion information. The generated travel plan is then notified to the user using the most appropriate method. The notification is delivered to a smartphone or tablet. Based on this information, the user can travel effectively and without stress.

[0096] As a concrete example, when a user arrives at their planned departure station based on a travel plan registered on their smartphone, the following prompt message will appear in the user's app: "Please tell me the best route to my next destination, avoiding congestion." In this case, by following the suggested route, the user can avoid congestion and reach their destination smoothly.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] Users enter travel information through a smartphone application. This information includes departure point, destination, travel date and time, and required assistance. The entered travel information is sent to a server and registered in a database.

[0100] Step 2:

[0101] The server collects information on transportation facilities, vehicle operation information, and congestion status in real time via the public transportation API, based on registered travel information. It receives data provided by the transportation API as input and obtains information including congestion levels and operating times.

[0102] Step 3:

[0103] The server uses Python and TensorFlow to generate an optimal travel plan using an AI engine based on acquired data and user movement information. During this process, the data is preprocessed, and the generated AI model is used to calculate the optimal route and timing. The output is a travel plan that includes the optimal boarding location and estimated travel time.

[0104] Step 4:

[0105] The generated travel plan is sent from the server to the user's smartphone. The user's device receives the push notification and displays the plan details on the screen. The user then uses this as a reference to carry out their actual travel.

[0106] Step 5:

[0107] While in transit, the user's device continuously receives real-time information updates from the server. Current location and unexpected delay information are provided periodically, allowing the user to adjust their plan as needed.

[0108] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0109] The system for implementing this invention provides more personalized assistance by integrating an emotion engine, in addition to generating and providing user mobility assistance plans. The system consists primarily of a user, a server, and a terminal.

[0110] First, the user enters travel details using a dedicated application or web portal. This includes the travel date, departure station, arrival station, desired travel time, and required assistance. The provided information is sent to the server and registered in the database.

[0111] The server uses the railway company's API to collect real-time information on station and train facilities, operating status, and congestion levels. Furthermore, the server uses an emotion engine to analyze users' emotions based on input data and past feedback. The results of this analysis are considered when creating support plans.

[0112] The generated support plan is adjusted according to the user's emotional state. For example, if the user appears tense, more detailed instructions will be provided. Conversely, if the user is relaxed, only the bare minimum of information will be provided to reduce their stress.

[0113] Next, the server notifies the terminal of the adjusted support plan. The notification includes specific vehicle information, potential congestion information for the day, and customized guidance for the user. This allows the user to respond appropriately at each step of their journey.

[0114] Finally, after the journey is complete, users provide feedback, and the system uses this information to further improve the plan. This feedback is used for analysis by an emotion engine and utilized in creating the next support plan. For example, if a user felt uneasy in an area last time, additional support staff will be assigned to that area next time. This improves user satisfaction during travel and enhances the quality of support provided by the railway company.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] Users access a dedicated app or web portal and enter their travel schedule. They enter the travel date, departure station, arrival station, departure time, and any necessary support. This data is sent to the server and automatically stored in the database.

[0118] Step 2:

[0119] The server obtains real-time information on station and train accessibility facilities, operating status, and congestion levels via the railway company's API. This information is aggregated as data necessary for generating subsequent support plans.

[0120] Step 3:

[0121] The server analyzes the user's emotions using an emotion engine based on the user's past movement data and input information. It uses facial recognition and voice analysis functions to infer the user's emotional state and records it in an emotion database.

[0122] Step 4:

[0123] By comparing the emotion analysis results with collected real-time information, the server generates the optimal support plan. This plan is adjusted according to the user's emotions; for example, if stress levels are high, additional guidance will be provided.

[0124] Step 5:

[0125] The server sends the generated assistance plan to the terminal and notifies the user. The notification includes recommended vehicles and locations, congestion forecasts, and personalized, emotionally responsive guidance, allowing the user to proceed with their journey.

[0126] Step 6:

[0127] While on the move, the device provides users with real-time updates. If there are any changes in the environment or situation, the latest information, including emotional responses, is immediately transmitted to the user based on instructions from the server.

[0128] Step 7:

[0129] After completing the journey, users provide feedback and comments within the app. This feedback is used by the emotion engine to improve future support plans, contributing to the overall performance of the system.

[0130] (Example 2)

[0131] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0132] Conventional mobility assistance systems failed to provide individualized support that took into account the user's emotional state, and thus could not adequately enhance user comfort and satisfaction. Furthermore, they lacked efficiency in real-time information acquisition and the deployment of support personnel, making it difficult to provide prompt and accurate support to users.

[0133] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0134] In this invention, the server includes means for recording travel details provided by the user, means for acquiring equipment information, transportation information during operation, and congestion status in real time, and means for analyzing the user's emotional state. This makes it possible to provide an adjustment support plan tailored to the individual needs of the user, thereby improving their sense of security and satisfaction during travel.

[0135] "User" refers to an individual or group that uses the system to receive assistance related to mobility.

[0136] "Travel details" refers to specific information provided by the user, such as the date and time of travel, departure point, arrival point, and the type of support required.

[0137] "Means of recording" refers to methods and devices for saving user movement details to a database or other recording media.

[0138] "Facility information" refers to available facilities, installation status, and accessibility information for stations and other transportation facilities.

[0139] "Information on transportation in operation" refers to the operating schedules, delay information, and route information for public transportation such as trains and buses.

[0140] "Congestion status" refers to information regarding the degree of crowding among people on a particular mode of transport, at a station, or within a facility.

[0141] "Means of acquiring data in real time" refers to technologies and processes that enable the immediate acquisition of the latest status and conditions, making them available for use in the system.

[0142] "Emotional state" refers to the results of evaluating and analyzing the user's psychological state and emotional responses.

[0143] "Means of analysis" refers to technical methods and devices used to evaluate the user's condition and needs based on collected data and information.

[0144] A "support adjustment plan" refers to a mobility support plan that is optimized and proposed based on the individual circumstances of the user, using collected and analyzed information.

[0145] "Means of communication" refers to the technical methods and devices used to transmit the generated support plan to the user.

[0146] This invention is a system that personalizes user mobility assistance, and is operated with clearly defined roles for the server, terminal, and user. The server collects and analyzes information provided by the user and generates an optimal mobility assistance plan. The following hardware and software are used for implementation.

[0147] The server utilizes a database management system, such as an RDBMS like PostgreSQL, to store travel details. It also employs communication protocols to obtain facility and operational information via APIs from railway companies and public transport organizations. For real-time data acquisition, it uses WebSockets as an asynchronous communication technology. Furthermore, to analyze emotional states, it leverages a generative AI model that performs natural language processing, utilizing the open-source library TensorFlow.

[0148] The device provides an interface for users to enter travel details and receive a plan. For example, this could be a dedicated application running on iOS or Android®. This application would feature an intuitive UI to facilitate user interaction and would include form input and notification functions.

[0149] Users access the mobility assistance system via mobile devices or computers. For example, a parent with young children might input, "Please generate a plan for traveling from Tokyo Station to Shin-Osaka Station with a stroller next Sunday," and the system would notify them of the necessary assistance, enabling a smooth journey.

[0150] An example of a prompt message is, "Create a travel assistance plan for a wheelchair user from Tokyo to Shin-Osaka. Include information on priority seating and elevators." Based on this input, an assistance plan tailored to the user's needs will be generated and provided.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] Users access the system's dedicated application or web portal using a terminal and enter detailed travel information. Input fields include the travel date, departure station, arrival station, desired time, and required assistance. For example, if a user enters "November 5, 2023, departing from Tokyo Station, bound for Shin-Osaka Station, desired time 10:00, wheelchair user," this information is formatted as data. After formatting, the input data is sent to the server.

[0154] Step 2:

[0155] The server receives the movement details sent by the user and registers them in the database. At this time, the database stores detailed information along with the user's movement history. This will enable pattern recognition and data analysis in the future. The output from the database confirms that the information is available for use by other modules.

[0156] Step 3:

[0157] The server uses APIs from external transportation companies to collect facility information, operating status, and congestion information in real time. This information collection includes, for example, "station elevator operational status" and "train delay information." Since the collected data is disorganized in its raw form, it is converted into a format that is easy to analyze and stored in preparation for the next processing step.

[0158] Step 4:

[0159] The server uses an emotion engine to analyze the user's emotional state. It analyzes user feedback and past usage data based on natural language processing techniques to infer how the user is currently feeling and what kind of support they need. Input is text and user responses, and output is the analyzed emotion data.

[0160] Step 5:

[0161] The server uses a generative AI model to create a support plan based on the acquired real-time data and sentiment analysis results. The generated plan includes specific vehicles to ride, available facilities, and routes to avoid congestion. The generated data is formatted in a way that is realistically understandable to the user.

[0162] Step 6:

[0163] The server notifies the terminal of the created support plan. The terminal displays the notification in a way that is easy for the user to understand, and this includes providing it as audio guidance or visual information. For example, instructions such as "Today, you will be in car number 1 with your assigned seat, and you will use the elevator at the front" are visualized.

[0164] Step 7:

[0165] After the journey is complete, the user provides feedback about the journey using their device. The feedback is sent to the server and analyzed again by the emotion engine. This provides data that can be used to improve future support plans, and the analysis results are stored in a database.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] In modern society, there is a demand for services that reduce stress and anxiety and provide a comfortable experience when citizens move around within cities. Therefore, personalized mobility support that takes into account the emotional state of users is necessary, and improving the quality of support for people with specific needs, such as visitors and the elderly, is a particular challenge.

[0169] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0170] In this invention, the server includes means for registering travel information and emotional information provided by the user, means for collecting station equipment information, operating vehicle information, and congestion status in real time, and means for generating an optimal and emotionally adaptive support plan based on the travel information, emotional information, and the collected information. This makes it possible to provide a personalized support plan that corresponds to the user's emotional state.

[0171] "User" refers to an individual who uses this system and receives mobility assistance.

[0172] "Travel information" refers to detailed travel data provided by users, such as departure point, destination, and travel time.

[0173] "Emotional information" refers to data about the user's current emotional state, including stress levels and relaxation levels.

[0174] "Station facilities information" refers to information indicating the location and availability of elevators, escalators, and ticket gates within the station.

[0175] "Information on vehicles in operation" refers to information regarding the location and timetable of public transportation such as trains and buses that are currently in service.

[0176] "Congestion status" refers to information regarding the degree of congestion at stations and on trains, as well as information regarding expected congestion levels.

[0177] An "optimal and emotionally adaptive support plan" refers to a customized mobility guidance and support plan tailored to the user's needs and emotional state, based on collected information and emotional data.

