System

Abstract

An object of the system according to the embodiment is to propose an appropriate timetable re-arrangement quickly when a traffic disturbance occurs.SOLUTION: A system according to an embodiment includes a collecting unit, a recombining unit, and a providing unit. The collection unit collects information on a traffic disturbance. Based on the information collected by the collection unit, the recombination unit proposes an appropriate diamond recombination. The providing unit provides a result of the die reassembly proposed by the reassembling unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to quickly and appropriately reschedule train schedules when traffic disruptions occur, which can lead to confusion.

[0005] The system according to the embodiment aims to quickly propose an appropriate timetable change when a traffic disturbance occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a reorganization unit, and a provision unit. The collection unit collects information about traffic disruptions. The reorganization unit proposes an appropriate schedule reorganization based on the information collected by the collection unit. The provision unit provides the results of the schedule reorganization proposed by the reorganization unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly propose an appropriate timetable change when a traffic disturbance occurs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A train schedule rescheduling system according to an embodiment of the present invention uses AI to appropriately reschedule train schedules when traffic disruptions occur. The system collects traffic disruption information, proposes optimal train schedule rescheduling, and provides the results to mitigate disruption. For example, when a train accident or delay occurs, the AI ​​collects that information. For example, detailed information such as the location of the accident, the extent of the impact, and the delay time is collected. The system then proposes an optimal train schedule rescheduling based on the collected information. For example, the system proposes rerouting other trains to avoid the delayed section. Furthermore, the system monitors the effectiveness of the proposed schedule rescheduling in real time and reschedules the train schedule as necessary. For example, if the proposed schedule rescheduling is not working properly, the AI ​​makes a new proposal and reschedules the train schedule. This allows the system to prevent congestion at stations and reduce disruption caused by sudden schedule rescheduling. This allows the system to quickly and accurately reschedule train schedules when traffic disruptions occur. For example, when a train accident occurs, the AI ​​can quickly propose an optimal schedule rescheduling, minimizing passenger disruption. In addition, AI monitors the effects of timetable changes in real time, ensuring optimal operation conditions are maintained at all times.

[0029] A timetable rescheduling system according to an embodiment includes a collection unit, a rescheduling unit, and a provision unit. The collection unit collects information on traffic disruptions. Traffic disruptions include, but are not limited to, accidents, natural disasters, and construction work. The collection unit collects detailed information, such as the location of the accident, the extent of its impact, and delay times. The collection unit can also collect information from social media and news sites to comprehensively grasp the situation. For example, it collects posts from social media to understand eyewitness accounts of the accident and the situation at the scene. The rescheduling unit proposes an appropriate timetable rescheduling based on the information collected by the collection unit. Appropriate timetable rescheduling includes, but is not limited to, reducing delays and improving passenger convenience. The rescheduling unit, for example, analyzes the collected information and calculates an optimal timetable rescheduling. The rescheduling unit can also monitor the effectiveness of the proposed timetable rescheduling in real time and reschedule the timetable as necessary. For example, if the proposed timetable rescheduling is not working well, the rescheduling unit makes a new proposal and reschedules the timetable. The providing unit provides the results of the timetable rearrangement proposed by the rearrangement unit. The providing unit provides operation information based on the results of the proposed timetable rearrangement, for example. The providing unit also makes the operation information multilingual so that it can also accommodate foreign passengers. For example, the operation information provided can be multilingual, such as English, Chinese, and Korean. This allows the timetable rearrangement system according to the embodiment to perform appropriate timetable rearrangements in the event of a traffic jam, thereby reducing confusion.

[0030] The collection unit can collect detailed information including the location of the accident, the extent of its impact, and delay time. Detailed information includes, but is not limited to, the location of the accident, the extent of its impact, and delay time. For example, the collection unit identifies the location of the accident and grasps its extent of impact. The collection unit can also accurately measure and collect delay time. For example, the collection unit can identify the location of the accident using GPS data and display the extent of its impact on a map. The collection unit can also measure delay time in real time and record it in a database. By collecting detailed information, more accurate train schedule rescheduling becomes possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the location of the accident and the extent of its impact into the generation AI and cause the generation AI to collect detailed information.

[0031] The reconfiguration unit can analyze the collected information and calculate an appropriate timetable rearrangement. Examples of appropriate timetable rearrangement include, but are not limited to, shortening delay times and improving passenger convenience. The reconfiguration unit, for example, analyzes the collected information and calculates an optimal timetable rearrangement. For example, the reconfiguration unit suggests changing the operating routes of other trains to avoid sections where delays are occurring. The reconfiguration unit can also analyze passenger movement patterns and provide optimal transfer guidance. For example, the reconfiguration unit analyzes passengers' past movement patterns and provides optimal transfer guidance. This enables optimal timetable rearrangement by analyzing the collected information. Some or all of the above-described processing in the reconfiguration unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconfiguration unit can input the collected information to a generation AI and cause the generation AI to calculate an appropriate timetable rearrangement.

[0032] The providing unit can provide operation information based on the results of the proposed timetable rearrangement. The operation information includes, for example, the arrival time of the next train and an alternative route, but is not limited to these examples. For example, the providing unit provides the arrival time of the next train based on the results of the proposed timetable rearrangement. The providing unit can also propose an alternative route and provide appropriate information to passengers. For example, the providing unit provides optimal transfer guidance to passengers based on the results of the proposed timetable rearrangement. In this way, appropriate information can be provided to passengers by providing operation information based on the results of the proposed timetable rearrangement. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the results of the proposed timetable rearrangement to a generating AI and cause the generating AI to provide operation information.