[0178] "Support staff" refers to personnel who provide assistance and guidance to users on-site.

[0179] "Adaptive guidance" refers to a method of providing personalized travel information tailored to the user's current situation and emotions.

[0180] To implement this invention, the system consists of a server, a user's terminal, and the user. First, the user uses a smartphone or smart glasses to input travel information and emotional information through a dedicated application. Here, travel information includes the departure point, destination, and desired travel time, while emotional information includes the user's stress level and degree of relaxation.

[0181] The server utilizes railway company APIs and other transportation information APIs to collect real-time information on station and line facilities, operating status, and congestion levels. This information is combined with user sentiment information using a generative AI model to create optimal and emotionally adaptive support plans. For example, it suggests routes that avoid congestion and places where users can relax, ensuring a comfortable journey.

[0182] The generated support plan is notified to the user's device, and the user travels along the designated route. This ensures a stress-free journey for the user. After completing the journey, the user can send feedback again from their device, which helps to improve the support plan for future journeys.

[0183] As a concrete example, when tourists visiting Japan for the first time efficiently tour tourist attractions in a new city, the system can provide transportation support that reduces anxiety about the Japanese language while allowing them to experience the culture. In such cases, the system generates guidance that takes the tourist's feelings into consideration, enabling smooth movement between tourist spots.

[0184] An example of a prompt message for a generative AI model is as follows:

[0185] "The user is a tourist visiting Japan, wants to travel from Shibuya Station to Asakusa Station, and is feeling anxious about their first visit to Japan. Please generate travel suggestions that take their cultural background into consideration and provide them with a sense of security."

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The user enters their departure point, destination, desired travel time, and emotional state using a smartphone or smart glasses app. This input data is sent from the device to the server. The server receives the user's travel information and emotional state as input and registers it in its database.

[0189] Step 2:

[0190] The server collects real-time facility information, operating status, and congestion data for stations and trains in operation through railway company APIs and traffic information APIs. The data obtained from these APIs is organized within the system and converted into a readily usable format. The collected information is recorded along with user travel information.

[0191] Step 3:

[0192] The server uses a generative AI model to analyze user movement information, emotional information, and collected real-time data. This analysis process generates an appropriate support plan. In the initial stages of the analysis, it recognizes the user's emotional state and creates relaxing guidance, especially for users experiencing tension or anxiety. The output is a personalized, optimal, and emotionally adaptive support plan.

[0193] Step 4:

[0194] The server notifies the user's device of the generated assistance plan. The notified plan includes, for example, the optimal route to avoid congestion and relaxation points that take emotions into consideration. The device displays this information in an easy-to-understand manner for the user, making it usable as a guide while traveling.

[0195] Step 5:

[0196] After the journey, the user enters feedback from their device and sends it to the server. This feedback is stored in a database and used when creating future support plans. The feedback process involves data analysis based on changes in the user's emotional state and their experience during the journey, in order to improve the accuracy of the plan.

[0197] 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.

[0198] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0204] 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.

[0205] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0206] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0207] 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.

[0208] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0209] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0210] The 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.

[0211] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0212] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0213] In implementing this invention, a series of systems are configured to improve user convenience and maximize the efficiency of railway operators. This system consists of three main components: users, servers, and terminals.

[0214] First, users register their travel information using a dedicated application or web portal. This information includes the date and time of travel, departure point, destination, and the type of assistance required. This allows the server to understand the user's specific needs.

[0215] Next, the server collects real-time information on station and train facilities, congestion levels, and operational status. This information is obtained through APIs and data feeds provided by the railway company. The server then uses this data to generate an optimized support plan for users. This plan includes which train cars and doors should be used, when support is needed, and also provides instructions for the efficient deployment of support personnel.

[0216] After a support plan is generated, the server notifies the user via a terminal. This terminal can be a mobile device or a personal computer, allowing the user to review the plan and use it to plan their journey. This notification includes specific boarding locations, available accessible routes, and predicted congestion information.

[0217] During actual travel, the terminal continuously provides users with real-time information. This includes the current location of the train, delay information, and equipment usage status. Users will also be notified of scheduled support personnel as needed, ensuring they can travel with peace of mind.

[0218] This system will enable the effective use of limited human resources and create an environment where wheelchair users can use the railway with peace of mind. Furthermore, it will allow users to travel more systematically and efficiently, providing significant benefits to railway operators in terms of operations and service provision.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] Users access a dedicated app or web portal and enter detailed travel information. This includes the travel date, departure station, arrival station, desired departure time, and required assistance. This information is sent to the server and registered in the database.

[0222] Step 2:

[0223] The server uses APIs provided by railway companies to collect real-time information such as accessibility facilities at each station, train operation status, and congestion levels. This ensures that the system is always updated with the latest information.

[0224] Step 3:

[0225] The server uses an AI algorithm to generate the optimal support plan based on travel information provided by the user and collected real-time data. This includes recommending a suitable vehicle, the appropriate boarding and alighting locations, and assigning appropriate support personnel.

[0226] Step 4:

[0227] The server notifies the user's device of the generated support plan. The notification includes specific vehicle information, the barrier-free route for the day, congestion forecasts, and information on the deployment of support staff.

[0228] Step 5:

[0229] On the day of travel, the terminal will continuously provide real-time updates. Users can receive the latest information through the terminal regarding train delays, equipment usage, and the deployment status of support personnel.

[0230] Step 6:

[0231] After the transfer is complete, users can send feedback on the assistance provided to the system to help improve future services. This feedback is stored on the server and used to improve future assistance plans.

[0232] (Example 1)

[0233] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0234] In modern society, as the use of public transportation continues to increase, there is a growing need to provide smooth and safe transportation for users who have difficulty traveling. However, current systems lack sufficient real-time information provision and appropriate allocation of human resources, making it difficult to support efficient travel.

[0235] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0236] In this invention, the server includes means for registering travel information provided by the user, means for collecting transportation facility information, vehicle information in operation, and congestion status in real time, means for generating an optimal support plan based on the collected information, and means for providing real-time information during travel via the user's terminal. This makes it possible to provide an optimal travel plan tailored to the user's needs and to provide reliable real-time information during travel.

[0237] "Travel information provided by users" refers to information provided by users regarding their travel, including the date and time of their trip, departure point, destination, and the type of assistance they require.

[0238] "Transportation facility information" refers to data regarding the operational status and availability of various facilities installed at stations and on trains.

[0239] "Information on vehicles in operation" refers to data regarding the current location and operating schedule of public transportation such as trains and buses.

[0240] "Congestion status" refers to information regarding the degree of congestion and occupancy rate within public transportation.

[0241] "Means for generating optimal support plans" refers to a process that automatically creates specific support plans to facilitate user mobility based on collected mobility and equipment information.

[0242] "Means of notification" refers to technology that sends the generated support plan to the user's device and informs the user of its contents.

[0243] "Means of providing real-time information" refers to a system that continuously updates and provides information on the current location and status of transportation so that users can always stay informed of the latest situation while on the move.

[0244] The system of this invention mainly consists of a server, a terminal, and a user. The following specific steps are taken to implement the invention.

[0245] First, users enter their travel information using a dedicated application or web portal. This includes the date and time of travel, departure point, destination, and required assistance. To ensure accurate registration of this information, the interface employs a user-friendly design.

[0246] Next, the server uses transportation APIs and external data feeds to collect real-time information on actual train operations and station facilities in order to process the user's travel information mentioned above. The server incorporates algorithms for data collection and plan generation.

[0247] The server generates an optimal mobility assistance plan based on collected information and the user's travel data. This plan includes specific instructions such as which vehicle and which door to use, the timing to avoid congestion during travel, and the deployment of necessary support personnel.

[0248] After a support plan is generated, the server sends it to the user's device. This device can be the user's mobile device or computer. This makes it easier for the user to review the plan they receive.

[0249] The device continues to provide real-time updated information while the user is on the move, including the current train location, delay information, and station equipment usage status. It also facilitates smooth communication with support personnel based on the generated support plan.

[0250] As a concrete example, to ensure smooth movement for wheelchair users at train stations, the system includes information on whether or not elevators will be used in the plan and suggests appropriate boarding positions for the user. In this way, the system realizes the optimal travel experience within limited time and resources.

[0251] An example of a prompt message would be, "Please suggest the optimal travel plan for a wheelchair user using the train."

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] Users log in to a dedicated application or web portal and register their travel details. This information includes the date and time, departure point, destination, and required assistance. The entered data is sent to a server and securely stored in a database. Here, travel information based on the user's needs is collected in digital format.

[0255] Step 2:

[0256] The server collects real-time information on station facilities, trains in operation, and congestion levels through transportation APIs and external data feeds. Using this information as input, the server integrates and cross-references the collected data to perform analysis that determines the most suitable travel routes and timings for users. Data processing includes predicting current congestion levels and delay risks. The analyzed data is then fed into an internal analysis model as output.

[0257] Step 3:

[0258] The server generates an optimal support plan for the user's travel based on the analyzed data. This generation uses a generative AI model that automatically formulates the plan best suited to each user's needs. The input is the analyzed data obtained in step 2, and the output is a specific support plan. This support plan includes details such as which vehicle door to use, the shortest travel route, and the most appropriate time allocation.

[0259] Step 4:

[0260] The server notifies the device of the generated assistance plan. The device is the user's mobile device or computer, and upon receiving the notification, the user can view the plan details. Specifically, a real-time navigation guide is displayed on the user's screen via a push notification to the device. This notification includes the assistance plan, and the user begins their journey based on it.

[0261] Step 5:

[0262] While on the move, the terminal continuously provides the user with the latest information. This includes real-time updates on train delays, current location, and station facility usage. Input here is real-time information continuously provided by the server. Data processing involves updating environment variables as the user moves and suggesting the optimal action based on predictions. The user receives immediate notifications through the terminal, enabling them to take timely and appropriate action.

[0263] (Application Example 1)

[0264] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] Users of public transport in urban areas face the problem of not being able to accurately grasp real-time traffic conditions and congestion levels, making it difficult to plan their travel efficiently. This can lead to unexpected delays and congestion that disrupt comfortable travel. Furthermore, transportation providers also face the challenge of optimizing resource allocation.

[0266] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0267] In this invention, the server includes means for registering travel information provided by users, means for collecting information on transportation facilities, information on the operation of moving objects, and congestion status in real time, and means for generating an optimal travel plan based on the travel information and the collected information. This enables users to move efficiently within cities and allows transportation providers to optimize their resources.

[0268] "Travel information" refers to data provided by the user, such as the origin, destination, date and time of travel, and the type of support needed.

[0269] "Transportation facility information" refers to information about stations, bus stops, and train and bus facilities, including access methods and the availability of barrier-free facilities.

[0270] "Mobile transportation operation information" refers to data showing the operating status of public transportation such as trains and buses, including information such as operating times and delay status.

[0271] "Congestion status" refers to data that indicates the density of people and vehicles in public transportation and road conditions.

[0272] A "travel plan" is a plan that provides the optimal travel procedure and route based on the user's input information and real-time traffic information.

[0273] "Support personnel" are individuals assigned to facilitate the smooth movement of users, and their efficient allocation is ensured.

[0274] In implementing this invention, the server first receives travel information provided by the user through a web application or mobile application. This information includes data such as the origin, destination, date and time of travel, and the type of assistance required. The server registers this travel information in a database and collects real-time information on transportation facilities, vehicle operation information, and congestion status through a public transportation API. This includes operating hours, delay information, and accessibility status of facilities.

[0275] The server uses an AI engine based on Python and TensorFlow to analyze collected data and generate an optimal travel plan for the user. This travel plan includes the best boarding location, available barrier-free routes, and predicted congestion information. The generated travel plan is then notified to the user using the most appropriate method. The notification is delivered to a smartphone or tablet. Based on this information, the user can travel effectively and without stress.

[0276] As a concrete example, when a user arrives at their planned departure station based on a travel plan registered on their smartphone, the following prompt message will appear in the user's app: "Please tell me the best route to my next destination, avoiding congestion." In this case, by following the suggested route, the user can avoid congestion and reach their destination smoothly.

[0277] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0278] Step 1:

[0279] Users enter travel information through a smartphone application. This information includes departure point, destination, travel date and time, and required assistance. The entered travel information is sent to a server and registered in a database.

[0280] Step 2:

[0281] Based on the registered movement information, the server collects real-time information on transportation facilities, the operation information of moving objects, and congestion status through a public transportation API. It receives data provided from the transportation API as input and obtains information including the degree of congestion and travel time.

[0282] Step 3:

[0283] Based on the acquired data and the user's movement information, the server uses Python and TensorFlow to generate an optimal movement plan with an AI engine. In this process, the data is preprocessed, and the generated AI model is utilized to calculate the optimal route and timing. As output, a movement plan including the optimal boarding location and required time is obtained.

[0284] Step 4:

[0285] The generated movement plan is notified from the server to the user's smartphone. The user's terminal receives a push notification and displays the details of the plan on the screen. The user makes actual movements based on this reference.

[0286] Step 5:

[0287] During the movement, the user's terminal continuously receives real-time information updates from the server. The current position of the moving object and unexpected delay information are provided regularly, and the user can adjust the plan according to the situation.

[0288] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0289] The system for implementing this invention provides more individualized support by integrating an emotion engine in addition to the generation and provision of a user's movement support plan. The system mainly consists of a user, a server, and a terminal.

[0290] First, the user enters travel details using a dedicated application or web portal. This includes the travel date, departure station, arrival station, desired travel time, and required assistance. The provided information is sent to the server and registered in the database.

[0291] The server uses the railway company's API to collect real-time information on station and train facilities, operating status, and congestion levels. Furthermore, the server uses an emotion engine to analyze users' emotions based on input data and past feedback. The results of this analysis are considered when creating support plans.

[0292] The generated support plan is adjusted according to the user's emotional state. For example, if the user appears tense, more detailed instructions will be provided. Conversely, if the user is relaxed, only the bare minimum of information will be provided to reduce their stress.

[0293] Next, the server notifies the terminal of the adjusted support plan. The notification includes specific vehicle information, potential congestion information for the day, and customized guidance for the user. This allows the user to respond appropriately at each step of their journey.

[0294] Finally, after the journey is complete, users provide feedback, and the system uses this information to further improve the plan. This feedback is used for analysis by an emotion engine and utilized in creating the next support plan. For example, if a user felt uneasy in an area last time, additional support staff will be assigned to that area next time. This improves user satisfaction during travel and enhances the quality of support provided by the railway company.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] Users access a dedicated app or web portal and enter their travel schedule. They enter the travel date, departure station, arrival station, departure time, and any necessary support. This data is sent to the server and automatically stored in the database.

[0298] Step 2:

[0299] The server obtains real-time information on station and train accessibility facilities, operating status, and congestion levels via the railway company's API. This information is aggregated as data necessary for generating subsequent support plans.

[0300] Step 3:

[0301] The server analyzes the user's emotions using an emotion engine based on the user's past movement data and input information. It uses facial recognition and voice analysis functions to infer the user's emotional state and records it in an emotion database.

[0302] Step 4:

[0303] By comparing the emotion analysis results with collected real-time information, the server generates the optimal support plan. This plan is adjusted according to the user's emotions; for example, if stress levels are high, additional guidance will be provided.

[0304] Step 5:

[0305] The server sends the generated assistance plan to the terminal and notifies the user. The notification includes recommended vehicles and locations, congestion forecasts, and personalized, emotionally responsive guidance, allowing the user to proceed with their journey.

[0306] Step 6:

[0307] While on the move, the device provides users with real-time updates. If there are any changes in the environment or situation, the latest information, including emotional responses, is immediately transmitted to the user based on instructions from the server.

[0308] Step 7:

[0309] After the movement is completed, the user provides impressions and feedback within the application. This feedback is used to improve the subsequent support plans by the emotion engine and contributes to the improvement of the overall system performance.

[0310] (Example 2)

[0311] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0312] In the conventional movement support system, individualized support considering the user's emotional state was not provided, and the comfort and satisfaction of the user could not be sufficiently enhanced. Also, in terms of real-time information acquisition and the arrangement of support personnel, there was a lack of efficiency, and it was difficult to provide prompt and accurate support to the user.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0314] In this invention, the server includes means for recording the movement details provided by the user, means for acquiring facility information, means for acquiring traffic means information in operation, and means for analyzing the emotional state of the user in real time. As a result, it becomes possible to provide an adjustment support plan tailored to the individual needs of the user, and the sense of security and satisfaction during movement can be improved.

[0315] The "user" refers to an individual or a group that receives support regarding movement using the system.

[0316] The "movement details" refer to specific information such as the date and time of movement, departure point, arrival point, and the content of support required provided by the user.

[0317] "Means of recording" refers to methods and devices for saving user movement details to a database or other recording media.

[0318] "Facility information" refers to available facilities, installation status, and accessibility information for stations and other transportation facilities.

[0319] "Information on transportation in operation" refers to the operating schedules, delay information, and route information for public transportation such as trains and buses.

[0320] "Congestion status" refers to information regarding the degree of crowding among people on a particular mode of transport, at a station, or within a facility.

[0321] "Means of acquiring data in real time" refers to technologies and processes that enable the immediate acquisition of the latest status and conditions, making them available for use in the system.

[0322] "Emotional state" refers to the results of evaluating and analyzing the user's psychological state and emotional responses.

[0323] "Means of analysis" refers to technical methods and devices used to evaluate the user's condition and needs based on collected data and information.

[0324] A "support adjustment plan" refers to a mobility support plan that is optimized and proposed based on the individual circumstances of the user, using collected and analyzed information.

[0325] "Means of communication" refers to the technical methods and devices used to transmit the generated support plan to the user.

[0326] This invention is a system that personalizes user mobility assistance, and is operated with clearly defined roles for the server, terminal, and user. The server collects and analyzes information provided by the user and generates an optimal mobility assistance plan. The following hardware and software are used for implementation.

[0327] The server utilizes a database management system, such as an RDBMS like PostgreSQL, to store travel details. It also employs communication protocols to obtain facility and operational information via APIs from railway companies and public transport organizations. For real-time data acquisition, it uses WebSockets as an asynchronous communication technology. Furthermore, to analyze emotional states, it leverages a generative AI model that performs natural language processing, utilizing the open-source library TensorFlow.

[0328] The device provides an interface for users to enter travel details and receive a plan. For example, this could be a dedicated application running on iOS or Android. This app would feature an intuitive UI to facilitate user interaction and would include form input and notification functions.

[0329] Users access the mobility assistance system via mobile devices or computers. For example, a parent with young children might input, "Please generate a plan for traveling from Tokyo Station to Shin-Osaka Station with a stroller next Sunday," and the system would notify them of the necessary assistance, enabling a smooth journey.

[0330] An example of a prompt message is, "Create a travel assistance plan for a wheelchair user from Tokyo to Shin-Osaka. Include information on priority seating and elevators." Based on this input, an assistance plan tailored to the user's needs will be generated and provided.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] Users access the system's dedicated application or web portal using a terminal and enter detailed travel information. Input fields include the travel date, departure station, arrival station, desired time, and required assistance. For example, if a user enters "November 5, 2023, departing from Tokyo Station, bound for Shin-Osaka Station, desired time 10:00, wheelchair user," this information is formatted as data. After formatting, the input data is sent to the server.

[0334] Step 2:

[0335] The server receives the movement details sent by the user and registers them in the database. At this time, the database stores detailed information along with the user's movement history. This will enable pattern recognition and data analysis in the future. The output from the database confirms that the information is available for use by other modules.

[0336] Step 3:

[0337] The server uses APIs from external transportation companies to collect facility information, operating status, and congestion information in real time. This information collection includes, for example, "station elevator operational status" and "train delay information." Since the collected data is disorganized in its raw form, it is converted into a format that is easy to analyze and stored in preparation for the next processing step.

[0338] Step 4:

[0339] The server uses an emotion engine to analyze the user's emotional state. It analyzes user feedback and past usage data based on natural language processing techniques to infer how the user is currently feeling and what kind of support they need. Input is text and user responses, and output is the analyzed emotion data.

[0340] Step 5:

[0341] The server uses a generative AI model to create a support plan based on the acquired real-time data and sentiment analysis results. The generated plan includes specific vehicles to ride, available facilities, and routes to avoid congestion. The generated data is formatted in a way that is realistically understandable to the user.

[0342] Step 6:

[0343] The server notifies the terminal of the created support plan. The terminal displays the notification in a way that is easy for the user to understand, and this includes providing it as audio guidance or visual information. For example, instructions such as "Today, you will be in car number 1 with your assigned seat, and you will use the elevator at the front" are visualized.

[0344] Step 7:

[0345] After the journey is complete, the user provides feedback about the journey using their device. The feedback is sent to the server and analyzed again by the emotion engine. This provides data that can be used to improve future support plans, and the analysis results are stored in a database.