[0033] The reorganization unit can monitor the effect of the proposed timetable rearrangement in real time and rearrange the timetable again. Specific methods for real-time monitoring include, but are not limited to, the type of monitoring system and the monitoring frequency. The reorganization unit, for example, monitors the effect of the proposed timetable rearrangement in real time and rearranges the timetable again as necessary. For example, if the proposed timetable rearrangement is not working well, the reorganization unit makes a new proposal and rearranges the timetable again. This makes it possible to always rearrange the timetable optimally by monitoring in real time. Some or all of the above-mentioned processing in the reorganization unit may be performed using AI, for example, or may be performed without using AI. For example, the reorganization unit can input the effect of the proposed timetable rearrangement into the generation AI and have the generation AI perform real-time monitoring.

[0034] The collection unit can collect video or data from the scene using a drone or a sensor. Examples of drones and sensors include, but are not limited to, camera-equipped drones and temperature sensors. The collection unit, for example, flies a drone to collect aerial video of the accident scene and grasps the situation in real time. The collection unit can also install sensors to collect environmental data (temperature, humidity, gas concentration, etc.) from the accident scene. Furthermore, the collection unit can link the drone and the sensor to create a detailed 3D map of the accident scene and grasp the extent of the impact. This allows detailed information from the scene to be collected using the drone or sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the drone or sensor into the generation AI and have the generation AI collect video and data from the scene.

[0035] The collection unit can compare and analyze past delay data with current traffic conditions to predict delay times. Past delay data includes, for example, delay records from the past year and delay causes, but is not limited to, examples. For example, the collection unit can predict delay times by comparing the past delay data with current traffic conditions. The collection unit can also combine the past delay data with current weather information to more accurately predict delay times. Furthermore, the collection unit can predict delay times by comparing the past delay data with the current number of passengers. This improves the accuracy of delay time predictions by comparing the past data with current conditions. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input past delay data and current traffic condition data into a generation AI and cause the generation AI to predict delay times.

[0036] The collection unit can update the collected information in real time and build a database to always keep track of the latest situation. Specific methods for real-time updating include, but are not limited to, the update frequency and the technology used. For example, the collection unit can update the collected information in the database in real time to always keep track of the latest situation. The collection unit can also build the database on the cloud and make it accessible from multiple devices. Furthermore, the collection unit can increase the update frequency of the database to keep the information fresh. This allows the information to be updated in real time, so that the latest situation can always be kept track of. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the collected information to a generation AI and have the generation AI update the database in real time.

[0037] When collecting information on traffic disruptions, the collection unit can also collect information from social media and news sites to comprehensively grasp the situation. Examples of social media and news sites include, but are not limited to, LINE (registered trademark), Twitter (registered trademark), Facebook (registered trademark), and major news sites. The collection unit, for example, collects posts from social media to grasp eyewitness information about the accident and the situation at the scene. The collection unit can also collect information from news sites and confirm official announcements and news reports. Furthermore, the collection unit can integrate information from social media and news sites to comprehensively grasp the situation. This makes it possible to comprehensively grasp the situation by collecting information from social media and news sites. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information from social media and news sites into the generation AI and cause the generation AI to perform comprehensive situation assessment.

[0038] The collection unit can share the collected information with other transportation facilities to promote collaboration. Examples of other transportation facilities include, but are not limited to, buses, taxis, and shared ride services. For example, the collection unit can share the collected information with a bus company to propose an alternative route. The collection unit can also share the collected information with a taxi company to ensure passenger transportation. Furthermore, the collection unit can share the collected information with other railway companies to coordinate schedules. This allows collaboration by sharing information with other transportation facilities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected information into a generation AI and cause the generation AI to share information with other transportation facilities.

[0039] The collection unit can propose appropriate evacuation routes and alternative means to passengers based on the collected information. Appropriate evacuation routes and alternative means include, but are not limited to, the nearest evacuation shelter and alternative means of transportation. For example, the collection unit can propose an optimal evacuation route to passengers based on the collected information. The collection unit can also propose alternative means of transportation (such as a bus or taxi) to passengers based on the collected information. Furthermore, the collection unit can propose the nearest evacuation shelter to passengers based on the collected information. This ensures the safety of passengers by proposing appropriate evacuation routes and alternative means based on the collected information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected information to a generation AI and cause the generation AI to propose appropriate evacuation routes and alternative means.

[0040] The rescheduling unit can analyze the collected information and refer to past success stories when calculating the optimal timetable rearrangement. Past success stories include, but are not limited to, past successful timetable rearrangement cases and factors behind success. The rescheduling unit, for example, calculates the optimal timetable rearrangement based on past successful timetable rearrangement cases during delays. The rescheduling unit can also calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during accidents. Furthermore, the rescheduling unit can calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during weather-related delays. This enables more effective timetable rearrangement by referring to past successful cases. Some or all of the above-described processing in the rescheduling unit may be performed using, for example, AI, or may be performed without using AI. For example, the rescheduling unit can input data from past successful cases into the generation AI and have the generation AI calculate the optimal timetable rearrangement.