[0346] (Application Example 2)

[0347] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0348] In modern society, there is a demand for services that reduce stress and anxiety and provide a comfortable experience when citizens move around within cities. Therefore, personalized mobility support that takes into account the emotional state of users is necessary, and improving the quality of support for people with specific needs, such as visitors and the elderly, is a particular challenge.

[0349] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0350] In this invention, the server includes means for registering travel information and emotional information provided by the user, means for collecting station equipment information, operating vehicle information, and congestion status in real time, and means for generating an optimal and emotionally adaptive support plan based on the travel information, emotional information, and the collected information. This makes it possible to provide a personalized support plan that corresponds to the user's emotional state.

[0351] "User" refers to an individual who uses this system and receives mobility assistance.

[0352] "Travel information" refers to detailed travel data provided by users, such as departure point, destination, and travel time.

[0353] "Emotional information" refers to data about the user's current emotional state, including stress levels and relaxation levels.

[0354] "Station facilities information" refers to information indicating the location and availability of elevators, escalators, and ticket gates within the station.

[0355] "Information on vehicles in operation" refers to information regarding the location and timetable of public transportation such as trains and buses that are currently in service.

[0356] "Congestion status" refers to information regarding the degree of congestion at stations and on trains, as well as information regarding expected congestion levels.

[0357] An "optimal and emotionally adaptive support plan" refers to a customized mobility guidance and support plan tailored to the user's needs and emotional state, based on collected information and emotional data.

[0358] "Support staff" refers to personnel who provide assistance and guidance to users on-site.

[0359] "Adaptive guidance" refers to a method of providing personalized travel information tailored to the user's current situation and emotions.

[0360] To implement this invention, the system consists of a server, a user's terminal, and the user. First, the user uses a smartphone or smart glasses to input travel information and emotional information through a dedicated application. Here, travel information includes the departure point, destination, and desired travel time, while emotional information includes the user's stress level and degree of relaxation.

[0361] The server utilizes railway company APIs and other transportation information APIs to collect real-time information on station and line facilities, operating status, and congestion levels. This information is combined with user sentiment information using a generative AI model to create optimal and emotionally adaptive support plans. For example, it suggests routes that avoid congestion and places where users can relax, ensuring a comfortable journey.

[0362] The generated support plan is notified to the user's device, and the user travels along the designated route. This ensures a stress-free journey for the user. After completing the journey, the user can send feedback again from their device, which helps to improve the support plan for future journeys.

[0363] As a concrete example, when tourists visiting Japan for the first time efficiently tour tourist attractions in a new city, the system can provide transportation support that reduces anxiety about the Japanese language while allowing them to experience the culture. In such cases, the system generates guidance that takes the tourist's feelings into consideration, enabling smooth movement between tourist spots.

[0364] An example of a prompt message for a generative AI model is as follows:

[0365] "The user is a tourist visiting Japan, wants to travel from Shibuya Station to Asakusa Station, and is feeling anxious about their first visit to Japan. Please generate travel suggestions that take their cultural background into consideration and provide them with a sense of security."

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The user enters their departure point, destination, desired travel time, and emotional state using a smartphone or smart glasses app. This input data is sent from the device to the server. The server receives the user's travel information and emotional state as input and registers it in its database.

[0369] Step 2:

[0370] The server collects real-time facility information, operating status, and congestion data for stations and trains in operation through railway company APIs and traffic information APIs. The data obtained from these APIs is organized within the system and converted into a readily usable format. The collected information is recorded along with user travel information.

[0371] Step 3:

[0372] The server uses a generative AI model to analyze user movement information, emotional information, and collected real-time data. This analysis process generates an appropriate support plan. In the initial stages of the analysis, it recognizes the user's emotional state and creates relaxing guidance, especially for users experiencing tension or anxiety. The output is a personalized, optimal, and emotionally adaptive support plan.

[0373] Step 4:

[0374] The server notifies the user's device of the generated assistance plan. The notified plan includes, for example, the optimal route to avoid congestion and relaxation points that take emotions into consideration. The device displays this information in an easy-to-understand manner for the user, making it usable as a guide while traveling.

[0375] Step 5:

[0376] After the journey, the user enters feedback from their device and sends it to the server. This feedback is stored in a database and used when creating future support plans. The feedback process involves data analysis based on changes in the user's emotional state and their experience during the journey, in order to improve the accuracy of the plan.

[0377] 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.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0384] 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.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0386] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] 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.

[0388] 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.

[0389] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The 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.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] In implementing this invention, a series of systems are configured to improve user convenience and maximize the efficiency of railway operators. This system consists of three main components: users, servers, and terminals.

[0394] First, users register their travel information using a dedicated application or web portal. This information includes the date and time of travel, departure point, destination, and the type of assistance required. This allows the server to understand the user's specific needs.

[0395] Next, the server collects real-time information on station and train facilities, congestion levels, and operational status. This information is obtained through APIs and data feeds provided by the railway company. The server then uses this data to generate an optimized support plan for users. This plan includes which train cars and doors should be used, when support is needed, and also provides instructions for the efficient deployment of support personnel.

[0396] After a support plan is generated, the server notifies the user via a terminal. This terminal can be a mobile device or a personal computer, allowing the user to review the plan and use it to plan their journey. This notification includes specific boarding locations, available accessible routes, and predicted congestion information.

[0397] During actual travel, the terminal continuously provides users with real-time information. This includes the current location of the train, delay information, and equipment usage status. Users will also be notified of scheduled support personnel as needed, ensuring they can travel with peace of mind.

[0398] This system will enable the effective use of limited human resources and create an environment where wheelchair users can use the railway with peace of mind. Furthermore, it will allow users to travel more systematically and efficiently, providing significant benefits to railway operators in terms of operations and service provision.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] Users access a dedicated app or web portal and enter detailed travel information. This includes the travel date, departure station, arrival station, desired departure time, and required assistance. This information is sent to the server and registered in the database.

[0402] Step 2:

[0403] The server uses APIs provided by railway companies to collect real-time information such as accessibility facilities at each station, train operation status, and congestion levels. This ensures that the system is always updated with the latest information.

[0404] Step 3:

[0405] The server uses an AI algorithm to generate the optimal support plan based on travel information provided by the user and collected real-time data. This includes recommending a suitable vehicle, the appropriate boarding and alighting locations, and assigning appropriate support personnel.

[0406] Step 4:

[0407] The server notifies the user's device of the generated support plan. The notification includes specific vehicle information, the barrier-free route for the day, congestion forecasts, and information on the deployment of support staff.

[0408] Step 5:

[0409] On the day of travel, the terminal will continuously provide real-time updates. Users can receive the latest information through the terminal regarding train delays, equipment usage, and the deployment status of support personnel.

[0410] Step 6:

[0411] After the transfer is complete, users can send feedback on the assistance provided to the system to help improve future services. This feedback is stored on the server and used to improve future assistance plans.

[0412] (Example 1)

[0413] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0414] In modern society, as the use of public transportation continues to increase, there is a growing need to provide smooth and safe transportation for users who have difficulty traveling. However, current systems lack sufficient real-time information provision and appropriate allocation of human resources, making it difficult to support efficient travel.

[0415] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0416] In this invention, the server includes means for registering travel information provided by the user, means for collecting transportation facility information, vehicle information in operation, and congestion status in real time, means for generating an optimal support plan based on the collected information, and means for providing real-time information during travel via the user's terminal. This makes it possible to provide an optimal travel plan tailored to the user's needs and to provide reliable real-time information during travel.

[0417] "Travel information provided by users" refers to information provided by users regarding their travel, including the date and time of their trip, departure point, destination, and the type of assistance they require.

[0418] "Transportation facility information" refers to data regarding the operational status and availability of various facilities installed at stations and on trains.

[0419] "Information on vehicles in operation" refers to data regarding the current location and operating schedule of public transportation such as trains and buses.

[0420] "Congestion status" refers to information regarding the degree of congestion and occupancy rate within public transportation.

[0421] "Means for generating optimal support plans" refers to a process that automatically creates specific support plans to facilitate user mobility based on collected mobility and equipment information.

[0422] "Means of notification" refers to technology that sends the generated support plan to the user's device and informs the user of its contents.

[0423] "Means of providing real-time information" refers to a system that continuously updates and provides information on the current location and status of transportation so that users can always stay informed of the latest situation while on the move.

[0424] The system of this invention mainly consists of a server, a terminal, and a user. The following specific steps are taken to implement the invention.

[0425] First, users enter their travel information using a dedicated application or web portal. This includes the date and time of travel, departure point, destination, and required assistance. To ensure accurate registration of this information, the interface employs a user-friendly design.

[0426] Next, the server uses transportation APIs and external data feeds to collect real-time information on actual train operations and station facilities in order to process the user's travel information mentioned above. The server incorporates algorithms for data collection and plan generation.

[0427] The server generates an optimal mobility assistance plan based on collected information and the user's travel data. This plan includes specific instructions such as which vehicle and which door to use, the timing to avoid congestion during travel, and the deployment of necessary support personnel.

[0428] After a support plan is generated, the server sends it to the user's device. This device can be the user's mobile device or computer. This makes it easier for the user to review the plan they receive.

[0429] The device continues to provide real-time updated information while the user is on the move, including the current train location, delay information, and station equipment usage status. It also facilitates smooth communication with support personnel based on the generated support plan.

[0430] As a concrete example, to ensure smooth movement for wheelchair users at train stations, the system includes information on whether or not elevators will be used in the plan and suggests appropriate boarding positions for the user. In this way, the system realizes the optimal travel experience within limited time and resources.

[0431] An example of a prompt message would be, "Please suggest the optimal travel plan for a wheelchair user using the train."

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] Users log in to a dedicated application or web portal and register their travel details. This information includes the date and time, departure point, destination, and required assistance. The entered data is sent to a server and securely stored in a database. Here, travel information based on the user's needs is collected in digital format.

[0435] Step 2:

[0436] The server collects real-time information on station facilities, trains in operation, and congestion levels through transportation APIs and external data feeds. Using this information as input, the server integrates and cross-references the collected data to perform analysis that determines the most suitable travel routes and timings for users. Data processing includes predicting current congestion levels and delay risks. The analyzed data is then fed into an internal analysis model as output.

[0437] Step 3:

[0438] The server generates an optimal support plan for the user's travel based on the analyzed data. This generation uses a generative AI model that automatically formulates the plan best suited to each user's needs. The input is the analyzed data obtained in step 2, and the output is a specific support plan. This support plan includes details such as which vehicle door to use, the shortest travel route, and the most appropriate time allocation.

[0439] Step 4:

[0440] The server notifies the device of the generated assistance plan. The device is the user's mobile device or computer, and upon receiving the notification, the user can view the plan details. Specifically, a real-time navigation guide is displayed on the user's screen via a push notification to the device. This notification includes the assistance plan, and the user begins their journey based on it.

[0441] Step 5:

[0442] While on the move, the terminal continuously provides the user with the latest information. This includes real-time updates on train delays, current location, and station facility usage. Input here is real-time information continuously provided by the server. Data processing involves updating environment variables as the user moves and suggesting the optimal action based on predictions. The user receives immediate notifications through the terminal, enabling them to take timely and appropriate action.

[0443] (Application Example 1)

[0444] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0445] Users of public transport in urban areas face the problem of not being able to accurately grasp real-time traffic conditions and congestion levels, making it difficult to plan their travel efficiently. This can lead to unexpected delays and congestion that disrupt comfortable travel. Furthermore, transportation providers also face the challenge of optimizing resource allocation.

[0446] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0447] In this invention, the server includes means for registering travel information provided by users, means for collecting information on transportation facilities, information on the operation of moving objects, and congestion status in real time, and means for generating an optimal travel plan based on the travel information and the collected information. This enables users to move efficiently within cities and allows transportation providers to optimize their resources.

[0448] "Travel information" refers to data provided by the user, such as the origin, destination, date and time of travel, and the type of support needed.

[0449] "Transportation facility information" refers to information about stations, bus stops, and train and bus facilities, including access methods and the availability of barrier-free facilities.

[0450] "Mobile transportation operation information" refers to data showing the operating status of public transportation such as trains and buses, including information such as operating times and delay status.

[0451] "Congestion status" refers to data that indicates the density of people and vehicles in public transportation and road conditions.

[0452] A "travel plan" is a plan that provides the optimal travel procedure and route based on the user's input information and real-time traffic information.

[0453] "Support personnel" are individuals assigned to facilitate the smooth movement of users, and their efficient allocation is ensured.

[0454] In implementing this invention, the server first receives travel information provided by the user through a web application or mobile application. This information includes data such as the origin, destination, date and time of travel, and the type of assistance required. The server registers this travel information in a database and collects real-time information on transportation facilities, vehicle operation information, and congestion status through a public transportation API. This includes operating hours, delay information, and accessibility status of facilities.

[0455] The server uses an AI engine based on Python and TensorFlow to analyze collected data and generate an optimal travel plan for the user. This travel plan includes the best boarding location, available barrier-free routes, and predicted congestion information. The generated travel plan is then notified to the user using the most appropriate method. The notification is delivered to a smartphone or tablet. Based on this information, the user can travel effectively and without stress.

[0456] As a concrete example, when a user arrives at their planned departure station based on a travel plan registered on their smartphone, the following prompt message will appear in the user's app: "Please tell me the best route to my next destination, avoiding congestion." In this case, by following the suggested route, the user can avoid congestion and reach their destination smoothly.

[0457] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0458] Step 1:

[0459] Users enter travel information through a smartphone application. This information includes departure point, destination, travel date and time, and required assistance. The entered travel information is sent to a server and registered in a database.

[0460] Step 2:

[0461] The server collects information on transportation facilities, vehicle operation information, and congestion status in real time via the public transportation API, based on registered travel information. It receives data provided by the transportation API as input and obtains information including congestion levels and operating times.

[0462] Step 3:

[0463] The server uses Python and TensorFlow to generate an optimal travel plan using an AI engine based on acquired data and user movement information. During this process, the data is preprocessed, and the generated AI model is used to calculate the optimal route and timing. The output is a travel plan that includes the optimal boarding location and estimated travel time.

[0464] Step 4:

[0465] The generated travel plan is sent from the server to the user's smartphone. The user's device receives the push notification and displays the plan details on the screen. The user then uses this as a reference to carry out their actual travel.

[0466] Step 5:

[0467] While in transit, the user's device continuously receives real-time information updates from the server. Current location and unexpected delay information are provided periodically, allowing the user to adjust their plan as needed.

[0468] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0469] The system for implementing this invention provides more personalized assistance by integrating an emotion engine, in addition to generating and providing user mobility assistance plans. The system consists primarily of a user, a server, and a terminal.

[0470] First, the user enters travel details using a dedicated application or web portal. This includes the travel date, departure station, arrival station, desired travel time, and required assistance. The provided information is sent to the server and registered in the database.

[0471] The server uses the railway company's API to collect real-time information on station and train facilities, operating status, and congestion levels. Furthermore, the server uses an emotion engine to analyze users' emotions based on input data and past feedback. The results of this analysis are considered when creating support plans.

[0472] The generated support plan is adjusted according to the user's emotional state. For example, if the user appears tense, more detailed instructions will be provided. Conversely, if the user is relaxed, only the bare minimum of information will be provided to reduce their stress.

[0473] Next, the server notifies the terminal of the adjusted support plan. The notification includes specific vehicle information, potential congestion information for the day, and customized guidance for the user. This allows the user to respond appropriately at each step of their journey.

[0474] Finally, after the journey is complete, users provide feedback, and the system uses this information to further improve the plan. This feedback is used for analysis by an emotion engine and utilized in creating the next support plan. For example, if a user felt uneasy in an area last time, additional support staff will be assigned to that area next time. This improves user satisfaction during travel and enhances the quality of support provided by the railway company.

[0475] The following describes the processing flow.

[0476] Step 1:

[0477] Users access a dedicated app or web portal and enter their travel schedule. They enter the travel date, departure station, arrival station, departure time, and any necessary support. This data is sent to the server and automatically stored in the database.

[0478] Step 2:

[0479] The server obtains real-time information on station and train accessibility facilities, operating status, and congestion levels via the railway company's API. This information is aggregated as data necessary for generating subsequent support plans.

[0480] Step 3:

[0481] The server analyzes the user's emotions using an emotion engine based on the user's past movement data and input information. It uses facial recognition and voice analysis functions to infer the user's emotional state and records it in an emotion database.

[0482] Step 4:

[0483] By comparing the emotion analysis results with collected real-time information, the server generates the optimal support plan. This plan is adjusted according to the user's emotions; for example, if stress levels are high, additional guidance will be provided.

[0484] Step 5:

[0485] The server sends the generated assistance plan to the terminal and notifies the user. The notification includes recommended vehicles and locations, congestion forecasts, and personalized, emotionally responsive guidance, allowing the user to proceed with their journey.

[0486] Step 6:

[0487] While on the move, the device provides users with real-time updates. If there are any changes in the environment or situation, the latest information, including emotional responses, is immediately transmitted to the user based on instructions from the server.

[0488] Step 7:

[0489] After completing the journey, users provide feedback and comments within the app. This feedback is used by the emotion engine to improve future support plans, contributing to the overall performance of the system.

[0490] (Example 2)

[0491] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0492] Conventional mobility assistance systems failed to provide individualized support that took into account the user's emotional state, and thus could not adequately enhance user comfort and satisfaction. Furthermore, they lacked efficiency in real-time information acquisition and the deployment of support personnel, making it difficult to provide prompt and accurate support to users.

[0493] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0494] In this invention, the server includes means for recording travel details provided by the user, means for acquiring equipment information, transportation information during operation, and congestion status in real time, and means for analyzing the user's emotional state. This makes it possible to provide an adjustment support plan tailored to the individual needs of the user, thereby improving their sense of security and satisfaction during travel.

[0495] "User" refers to an individual or group that uses the system to receive assistance related to mobility.

[0496] "Travel details" refers to specific information provided by the user, such as the date and time of travel, departure point, arrival point, and the type of support required.

[0497] "Means of recording" refers to methods and devices for saving user movement details to a database or other recording media.

[0498] "Facility information" refers to available facilities, installation status, and accessibility information for stations and other transportation facilities.

[0499] "Information on transportation in operation" refers to the operating schedules, delay information, and route information for public transportation such as trains and buses.

[0500] "Congestion status" refers to information regarding the degree of crowding among people on a particular mode of transport, at a station, or within a facility.

[0501] "Means of acquiring data in real time" refers to technologies and processes that enable the immediate acquisition of the latest status and conditions, making them available for use in the system.

[0502] "Emotional state" refers to the results of evaluating and analyzing the user's psychological state and emotional responses.

[0503] "Means of analysis" refers to technical methods and devices used to evaluate the user's condition and needs based on collected data and information.

[0504] A "support adjustment plan" refers to a mobility support plan that is optimized and proposed based on the individual circumstances of the user, using collected and analyzed information.

[0505] "Means of communication" refers to the technical methods and devices used to transmit the generated support plan to the user.

[0506] This invention is a system that personalizes user mobility assistance, and is operated with clearly defined roles for the server, terminal, and user. The server collects and analyzes information provided by the user and generates an optimal mobility assistance plan. The following hardware and software are used for implementation.

[0507] The server utilizes a database management system, such as an RDBMS like PostgreSQL, to store travel details. It also employs communication protocols to obtain facility and operational information via APIs from railway companies and public transport organizations. For real-time data acquisition, it uses WebSockets as an asynchronous communication technology. Furthermore, to analyze emotional states, it leverages a generative AI model that performs natural language processing, utilizing the open-source library TensorFlow.

[0508] The device provides an interface for users to enter travel details and receive a plan. For example, this could be a dedicated application running on iOS or Android. This app would feature an intuitive UI to facilitate user interaction and would include form input and notification functions.

[0509] Users access the mobility assistance system via mobile devices or computers. For example, a parent with young children might input, "Please generate a plan for traveling from Tokyo Station to Shin-Osaka Station with a stroller next Sunday," and the system would notify them of the necessary assistance, enabling a smooth journey.

[0510] An example of a prompt message is, "Create a travel assistance plan for a wheelchair user from Tokyo to Shin-Osaka. Include information on priority seating and elevators." Based on this input, an assistance plan tailored to the user's needs will be generated and provided.

[0511] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0512] Step 1:

[0513] Users access the system's dedicated application or web portal using a terminal and enter detailed travel information. Input fields include the travel date, departure station, arrival station, desired time, and required assistance. For example, if a user enters "November 5, 2023, departing from Tokyo Station, bound for Shin-Osaka Station, desired time 10:00, wheelchair user," this information is formatted as data. After formatting, the input data is sent to the server.

[0514] Step 2:

[0515] The server receives the movement details sent by the user and registers them in the database. At this time, the database stores detailed information along with the user's movement history. This will enable pattern recognition and data analysis in the future. The output from the database confirms that the information is available for use by other modules.