[0041] When proposing a timetable rearrangement, the rearrangement unit can consider cooperation with other transportation modes to improve the overall transportation network. Cooperation with other transportation modes includes, but is not limited to, information sharing protocols and cooperation procedures. For example, the rearrangement unit can consider cooperation with other railway companies to propose a comprehensive timetable rearrangement. The rearrangement unit can also consider cooperation with bus companies to propose alternative routes. Furthermore, the rearrangement unit can consider cooperation with taxi companies to ensure passenger transportation. This enables the optimization of the overall transportation network by considering cooperation with other transportation modes. Some or all of the above-described processing in the rearrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the rearrangement unit can input cooperation data with other transportation modes into the generation AI and cause the generation AI to improve the overall transportation network.

[0042] When proposing a schedule rearrangement, the rearrangement unit can analyze passenger movement patterns and provide optimal transfer guidance. Passenger movement patterns include, but are not limited to, past movement history and movement tendencies. For example, the rearrangement unit can analyze passengers' past movement patterns and provide optimal transfer guidance. The rearrangement unit can also analyze passengers' current movement patterns in real time and provide optimal transfer guidance. Furthermore, the rearrangement unit can combine passenger movement patterns with traffic conditions to provide optimal transfer guidance. In this way, optimal transfer guidance can be provided by analyzing passenger movement patterns. Some or all of the above-described processing in the rearrangement unit may be performed, for example, using AI or without AI. For example, the rearrangement unit can input passenger movement pattern data into a generation AI and cause the generation AI to provide optimal transfer guidance.

[0043] The reconfiguration unit can also take external factors, including weather or event information, into consideration when proposing a schedule rearrangement. Weather or event information includes, but is not limited to, weather data, an event calendar, and the like. For example, the reconfiguration unit can take weather information into consideration and propose an optimal schedule rearrangement for when bad weather occurs. The reconfiguration unit can also take event information into consideration and propose an optimal schedule rearrangement for when an event is held. Furthermore, the reconfiguration unit can combine weather information and event information to propose an optimal schedule rearrangement. This enables more appropriate schedule rearrangement by taking external factors, such as weather and event information, into consideration. Some or all of the above-described processing in the reconfiguration unit can be performed, for example, using AI, or can be performed without using AI. For example, the reconfiguration unit can input weather and event information data into the generation AI and have the generation AI execute a schedule rearrangement proposal.

[0044] When proposing a schedule rearrangement, the rearrangement unit can perform customization according to passenger attributes. Passenger attributes include, but are not limited to, age, gender, and occupation, for example. The rearrangement unit can propose an optimal schedule rearrangement, for example, according to the passenger's age. The rearrangement unit can also propose an optimal schedule rearrangement according to the passenger's gender. Furthermore, the rearrangement unit can propose an optimal schedule rearrangement according to the passenger's occupation. This enables more appropriate schedule rearrangement by customizing according to passenger attributes. Some or all of the above-described processing in the rearrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the rearrangement unit can input passenger attribute data into the generation AI and cause the generation AI to customize the schedule rearrangement.

[0045] The reordering unit can reflect passenger feedback when proposing a schedule rearrangement and improve the proposal. Passenger feedback includes, but is not limited to, for example, questionnaires and app rating functions. The reordering unit can, for example, collect passenger feedback and improve the schedule rearrangement proposal. The reordering unit can also reflect passenger feedback in real time and adjust the proposal. Furthermore, the reordering unit can analyze passenger feedback and use it in the next schedule rearrangement proposal. In this way, by reflecting passenger feedback, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the reordering unit may be performed, for example, using AI or without AI. For example, the reordering unit can input passenger feedback data into the generation AI and cause the generation AI to improve the proposal.

[0046] The providing unit can provide optimal transfer guidance to passengers based on the results of the proposed timetable rearrangement. The optimal transfer guidance includes, but is not limited to, transfer times and transfer routes. For example, the providing unit can provide optimal transfer guidance to passengers based on the results of the proposed timetable rearrangement. The providing unit can also suggest an alternative route to passengers based on the results of the proposed timetable rearrangement. Furthermore, the providing unit can guide passengers to the nearest evacuation site based on the results of the proposed timetable rearrangement. This improves passenger convenience by providing optimal transfer guidance based on the results of the proposed timetable rearrangement. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the results of the proposed timetable rearrangement to a generating AI and cause the generating AI to provide optimal transfer guidance.

[0047] The providing unit can provide the operation information in multiple languages ​​to accommodate foreign passengers. Examples of multilingual support include, but are not limited to, English, Chinese, and Korean. The providing unit can provide the operation information in multiple languages, such as English, Chinese, and Korean. The providing unit can also display the operation information in multiple languages ​​at stations with many foreign passengers. Furthermore, the providing unit can announce the operation information in multiple languages ​​during times when there are many foreign passengers. This multilingual support enables appropriate information to be provided to foreign passengers. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the multilingual version of the operation information into a generating AI and have the generating AI provide the information.