[0516] Step 3:

[0517] The server uses APIs from external transportation companies to collect facility information, operating status, and congestion information in real time. This information collection includes, for example, "station elevator operational status" and "train delay information." Since the collected data is disorganized in its raw form, it is converted into a format that is easy to analyze and stored in preparation for the next processing step.

[0518] Step 4:

[0519] The server uses an emotion engine to analyze the user's emotional state. It analyzes user feedback and past usage data based on natural language processing techniques to infer how the user is currently feeling and what kind of support they need. Input is text and user responses, and output is the analyzed emotion data.

[0520] Step 5:

[0521] The server uses a generative AI model to create a support plan based on the acquired real-time data and sentiment analysis results. The generated plan includes specific vehicles to ride, available facilities, and routes to avoid congestion. The generated data is formatted in a way that is realistically understandable to the user.

[0522] Step 6:

[0523] The server notifies the terminal of the created support plan. The terminal displays the notification in a way that is easy for the user to understand, and this includes providing it as audio guidance or visual information. For example, instructions such as "Today, you will be in car number 1 with your assigned seat, and you will use the elevator at the front" are visualized.

[0524] Step 7:

[0525] After the journey is complete, the user provides feedback about the journey using their device. The feedback is sent to the server and analyzed again by the emotion engine. This provides data that can be used to improve future support plans, and the analysis results are stored in a database.

[0526] (Application Example 2)

[0527] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0528] In modern society, there is a demand for services that reduce stress and anxiety and provide a comfortable experience when citizens move around within cities. Therefore, personalized mobility support that takes into account the emotional state of users is necessary, and improving the quality of support for people with specific needs, such as visitors and the elderly, is a particular challenge.

[0529] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0530] In this invention, the server includes means for registering travel information and emotional information provided by the user, means for collecting station equipment information, operating vehicle information, and congestion status in real time, and means for generating an optimal and emotionally adaptive support plan based on the travel information, emotional information, and the collected information. This makes it possible to provide a personalized support plan that corresponds to the user's emotional state.

[0531] "User" refers to an individual who uses this system and receives mobility assistance.

[0532] "Travel information" refers to detailed travel data provided by users, such as departure point, destination, and travel time.

[0533] "Emotional information" refers to data about the user's current emotional state, including stress levels and relaxation levels.

[0534] "Station facilities information" refers to information indicating the location and availability of elevators, escalators, and ticket gates within the station.

[0535] "Information on vehicles in operation" refers to information regarding the location and timetable of public transportation such as trains and buses that are currently in service.

[0536] "Congestion status" refers to information regarding the degree of congestion at stations and on trains, as well as information regarding expected congestion levels.

[0537] An "optimal and emotionally adaptive support plan" refers to a customized mobility guidance and support plan tailored to the user's needs and emotional state, based on collected information and emotional data.

[0538] "Support staff" refers to personnel who provide assistance and guidance to users on-site.

[0539] "Adaptive guidance" refers to a method of providing personalized travel information tailored to the user's current situation and emotions.

[0540] To implement this invention, the system consists of a server, a user's terminal, and the user. First, the user uses a smartphone or smart glasses to input travel information and emotional information through a dedicated application. Here, travel information includes the departure point, destination, and desired travel time, while emotional information includes the user's stress level and degree of relaxation.

[0541] The server utilizes railway company APIs and other transportation information APIs to collect real-time information on station and line facilities, operating status, and congestion levels. This information is combined with user sentiment information using a generative AI model to create optimal and emotionally adaptive support plans. For example, it suggests routes that avoid congestion and places where users can relax, ensuring a comfortable journey.

[0542] The generated support plan is notified to the user's device, and the user travels along the designated route. This ensures a stress-free journey for the user. After completing the journey, the user can send feedback again from their device, which helps to improve the support plan for future journeys.

[0543] As a concrete example, when tourists visiting Japan for the first time efficiently tour tourist attractions in a new city, the system can provide transportation support that reduces anxiety about the Japanese language while allowing them to experience the culture. In such cases, the system generates guidance that takes the tourist's feelings into consideration, enabling smooth movement between tourist spots.

[0544] An example of a prompt message for a generative AI model is as follows:

[0545] "The user is a tourist visiting Japan, wants to travel from Shibuya Station to Asakusa Station, and is feeling anxious about their first visit to Japan. Please generate travel suggestions that take their cultural background into consideration and provide them with a sense of security."

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The user enters their departure point, destination, desired travel time, and emotional state using a smartphone or smart glasses app. This input data is sent from the device to the server. The server receives the user's travel information and emotional state as input and registers it in its database.

[0549] Step 2:

[0550] The server collects real-time facility information, operating status, and congestion data for stations and trains in operation through railway company APIs and traffic information APIs. The data obtained from these APIs is organized within the system and converted into a readily usable format. The collected information is recorded along with user travel information.

[0551] Step 3:

[0552] The server uses a generative AI model to analyze user movement information, emotional information, and collected real-time data. This analysis process generates an appropriate support plan. In the initial stages of the analysis, it recognizes the user's emotional state and creates relaxing guidance, especially for users experiencing tension or anxiety. The output is a personalized, optimal, and emotionally adaptive support plan.

[0553] Step 4:

[0554] The server notifies the user's device of the generated assistance plan. The notified plan includes, for example, the optimal route to avoid congestion and relaxation points that take emotions into consideration. The device displays this information in an easy-to-understand manner for the user, making it usable as a guide while traveling.

[0555] Step 5:

[0556] After the journey, the user enters feedback from their device and sends it to the server. This feedback is stored in a database and used when creating future support plans. The feedback process involves data analysis based on changes in the user's emotional state and their experience during the journey, in order to improve the accuracy of the plan.

[0557] 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.

[0558] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0559] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0564] 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.

[0565] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0566] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0567] 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.

[0568] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0569] 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.

[0570] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] The 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.

[0572] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0573] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0574] In implementing this invention, a series of systems are configured to improve user convenience and maximize the efficiency of railway operators. This system consists of three main components: users, servers, and terminals.

[0575] First, users register their travel information using a dedicated application or web portal. This information includes the date and time of travel, departure point, destination, and the type of assistance required. This allows the server to understand the user's specific needs.

[0576] Next, the server collects real-time information on station and train facilities, congestion levels, and operational status. This information is obtained through APIs and data feeds provided by the railway company. The server then uses this data to generate an optimized support plan for users. This plan includes which train cars and doors should be used, when support is needed, and also provides instructions for the efficient deployment of support personnel.

[0577] After a support plan is generated, the server notifies the user via a terminal. This terminal can be a mobile device or a personal computer, allowing the user to review the plan and use it to plan their journey. This notification includes specific boarding locations, available accessible routes, and predicted congestion information.

[0578] During actual travel, the terminal continuously provides users with real-time information. This includes the current location of the train, delay information, and equipment usage status. Users will also be notified of scheduled support personnel as needed, ensuring they can travel with peace of mind.

[0579] This system will enable the effective use of limited human resources and create an environment where wheelchair users can use the railway with peace of mind. Furthermore, it will allow users to travel more systematically and efficiently, providing significant benefits to railway operators in terms of operations and service provision.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] Users access a dedicated app or web portal and enter detailed travel information. This includes the travel date, departure station, arrival station, desired departure time, and required assistance. This information is sent to the server and registered in the database.

[0583] Step 2:

[0584] The server uses APIs provided by railway companies to collect real-time information such as accessibility facilities at each station, train operation status, and congestion levels. This ensures that the system is always updated with the latest information.

[0585] Step 3:

[0586] The server uses an AI algorithm to generate the optimal support plan based on travel information provided by the user and collected real-time data. This includes recommending a suitable vehicle, the appropriate boarding and alighting locations, and assigning appropriate support personnel.

[0587] Step 4:

[0588] The server notifies the user's device of the generated support plan. The notification includes specific vehicle information, the barrier-free route for the day, congestion forecasts, and information on the deployment of support staff.

[0589] Step 5:

[0590] On the day of travel, the terminal will continuously provide real-time updates. Users can receive the latest information through the terminal regarding train delays, equipment usage, and the deployment status of support personnel.

[0591] Step 6:

[0592] After the transfer is complete, users can send feedback on the assistance provided to the system to help improve future services. This feedback is stored on the server and used to improve future assistance plans.

[0593] (Example 1)

[0594] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0595] In modern society, as the use of public transportation continues to increase, there is a growing need to provide smooth and safe transportation for users who have difficulty traveling. However, current systems lack sufficient real-time information provision and appropriate allocation of human resources, making it difficult to support efficient travel.

[0596] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0597] In this invention, the server includes means for registering travel information provided by the user, means for collecting transportation facility information, vehicle information in operation, and congestion status in real time, means for generating an optimal support plan based on the collected information, and means for providing real-time information during travel via the user's terminal. This makes it possible to provide an optimal travel plan tailored to the user's needs and to provide reliable real-time information during travel.

[0598] "Travel information provided by users" refers to information provided by users regarding their travel, including the date and time of their trip, departure point, destination, and the type of assistance they require.

[0599] "Transportation facility information" refers to data regarding the operational status and availability of various facilities installed at stations and on trains.

[0600] "Information on vehicles in operation" refers to data regarding the current location and operating schedule of public transportation such as trains and buses.

[0601] "Congestion status" refers to information regarding the degree of congestion and occupancy rate within public transportation.

[0602] "Means for generating optimal support plans" refers to a process that automatically creates specific support plans to facilitate user mobility based on collected mobility and equipment information.

[0603] "Means of notification" refers to technology that sends the generated support plan to the user's device and informs the user of its contents.

[0604] "Means of providing real-time information" refers to a system that continuously updates and provides information on the current location and status of transportation so that users can always stay informed of the latest situation while on the move.

[0605] The system of this invention mainly consists of a server, a terminal, and a user. The following specific steps are taken to implement the invention.

[0606] First, users enter their travel information using a dedicated application or web portal. This includes the date and time of travel, departure point, destination, and required assistance. To ensure accurate registration of this information, the interface employs a user-friendly design.

[0607] Next, the server uses transportation APIs and external data feeds to collect real-time information on actual train operations and station facilities in order to process the user's travel information mentioned above. The server incorporates algorithms for data collection and plan generation.

[0608] The server generates an optimal mobility assistance plan based on collected information and the user's travel data. This plan includes specific instructions such as which vehicle and which door to use, the timing to avoid congestion during travel, and the deployment of necessary support personnel.

[0609] After a support plan is generated, the server sends it to the user's device. This device can be the user's mobile device or computer. This makes it easier for the user to review the plan they receive.