[0048] The providing unit can develop an interface for visually displaying the operation information to be provided. Specific methods for visually displaying the information include, but are not limited to, interface design and color usage. For example, the providing unit can use icons and color coding to visually display the operation information to be provided. The providing unit can also use maps and graphs to visually display the operation information to be provided. Furthermore, the providing unit can use animation to visually display the operation information to be provided. This makes it easier for passengers to understand the information by displaying it visually. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the development of a visually easy-to-understand interface to a generating AI and have the generating AI execute the interface design.

[0049] The providing unit can provide the operation information to be provided through various media, such as a smartphone app or digital signage. Examples of various media include, but are not limited to, a smartphone app or digital signage. For example, the providing unit provides the operation information to be provided through a smartphone app. The providing unit can also provide the operation information to be provided through digital signage. Furthermore, the providing unit can provide the operation information to be provided on station information boards. In this way, providing the information through various media makes it easier for passengers to obtain the information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the operation information to be provided to a generating AI and cause the generating AI to provide the information through various media, such as a smartphone app or digital signage.

[0050] The providing unit can reflect the passenger's current location information in the operation information it provides and propose an optimal travel route. Examples of the passenger's current location information include, but are not limited to, GPS data and location information services. For example, the providing unit can reflect the passenger's current location information in the operation information it provides and propose an optimal travel route. The providing unit can also reflect the passenger's current location information in the operation information it provides and propose the nearest evacuation site. Furthermore, the providing unit can reflect the passenger's current location information in the operation information it provides and propose an alternative means of transportation. By reflecting the passenger's current location information, an optimal travel route can be proposed. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the passenger's current location information into the generating AI and cause the generating AI to propose an optimal travel route.

[0051] The providing unit can reflect passenger feedback in the operation information to be provided, thereby improving the accuracy of the information provided. Passenger feedback includes, but is not limited to, for example, questionnaires and app rating functions. The providing unit can, for example, reflect passenger feedback in the operation information to be provided, thereby improving the accuracy of the information provided. The providing unit can also reflect passenger feedback in real time in the operation information to be provided, thereby improving the accuracy of the information provided. Furthermore, the providing unit can analyze passenger feedback in the operation information to be provided and use it in the next information provision. In this way, by reflecting passenger feedback, the accuracy of the information provided is improved. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input passenger feedback data into the generating AI and cause the generating AI to improve the accuracy of the information provided.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can collect video or data from the scene using a drone or a sensor. Examples of drones and sensors include, but are not limited to, camera-equipped drones and temperature sensors. The collection unit, for example, flies a drone to collect aerial video of the accident scene and grasps the situation in real time. The collection unit can also install sensors to collect environmental data (temperature, humidity, gas concentration, etc.) from the accident scene. Furthermore, the collection unit can link the drone and the sensor to create a detailed 3D map of the accident scene and grasp the extent of the impact. This allows detailed information from the scene to be collected using the drone or sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the drone or sensor into the generation AI and have the generation AI collect video and data from the scene.

[0054] The collection unit can compare and analyze past delay data with current traffic conditions to predict delay times. Past delay data includes, for example, delay records from the past year and delay causes, but is not limited to, examples. For example, the collection unit can predict delay times by comparing the past delay data with current traffic conditions. The collection unit can also combine the past delay data with current weather information to more accurately predict delay times. Furthermore, the collection unit can predict delay times by comparing the past delay data with the current number of passengers. This improves the accuracy of delay time predictions by comparing the past data with current conditions. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input past delay data and current traffic condition data into a generation AI and cause the generation AI to predict delay times.

[0055] When proposing a timetable rearrangement, the rearrangement unit can consider cooperation with other transportation modes to improve the overall transportation network. Cooperation with other transportation modes includes, but is not limited to, information sharing protocols and cooperation procedures. For example, the rearrangement unit can consider cooperation with other railway companies to propose a comprehensive timetable rearrangement. The rearrangement unit can also consider cooperation with bus companies to propose alternative routes. Furthermore, the rearrangement unit can consider cooperation with taxi companies to ensure passenger transportation. This enables the optimization of the overall transportation network by considering cooperation with other transportation modes. Some or all of the above-described processing in the rearrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the rearrangement unit can input cooperation data with other transportation modes into the generation AI and cause the generation AI to improve the overall transportation network.

[0056] The providing unit can provide the operation information in multiple languages ​​to accommodate foreign passengers. Examples of multilingual support include, but are not limited to, English, Chinese, and Korean. The providing unit can provide the operation information in multiple languages, such as English, Chinese, and Korean. The providing unit can also display the operation information in multiple languages ​​at stations with many foreign passengers. Furthermore, the providing unit can announce the operation information in multiple languages ​​during times when there are many foreign passengers. This multilingual support enables appropriate information to be provided to foreign passengers. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the multilingual version of the operation information into a generating AI and have the generating AI provide the information.

[0057] The rescheduling unit can analyze the collected information and refer to past success stories when calculating the optimal timetable rearrangement. Past success stories include, but are not limited to, past successful timetable rearrangement cases and factors behind success. The rescheduling unit, for example, calculates the optimal timetable rearrangement based on past successful timetable rearrangement cases during delays. The rescheduling unit can also calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during accidents. Furthermore, the rescheduling unit can calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during weather-related delays. This enables more effective timetable rearrangement by referring to past successful cases. Some or all of the above-described processing in the rescheduling unit may be performed using, for example, AI, or may be performed without using AI. For example, the rescheduling unit can input data from past successful cases into the generation AI and have the generation AI calculate the optimal timetable rearrangement.