[0610] The device continues to provide real-time updated information while the user is on the move, including the current train location, delay information, and station equipment usage status. It also facilitates smooth communication with support personnel based on the generated support plan.

[0611] As a concrete example, to ensure smooth movement for wheelchair users at train stations, the system includes information on whether or not elevators will be used in the plan and suggests appropriate boarding positions for the user. In this way, the system realizes the optimal travel experience within limited time and resources.

[0612] An example of a prompt message would be, "Please suggest the optimal travel plan for a wheelchair user using the train."

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] Users log in to a dedicated application or web portal and register their travel details. This information includes the date and time, departure point, destination, and required assistance. The entered data is sent to a server and securely stored in a database. Here, travel information based on the user's needs is collected in digital format.

[0616] Step 2:

[0617] The server collects real-time information on station facilities, trains in operation, and congestion levels through transportation APIs and external data feeds. Using this information as input, the server integrates and cross-references the collected data to perform analysis that determines the most suitable travel routes and timings for users. Data processing includes predicting current congestion levels and delay risks. The analyzed data is then fed into an internal analysis model as output.

[0618] Step 3:

[0619] The server generates an optimal support plan for the user's travel based on the analyzed data. This generation uses a generative AI model that automatically formulates the plan best suited to each user's needs. The input is the analyzed data obtained in step 2, and the output is a specific support plan. This support plan includes details such as which vehicle door to use, the shortest travel route, and the most appropriate time allocation.

[0620] Step 4:

[0621] The server notifies the device of the generated assistance plan. The device is the user's mobile device or computer, and upon receiving the notification, the user can view the plan details. Specifically, a real-time navigation guide is displayed on the user's screen via a push notification to the device. This notification includes the assistance plan, and the user begins their journey based on it.

[0622] Step 5:

[0623] While on the move, the terminal continuously provides the user with the latest information. This includes real-time updates on train delays, current location, and station facility usage. Input here is real-time information continuously provided by the server. Data processing involves updating environment variables as the user moves and suggesting the optimal action based on predictions. The user receives immediate notifications through the terminal, enabling them to take timely and appropriate action.

[0624] (Application Example 1)

[0625] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] Users of public transport in urban areas face the problem of not being able to accurately grasp real-time traffic conditions and congestion levels, making it difficult to plan their travel efficiently. This can lead to unexpected delays and congestion that disrupt comfortable travel. Furthermore, transportation providers also face the challenge of optimizing resource allocation.

[0627] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0628] In this invention, the server includes means for registering travel information provided by users, means for collecting information on transportation facilities, information on the operation of moving objects, and congestion status in real time, and means for generating an optimal travel plan based on the travel information and the collected information. This enables users to move efficiently within cities and allows transportation providers to optimize their resources.

[0629] "Travel information" refers to data provided by the user, such as the origin, destination, date and time of travel, and the type of support needed.

[0630] "Transportation facility information" refers to information about stations, bus stops, and train and bus facilities, including access methods and the availability of barrier-free facilities.

[0631] "Mobile transportation operation information" refers to data showing the operating status of public transportation such as trains and buses, including information such as operating times and delay status.

[0632] "Congestion status" refers to data that indicates the density of people and vehicles in public transportation and road conditions.

[0633] A "travel plan" is a plan that provides the optimal travel procedure and route based on the user's input information and real-time traffic information.

[0634] "Support personnel" are individuals assigned to facilitate the smooth movement of users, and their efficient allocation is ensured.

[0635] In implementing this invention, the server first receives travel information provided by the user through a web application or mobile application. This information includes data such as the origin, destination, date and time of travel, and the type of assistance required. The server registers this travel information in a database and collects real-time information on transportation facilities, vehicle operation information, and congestion status through a public transportation API. This includes operating hours, delay information, and accessibility status of facilities.

[0636] The server uses an AI engine based on Python and TensorFlow to analyze collected data and generate an optimal travel plan for the user. This travel plan includes the best boarding location, available barrier-free routes, and predicted congestion information. The generated travel plan is then notified to the user using the most appropriate method. The notification is delivered to a smartphone or tablet. Based on this information, the user can travel effectively and without stress.

[0637] As a concrete example, when a user arrives at their planned departure station based on a travel plan registered on their smartphone, the following prompt message will appear in the user's app: "Please tell me the best route to my next destination, avoiding congestion." In this case, by following the suggested route, the user can avoid congestion and reach their destination smoothly.

[0638] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0639] Step 1:

[0640] Users enter travel information through a smartphone application. This information includes departure point, destination, travel date and time, and required assistance. The entered travel information is sent to a server and registered in a database.

[0641] Step 2:

[0642] The server collects information on transportation facilities, vehicle operation information, and congestion status in real time via the public transportation API, based on registered travel information. It receives data provided by the transportation API as input and obtains information including congestion levels and operating times.

[0643] Step 3:

[0644] The server uses Python and TensorFlow to generate an optimal travel plan using an AI engine based on acquired data and user movement information. During this process, the data is preprocessed, and the generated AI model is used to calculate the optimal route and timing. The output is a travel plan that includes the optimal boarding location and estimated travel time.

[0645] Step 4:

[0646] The generated travel plan is sent from the server to the user's smartphone. The user's device receives the push notification and displays the plan details on the screen. The user then uses this as a reference to carry out their actual travel.

[0647] Step 5:

[0648] While in transit, the user's device continuously receives real-time information updates from the server. Current location and unexpected delay information are provided periodically, allowing the user to adjust their plan as needed.

[0649] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0650] The system for implementing this invention provides more personalized assistance by integrating an emotion engine, in addition to generating and providing user mobility assistance plans. The system consists primarily of a user, a server, and a terminal.

[0651] First, the user enters travel details using a dedicated application or web portal. This includes the travel date, departure station, arrival station, desired travel time, and required assistance. The provided information is sent to the server and registered in the database.

[0652] The server uses the railway company's API to collect real-time information on station and train facilities, operating status, and congestion levels. Furthermore, the server uses an emotion engine to analyze users' emotions based on input data and past feedback. The results of this analysis are considered when creating support plans.

[0653] The generated support plan is adjusted according to the user's emotional state. For example, if the user appears tense, more detailed instructions will be provided. Conversely, if the user is relaxed, only the bare minimum of information will be provided to reduce their stress.

[0654] Next, the server notifies the terminal of the adjusted support plan. The notification includes specific vehicle information, potential congestion information for the day, and customized guidance for the user. This allows the user to respond appropriately at each step of their journey.

[0655] Finally, after the journey is complete, users provide feedback, and the system uses this information to further improve the plan. This feedback is used for analysis by an emotion engine and utilized in creating the next support plan. For example, if a user felt uneasy in an area last time, additional support staff will be assigned to that area next time. This improves user satisfaction during travel and enhances the quality of support provided by the railway company.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] Users access a dedicated app or web portal and enter their travel schedule. They enter the travel date, departure station, arrival station, departure time, and any necessary support. This data is sent to the server and automatically stored in the database.

[0659] Step 2:

[0660] The server obtains real-time information on station and train accessibility facilities, operating status, and congestion levels via the railway company's API. This information is aggregated as data necessary for generating subsequent support plans.

[0661] Step 3:

[0662] The server analyzes the user's emotions using an emotion engine based on the user's past movement data and input information. It uses facial recognition and voice analysis functions to infer the user's emotional state and records it in an emotion database.

[0663] Step 4:

[0664] By comparing the emotion analysis results with collected real-time information, the server generates the optimal support plan. This plan is adjusted according to the user's emotions; for example, if stress levels are high, additional guidance will be provided.

[0665] Step 5:

[0666] The server sends the generated assistance plan to the terminal and notifies the user. The notification includes recommended vehicles and locations, congestion forecasts, and personalized, emotionally responsive guidance, allowing the user to proceed with their journey.

[0667] Step 6:

[0668] While on the move, the device provides users with real-time updates. If there are any changes in the environment or situation, the latest information, including emotional responses, is immediately transmitted to the user based on instructions from the server.

[0669] Step 7:

[0670] After completing the journey, users provide feedback and comments within the app. This feedback is used by the emotion engine to improve future support plans, contributing to the overall performance of the system.

[0671] (Example 2)

[0672] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0673] Conventional mobility assistance systems failed to provide individualized support that took into account the user's emotional state, and thus could not adequately enhance user comfort and satisfaction. Furthermore, they lacked efficiency in real-time information acquisition and the deployment of support personnel, making it difficult to provide prompt and accurate support to users.

[0674] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0675] In this invention, the server includes means for recording travel details provided by the user, means for acquiring equipment information, transportation information during operation, and congestion status in real time, and means for analyzing the user's emotional state. This makes it possible to provide an adjustment support plan tailored to the individual needs of the user, thereby improving their sense of security and satisfaction during travel.

[0676] "User" refers to an individual or group that uses the system to receive assistance related to mobility.

[0677] "Travel details" refers to specific information provided by the user, such as the date and time of travel, departure point, arrival point, and the type of support required.

[0678] "Means of recording" refers to methods and devices for saving user movement details to a database or other recording media.

[0679] "Facility information" refers to available facilities, installation status, and accessibility information for stations and other transportation facilities.

[0680] "Information on transportation in operation" refers to the operating schedules, delay information, and route information for public transportation such as trains and buses.

[0681] "Congestion status" refers to information regarding the degree of crowding among people on a particular mode of transport, at a station, or within a facility.

[0682] "Means of acquiring data in real time" refers to technologies and processes that enable the immediate acquisition of the latest status and conditions, making them available for use in the system.

[0683] "Emotional state" refers to the results of evaluating and analyzing the user's psychological state and emotional responses.

[0684] "Means of analysis" refers to technical methods and devices used to evaluate the user's condition and needs based on collected data and information.

[0685] A "support adjustment plan" refers to a mobility support plan that is optimized and proposed based on the individual circumstances of the user, using collected and analyzed information.

[0686] "Means of communication" refers to the technical methods and devices used to transmit the generated support plan to the user.

[0687] This invention is a system that personalizes user mobility assistance, and is operated with clearly defined roles for the server, terminal, and user. The server collects and analyzes information provided by the user and generates an optimal mobility assistance plan. The following hardware and software are used for implementation.

[0688] The server utilizes a database management system, such as an RDBMS like PostgreSQL, to store travel details. It also employs communication protocols to obtain facility and operational information via APIs from railway companies and public transport organizations. For real-time data acquisition, it uses WebSockets as an asynchronous communication technology. Furthermore, to analyze emotional states, it leverages a generative AI model that performs natural language processing, utilizing the open-source library TensorFlow.