[0058] The providing unit can develop an interface for visually displaying the operation information to be provided. Specific methods for visually displaying the information include, but are not limited to, interface design and color usage. For example, the providing unit can use icons and color coding to visually display the operation information to be provided. The providing unit can also use maps and graphs to visually display the operation information to be provided. Furthermore, the providing unit can use animation to visually display the operation information to be provided. This makes it easier for passengers to understand the information by displaying it visually. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the development of a visually easy-to-understand interface to a generating AI and have the generating AI execute the interface design.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection department collects information on traffic disruptions. Traffic disruptions include accidents, natural disasters, and construction work. The collection department collects detailed information such as the location of the accident, the extent of the impact, and the delay time. It also collects information from social media and news sites to gain a comprehensive understanding of the situation. For example, it collects posts from social media to understand eyewitness accounts of the accident and the situation at the scene. Step 2: The rescheduling unit proposes an appropriate timetable rearrangement based on the information collected by the collection unit. An appropriate timetable rearrangement includes reducing delays and improving passenger convenience. The rescheduling unit analyzes the collected information and calculates the optimal timetable rearrangement. It also monitors the effectiveness of the proposed timetable rearrangement in real time and rearranges the timetable again if necessary. For example, if the proposed timetable rearrangement is not working well, it will make a new proposal and rearrange the timetable again. Step 3: The provision unit provides the results of the timetable rearrangement proposed by the rearrangement unit. The provision unit provides operation information based on the results of the proposed timetable rearrangement. The operation information is also made multilingual to accommodate foreign passengers. For example, the operation information provided can be in multiple languages ​​such as English, Chinese, and Korean.

[0061] (Example 2) A train schedule rescheduling system according to an embodiment of the present invention uses AI to appropriately reschedule train schedules when traffic disruptions occur. The system collects traffic disruption information, proposes optimal train schedule rescheduling, and provides the results to mitigate disruption. For example, when a train accident or delay occurs, the AI ​​collects that information. For example, detailed information such as the location of the accident, the extent of the impact, and the delay time is collected. The system then proposes an optimal train schedule rescheduling based on the collected information. For example, the system proposes rerouting other trains to avoid the delayed section. Furthermore, the system monitors the effectiveness of the proposed schedule rescheduling in real time and reschedules the train schedule as necessary. For example, if the proposed schedule rescheduling is not working properly, the AI ​​makes a new proposal and reschedules the train schedule. This allows the system to prevent congestion at stations and reduce disruption caused by sudden schedule rescheduling. This allows the system to quickly and accurately reschedule train schedules when traffic disruptions occur. For example, when a train accident occurs, the AI ​​can quickly propose an optimal schedule rescheduling, minimizing passenger disruption. In addition, AI monitors the effects of timetable changes in real time, ensuring optimal operation conditions are maintained at all times.

[0062] A timetable rescheduling system according to an embodiment includes a collection unit, a rescheduling unit, and a provision unit. The collection unit collects information on traffic disruptions. Traffic disruptions include, but are not limited to, accidents, natural disasters, and construction work. The collection unit collects detailed information, such as the location of the accident, the extent of its impact, and delay times. The collection unit can also collect information from social media and news sites to comprehensively grasp the situation. For example, it collects posts from social media to understand eyewitness accounts of the accident and the situation at the scene. The rescheduling unit proposes an appropriate timetable rescheduling based on the information collected by the collection unit. Appropriate timetable rescheduling includes, but is not limited to, reducing delays and improving passenger convenience. The rescheduling unit, for example, analyzes the collected information and calculates an optimal timetable rescheduling. The rescheduling unit can also monitor the effectiveness of the proposed timetable rescheduling in real time and reschedule the timetable as necessary. For example, if the proposed timetable rescheduling is not working well, the rescheduling unit makes a new proposal and reschedules the timetable. The providing unit provides the results of the timetable rearrangement proposed by the rearrangement unit. The providing unit provides operation information based on the results of the proposed timetable rearrangement, for example. The providing unit also makes the operation information multilingual so that it can also accommodate foreign passengers. For example, the operation information provided can be multilingual, such as English, Chinese, and Korean. This allows the timetable rearrangement system according to the embodiment to perform appropriate timetable rearrangements in the event of a traffic jam, thereby reducing confusion.

[0063] The collection unit can collect detailed information including the location of the accident, the extent of its impact, and delay time. Detailed information includes, but is not limited to, the location of the accident, the extent of its impact, and delay time. For example, the collection unit identifies the location of the accident and grasps its extent of impact. The collection unit can also accurately measure and collect delay time. For example, the collection unit can identify the location of the accident using GPS data and display the extent of its impact on a map. The collection unit can also measure delay time in real time and record it in a database. By collecting detailed information, more accurate train schedule rescheduling becomes possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the location of the accident and the extent of its impact into the generation AI and cause the generation AI to collect detailed information.

[0064] The reconfiguration unit can analyze the collected information and calculate an appropriate timetable rearrangement. Examples of appropriate timetable rearrangement include, but are not limited to, shortening delay times and improving passenger convenience. The reconfiguration unit, for example, analyzes the collected information and calculates an optimal timetable rearrangement. For example, the reconfiguration unit suggests changing the operating routes of other trains to avoid sections where delays are occurring. The reconfiguration unit can also analyze passenger movement patterns and provide optimal transfer guidance. For example, the reconfiguration unit analyzes passengers' past movement patterns and provides optimal transfer guidance. This enables optimal timetable rearrangement by analyzing the collected information. Some or all of the above-described processing in the reconfiguration unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconfiguration unit can input the collected information to a generation AI and cause the generation AI to calculate an appropriate timetable rearrangement.

[0065] The providing unit can provide operation information based on the results of the proposed timetable rearrangement. The operation information includes, for example, the arrival time of the next train and an alternative route, but is not limited to these examples. For example, the providing unit provides the arrival time of the next train based on the results of the proposed timetable rearrangement. The providing unit can also propose an alternative route and provide appropriate information to passengers. For example, the providing unit provides optimal transfer guidance to passengers based on the results of the proposed timetable rearrangement. In this way, appropriate information can be provided to passengers by providing operation information based on the results of the proposed timetable rearrangement. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the results of the proposed timetable rearrangement to a generating AI and cause the generating AI to provide operation information.

[0066] The reorganization unit can monitor the effect of the proposed timetable rearrangement in real time and rearrange the timetable again. Specific methods for real-time monitoring include, but are not limited to, the type of monitoring system and the monitoring frequency. The reorganization unit, for example, monitors the effect of the proposed timetable rearrangement in real time and rearranges the timetable again as necessary. For example, if the proposed timetable rearrangement is not working well, the reorganization unit makes a new proposal and rearranges the timetable again. This makes it possible to always rearrange the timetable optimally by monitoring in real time. Some or all of the above-mentioned processing in the reorganization unit may be performed using AI, for example, or may be performed without using AI. For example, the reorganization unit can input the effect of the proposed timetable rearrangement into the generation AI and have the generation AI perform real-time monitoring.

[0067] The collection unit can estimate passenger emotions and determine the priority of information collection based on the estimated passenger emotions. For example, if passengers are feeling anxious, the collection unit can prioritize collecting detailed accident information and provide it quickly. Furthermore, if passengers are irritated, the collection unit can prioritize collecting delay time prediction information and provide accurate information. Furthermore, if passengers are calm, the collection unit can prioritize collecting information on the extent of the impact and grasp the overall situation. This enables more appropriate information to be provided by determining the priority of information collection according to passenger emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input passenger emotion data into the generation AI and have the generation AI determine the priority of information collection.

[0068] The collection unit can collect video or data from the scene using a drone or a sensor. Examples of drones and sensors include, but are not limited to, camera-equipped drones and temperature sensors. The collection unit, for example, flies a drone to collect aerial video of the accident scene and grasps the situation in real time. The collection unit can also install sensors to collect environmental data (temperature, humidity, gas concentration, etc.) from the accident scene. Furthermore, the collection unit can link the drone and the sensor to create a detailed 3D map of the accident scene and grasp the extent of the impact. This allows detailed information from the scene to be collected using the drone or sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the drone or sensor into the generation AI and have the generation AI collect video and data from the scene.

[0069] The collection unit can compare and analyze past delay data with current traffic conditions to predict delay times. Past delay data includes, for example, delay records from the past year and delay causes, but is not limited to, examples. For example, the collection unit can predict delay times by comparing the past delay data with current traffic conditions. The collection unit can also combine the past delay data with current weather information to more accurately predict delay times. Furthermore, the collection unit can predict delay times by comparing the past delay data with the current number of passengers. This improves the accuracy of delay time predictions by comparing the past data with current conditions. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input past delay data and current traffic condition data into a generation AI and cause the generation AI to predict delay times.

[0070] The collection unit can update the collected information in real time and build a database to always keep track of the latest situation. Specific methods for real-time updating include, but are not limited to, the update frequency and the technology used. For example, the collection unit can update the collected information in the database in real time to always keep track of the latest situation. The collection unit can also build the database on the cloud and make it accessible from multiple devices. Furthermore, the collection unit can increase the update frequency of the database to keep the information fresh. This allows the information to be updated in real time, so that the latest situation can always be kept track of. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the collected information to a generation AI and have the generation AI update the database in real time.

[0071] The collection unit can estimate passenger emotions and adjust the type of information to be collected based on the estimated passenger emotions. For example, if passengers are feeling anxious, the collection unit can prioritize collecting detailed accident information. Furthermore, if passengers are irritated, the collection unit can prioritize collecting delay time prediction information. Furthermore, if passengers are calm, the collection unit can prioritize collecting information on the extent of the impact. This allows for more appropriate information to be provided by adjusting the type of information to be collected according to passenger emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input passenger emotion data into the generation AI and cause the generation AI to adjust the type of information to be collected.

[0072] When collecting information on traffic disruptions, the collection unit can also collect information from social media and news sites to comprehensively grasp the situation. Examples of social media and news sites include, but are not limited to, LINE, Twitter, Facebook, and major news sites. The collection unit, for example, collects posts from social media to grasp eyewitness information about the accident and the situation at the scene. The collection unit can also collect information from news sites and check official announcements and news reports. Furthermore, the collection unit can integrate information from social media and news sites to comprehensively grasp the situation. This makes it possible to comprehensively grasp the situation by collecting information from social media and news sites. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information from social media and news sites into the generation AI and cause the generation AI to perform comprehensive situation assessment.