[0689] The device provides an interface for users to enter travel details and receive a plan. For example, this could be a dedicated application running on iOS or Android. This app would feature an intuitive UI to facilitate user interaction and would include form input and notification functions.

[0690] Users access the mobility assistance system via mobile devices or computers. For example, a parent with young children might input, "Please generate a plan for traveling from Tokyo Station to Shin-Osaka Station with a stroller next Sunday," and the system would notify them of the necessary assistance, enabling a smooth journey.

[0691] An example of a prompt message is, "Create a travel assistance plan for a wheelchair user from Tokyo to Shin-Osaka. Include information on priority seating and elevators." Based on this input, an assistance plan tailored to the user's needs will be generated and provided.

[0692] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0693] Step 1:

[0694] Users access the system's dedicated application or web portal using a terminal and enter detailed travel information. Input fields include the travel date, departure station, arrival station, desired time, and required assistance. For example, if a user enters "November 5, 2023, departing from Tokyo Station, bound for Shin-Osaka Station, desired time 10:00, wheelchair user," this information is formatted as data. After formatting, the input data is sent to the server.

[0695] Step 2:

[0696] The server receives the movement details sent by the user and registers them in the database. At this time, the database stores detailed information along with the user's movement history. This will enable pattern recognition and data analysis in the future. The output from the database confirms that the information is available for use by other modules.

[0697] Step 3:

[0698] The server uses APIs from external transportation companies to collect facility information, operating status, and congestion information in real time. This information collection includes, for example, "station elevator operational status" and "train delay information." Since the collected data is disorganized in its raw form, it is converted into a format that is easy to analyze and stored in preparation for the next processing step.

[0699] Step 4:

[0700] The server uses an emotion engine to analyze the user's emotional state. It analyzes user feedback and past usage data based on natural language processing techniques to infer how the user is currently feeling and what kind of support they need. Input is text and user responses, and output is the analyzed emotion data.

[0701] Step 5:

[0702] The server uses a generative AI model to create a support plan based on the acquired real-time data and sentiment analysis results. The generated plan includes specific vehicles to ride, available facilities, and routes to avoid congestion. The generated data is formatted in a way that is realistically understandable to the user.

[0703] Step 6:

[0704] The server notifies the terminal of the created support plan. The terminal displays the notification in a way that is easy for the user to understand, and this includes providing it as audio guidance or visual information. For example, instructions such as "Today, you will be in car number 1 with your assigned seat, and you will use the elevator at the front" are visualized.

[0705] Step 7:

[0706] After the journey is complete, the user provides feedback about the journey using their device. The feedback is sent to the server and analyzed again by the emotion engine. This provides data that can be used to improve future support plans, and the analysis results are stored in a database.

[0707] (Application Example 2)

[0708] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] In modern society, there is a demand for services that reduce stress and anxiety and provide a comfortable experience when citizens move around within cities. Therefore, personalized mobility support that takes into account the emotional state of users is necessary, and improving the quality of support for people with specific needs, such as visitors and the elderly, is a particular challenge.

[0710] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0711] In this invention, the server includes means for registering travel information and emotional information provided by the user, means for collecting station equipment information, operating vehicle information, and congestion status in real time, and means for generating an optimal and emotionally adaptive support plan based on the travel information, emotional information, and the collected information. This makes it possible to provide a personalized support plan that corresponds to the user's emotional state.

[0712] "User" refers to an individual who uses this system and receives mobility assistance.

[0713] "Travel information" refers to detailed travel data provided by users, such as departure point, destination, and travel time.

[0714] "Emotional information" refers to data about the user's current emotional state, including stress levels and relaxation levels.

[0715] "Station facilities information" refers to information indicating the location and availability of elevators, escalators, and ticket gates within the station.

[0716] "Information on vehicles in operation" refers to information regarding the location and timetable of public transportation such as trains and buses that are currently in service.

[0717] "Congestion status" refers to information regarding the degree of congestion at stations and on trains, as well as information regarding expected congestion levels.

[0718] An "optimal and emotionally adaptive support plan" refers to a customized mobility guidance and support plan tailored to the user's needs and emotional state, based on collected information and emotional data.

[0719] "Support staff" refers to personnel who provide assistance and guidance to users on-site.

[0720] "Adaptive guidance" refers to a method of providing personalized travel information tailored to the user's current situation and emotions.

[0721] To implement this invention, the system consists of a server, a user's terminal, and the user. First, the user uses a smartphone or smart glasses to input travel information and emotional information through a dedicated application. Here, travel information includes the departure point, destination, and desired travel time, while emotional information includes the user's stress level and degree of relaxation.

[0722] The server utilizes railway company APIs and other transportation information APIs to collect real-time information on station and line facilities, operating status, and congestion levels. This information is combined with user sentiment information using a generative AI model to create optimal and emotionally adaptive support plans. For example, it suggests routes that avoid congestion and places where users can relax, ensuring a comfortable journey.

[0723] The generated support plan is notified to the user's device, and the user travels along the designated route. This ensures a stress-free journey for the user. After completing the journey, the user can send feedback again from their device, which helps to improve the support plan for future journeys.

[0724] As a concrete example, when tourists visiting Japan for the first time efficiently tour tourist attractions in a new city, the system can provide transportation support that reduces anxiety about the Japanese language while allowing them to experience the culture. In such cases, the system generates guidance that takes the tourist's feelings into consideration, enabling smooth movement between tourist spots.

[0725] An example of a prompt message for a generative AI model is as follows:

[0726] "The user is a tourist visiting Japan, wants to travel from Shibuya Station to Asakusa Station, and is feeling anxious about their first visit to Japan. Please generate travel suggestions that take their cultural background into consideration and provide them with a sense of security."

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The user enters their departure point, destination, desired travel time, and emotional state using a smartphone or smart glasses app. This input data is sent from the device to the server. The server receives the user's travel information and emotional state as input and registers it in its database.

[0730] Step 2:

[0731] The server collects real-time facility information, operating status, and congestion data for stations and trains in operation through railway company APIs and traffic information APIs. The data obtained from these APIs is organized within the system and converted into a readily usable format. The collected information is recorded along with user travel information.

[0732] Step 3:

[0733] The server uses a generative AI model to analyze user movement information, emotional information, and collected real-time data. This analysis process generates an appropriate support plan. In the initial stages of the analysis, it recognizes the user's emotional state and creates relaxing guidance, especially for users experiencing tension or anxiety. The output is a personalized, optimal, and emotionally adaptive support plan.

[0734] Step 4:

[0735] The server notifies the user's device of the generated assistance plan. The notified plan includes, for example, the optimal route to avoid congestion and relaxation points that take emotions into consideration. The device displays this information in an easy-to-understand manner for the user, making it usable as a guide while traveling.

[0736] Step 5:

[0737] After the journey, the user enters feedback from their device and sends it to the server. This feedback is stored in a database and used when creating future support plans. The feedback process involves data analysis based on changes in the user's emotional state and their experience during the journey, in order to improve the accuracy of the plan.

[0738] 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.

[0739] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0740] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0741] 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.

[0742] Figure 9 shows an 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.

[0743] 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.

[0744] 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.

[0745] 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, motorcycles, etc., 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, for example, based 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.

[0746] 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."

[0747] 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.

[0748] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0749] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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 the like 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.

[0758] 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.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] A means of registering travel information provided by users,

[0762] A means of collecting station facility information, train information in operation, and congestion status in real time,

[0763] A means for generating an optimal support plan based on the aforementioned movement information and the aforementioned collected information,

[0764] A means of notifying the user of the aforementioned support plan,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, comprising means for effectively deploying support personnel based on the generated support plan.

[0768] (Claim 3)

[0769] The system according to claim 1, comprising means for providing real-time guidance according to the user's movement status.

[0770] "Example 1"

[0771] (Claim 1)

[0772] A means of registering travel information provided by users,

[0773] A means of collecting real-time information on transportation facilities, vehicles in operation, and congestion levels,

[0774] A means for generating an optimal support plan based on the aforementioned movement information and the aforementioned collected information,

[0775] A means of notifying the user of the aforementioned support plan,

[0776] A means of providing real-time information while the user is on the move via their device,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, comprising means for effectively allocating human resources based on the generated support plan.

[0780] (Claim 3)

[0781] The system according to claim 1, comprising means for providing real-time guidance according to the user's movement status.

[0782] "Application Example 1"

[0783] (Claim 1)

[0784] A means of registering travel information provided by users,

[0785] A means of collecting real-time information on transportation facilities, the operation status of mobile vehicles, and congestion levels,

[0786] Means for generating an optimal movement plan based on the aforementioned movement information and the aforementioned collected information,

[0787] Means for notifying the user of the aforementioned travel plan,

[0788] A means of presenting the optimal travel route based on current traffic conditions when users travel,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising means for effectively deploying support personnel based on the generated movement plan.

[0792] (Claim 3)

[0793] The system according to claim 1, comprising means for providing real-time guidance according to the user's movement status.

[0794] "Example 2 of combining an emotion engine"

[0795] (Claim 1)

[0796] A means of recording travel details provided by the user,

[0797] A means of obtaining real-time information on facilities, transportation methods in operation, and congestion status,

[0798] A means of analyzing the emotional state of users,

[0799] Means for creating a support plan adjusted based on the aforementioned movement details, the acquired information, and the emotion analysis results,

[0800] A means for communicating the aforementioned support plan to the user,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, comprising means for efficiently allocating support personnel based on the aforementioned adjusted support plan.

[0804] (Claim 3)

[0805] The system according to claim 1, comprising means for providing real-time information in response to the user's movement behavior.

[0806] "Application example 2 when combining with an emotional engine"

[0807] (Claim 1)

[0808] A means for registering movement information and emotional information provided by users,

[0809] A means of collecting station facility information, train information in operation, and congestion status in real time,

[0810] A means for generating an optimal and emotionally adaptive support plan based on the aforementioned movement information, emotional information, and collected information,

[0811] A means of notifying the user of the aforementioned support plan,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for effectively and adaptively deploying support personnel based on the generated support plan.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for providing real-time adaptive guidance in accordance with the user's movement status and emotional state. [Explanation of symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of registering travel information provided by users, A means of collecting station facility information, train information in operation, and congestion status in real time, A means for generating an optimal support plan based on the aforementioned movement information and the aforementioned collected information, A means of notifying the user of the aforementioned support plan, A system that includes this.

2. The system according to claim 1, comprising means for effectively deploying support personnel based on the generated support plan.

3. The system according to claim 1, comprising means for providing real-time guidance according to the user's movement status.

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