[0073] The collection unit can share the collected information with other transportation facilities to promote collaboration. Examples of other transportation facilities include, but are not limited to, buses, taxis, and shared ride services. For example, the collection unit can share the collected information with a bus company to propose an alternative route. The collection unit can also share the collected information with a taxi company to ensure passenger transportation. Furthermore, the collection unit can share the collected information with other railway companies to coordinate schedules. This allows collaboration by sharing information with other transportation facilities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected information into a generation AI and cause the generation AI to share information with other transportation facilities.

[0074] The collection unit can propose appropriate evacuation routes and alternative means to passengers based on the collected information. Appropriate evacuation routes and alternative means include, but are not limited to, the nearest evacuation shelter and alternative means of transportation. For example, the collection unit can propose an optimal evacuation route to passengers based on the collected information. The collection unit can also propose alternative means of transportation (such as a bus or taxi) to passengers based on the collected information. Furthermore, the collection unit can propose the nearest evacuation shelter to passengers based on the collected information. This ensures the safety of passengers by proposing appropriate evacuation routes and alternative means based on the collected information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected information to a generation AI and cause the generation AI to propose appropriate evacuation routes and alternative means.

[0075] The rescheduling unit can estimate passenger emotions and prioritize schedule rearrangements based on the estimated passenger emotions. For example, if passengers are feeling anxious, the rescheduling unit can quickly rearrange the schedule and prioritize resuming service. Furthermore, if passengers are irritated, the rescheduling unit can prioritize schedule rearrangements to minimize delays. Furthermore, if passengers are calm, the rescheduling unit can carefully rearrange the schedule so as not to widen the scope of impact. This enables more appropriate operation by prioritizing schedule rearrangements based on passenger emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the rescheduling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the rescheduling unit can input passenger emotion data into the generation AI and have the generation AI determine the schedule rearrangement priorities.

[0076] The rescheduling unit can analyze the collected information and refer to past success stories when calculating the optimal timetable rearrangement. Past success stories include, but are not limited to, past successful timetable rearrangement cases and factors behind success. The rescheduling unit, for example, calculates the optimal timetable rearrangement based on past successful timetable rearrangement cases during delays. The rescheduling unit can also calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during accidents. Furthermore, the rescheduling unit can calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during weather-related delays. This enables more effective timetable rearrangement by referring to past successful cases. Some or all of the above-described processing in the rescheduling unit may be performed using, for example, AI, or may be performed without using AI. For example, the rescheduling unit can input data from past successful cases into the generation AI and have the generation AI calculate the optimal timetable rearrangement.

[0077] When proposing a timetable rearrangement, the rearrangement unit can consider cooperation with other transportation modes to improve the overall transportation network. Cooperation with other transportation modes includes, but is not limited to, information sharing protocols and cooperation procedures. For example, the rearrangement unit can consider cooperation with other railway companies to propose a comprehensive timetable rearrangement. The rearrangement unit can also consider cooperation with bus companies to propose alternative routes. Furthermore, the rearrangement unit can consider cooperation with taxi companies to ensure passenger transportation. This enables the optimization of the overall transportation network by considering cooperation with other transportation modes. Some or all of the above-described processing in the rearrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the rearrangement unit can input cooperation data with other transportation modes into the generation AI and cause the generation AI to improve the overall transportation network.

[0078] When proposing a schedule rearrangement, the rearrangement unit can analyze passenger movement patterns and provide optimal transfer guidance. Passenger movement patterns include, but are not limited to, past movement history and movement tendencies. For example, the rearrangement unit can analyze passengers' past movement patterns and provide optimal transfer guidance. The rearrangement unit can also analyze passengers' current movement patterns in real time and provide optimal transfer guidance. Furthermore, the rearrangement unit can combine passenger movement patterns with traffic conditions to provide optimal transfer guidance. In this way, optimal transfer guidance can be provided by analyzing passenger movement patterns. Some or all of the above-described processing in the rearrangement unit may be performed, for example, using AI or without AI. For example, the rearrangement unit can input passenger movement pattern data into a generation AI and cause the generation AI to provide optimal transfer guidance.

[0079] The rescheduling unit can estimate passenger emotions and adjust the proposed schedule change based on the estimated passenger emotions. For example, if passengers are feeling anxious, the rescheduling unit can make a proposal that prioritizes a rapid resumption of service. Furthermore, if passengers are irritated, the rescheduling unit can make a proposal that minimizes delays. Furthermore, if passengers are calm, the rescheduling unit can make a careful proposal so as not to widen the scope of the impact. This allows for more appropriate schedule changes by adjusting the proposal according to passenger emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the rescheduling unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the rescheduling unit can input passenger emotion data into the generation AI and cause the generation AI to adjust the proposed schedule change.

[0080] The reconfiguration unit can also take external factors, including weather or event information, into consideration when proposing a schedule rearrangement. Weather or event information includes, but is not limited to, weather data, an event calendar, and the like. For example, the reconfiguration unit can take weather information into consideration and propose an optimal schedule rearrangement for when bad weather occurs. The reconfiguration unit can also take event information into consideration and propose an optimal schedule rearrangement for when an event is held. Furthermore, the reconfiguration unit can combine weather information and event information to propose an optimal schedule rearrangement. This enables more appropriate schedule rearrangement by taking external factors, such as weather and event information, into consideration. Some or all of the above-described processing in the reconfiguration unit can be performed, for example, using AI, or can be performed without using AI. For example, the reconfiguration unit can input weather and event information data into the generation AI and have the generation AI execute a schedule rearrangement proposal.

[0081] When proposing a schedule rearrangement, the rearrangement unit can perform customization according to passenger attributes. Passenger attributes include, but are not limited to, age, gender, and occupation, for example. The rearrangement unit can propose an optimal schedule rearrangement, for example, according to the passenger's age. The rearrangement unit can also propose an optimal schedule rearrangement according to the passenger's gender. Furthermore, the rearrangement unit can propose an optimal schedule rearrangement according to the passenger's occupation. This enables more appropriate schedule rearrangement by customizing according to passenger attributes. Some or all of the above-described processing in the rearrangement unit may be performed using AI, for example, or may be performed without using AI. For example, the rearrangement unit can input passenger attribute data into the generation AI and cause the generation AI to customize the schedule rearrangement.

[0082] The reordering unit can reflect passenger feedback when proposing a schedule rearrangement and improve the proposal. Passenger feedback includes, but is not limited to, for example, questionnaires and app rating functions. The reordering unit can, for example, collect passenger feedback and improve the schedule rearrangement proposal. The reordering unit can also reflect passenger feedback in real time and adjust the proposal. Furthermore, the reordering unit can analyze passenger feedback and use it in the next schedule rearrangement proposal. In this way, by reflecting passenger feedback, the accuracy of the proposal is improved. Some or all of the above-mentioned processing in the reordering unit may be performed, for example, using AI or without AI. For example, the reordering unit can input passenger feedback data into the generation AI and cause the generation AI to improve the proposal.

[0083] The providing unit can estimate passenger emotions and adjust the method of providing operation information based on the estimated passenger emotions. For example, if a passenger feels anxious, the providing unit can provide detailed operation information to give the passenger a sense of security. Furthermore, if a passenger feels irritated, the providing unit can provide concise and prompt operation information to reduce stress. Furthermore, if a passenger feels calm, the providing unit can provide an overall operation status to deepen the passenger's understanding. This enables more appropriate information provision by adjusting the method of providing operation information according to the passenger's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input passenger emotion data into the generation AI and cause the generation AI to adjust the method of providing operation information.

[0084] The providing unit can provide optimal transfer guidance to passengers based on the results of the proposed timetable rearrangement. The optimal transfer guidance includes, but is not limited to, transfer times and transfer routes. For example, the providing unit can provide optimal transfer guidance to passengers based on the results of the proposed timetable rearrangement. The providing unit can also suggest an alternative route to passengers based on the results of the proposed timetable rearrangement. Furthermore, the providing unit can guide passengers to the nearest evacuation site based on the results of the proposed timetable rearrangement. This improves passenger convenience by providing optimal transfer guidance based on the results of the proposed timetable rearrangement. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the results of the proposed timetable rearrangement to a generating AI and cause the generating AI to provide optimal transfer guidance.

[0085] The providing unit can provide the operation information in multiple languages ​​to accommodate foreign passengers. Examples of multilingual support include, but are not limited to, English, Chinese, and Korean. The providing unit can provide the operation information in multiple languages, such as English, Chinese, and Korean. The providing unit can also display the operation information in multiple languages ​​at stations with many foreign passengers. Furthermore, the providing unit can announce the operation information in multiple languages ​​during times when there are many foreign passengers. This multilingual support enables appropriate information to be provided to foreign passengers. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the multilingual version of the operation information into a generating AI and have the generating AI provide the information.

[0086] The providing unit can develop an interface for visually displaying the operation information to be provided. Specific methods for visually displaying the information include, but are not limited to, interface design and color usage. For example, the providing unit can use icons and color coding to visually display the operation information to be provided. The providing unit can also use maps and graphs to visually display the operation information to be provided. Furthermore, the providing unit can use animation to visually display the operation information to be provided. This makes it easier for passengers to understand the information by displaying it visually. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the development of a visually easy-to-understand interface to a generating AI and have the generating AI execute the interface design.

[0087] The providing unit can estimate passenger emotions and adjust the frequency of providing operation information based on the estimated passenger emotions. For example, if a passenger feels anxious, the providing unit can increase the frequency of providing operation information. Furthermore, if a passenger feels irritated, the providing unit can increase the frequency of providing operation information. Furthermore, if a passenger feels calm, the providing unit can maintain the normal frequency of providing operation information. This enables more appropriate information provision by adjusting the frequency of providing information according to the passenger emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input passenger emotion data into the generation AI and cause the generation AI to adjust the frequency of providing operation information.

[0088] The providing unit can provide the operation information to be provided through various media, such as a smartphone app or digital signage. Examples of various media include, but are not limited to, a smartphone app or digital signage. For example, the providing unit provides the operation information to be provided through a smartphone app. The providing unit can also provide the operation information to be provided through digital signage. Furthermore, the providing unit can provide the operation information to be provided on station information boards. In this way, providing the information through various media makes it easier for passengers to obtain the information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the operation information to be provided to a generating AI and cause the generating AI to provide the information through various media, such as a smartphone app or digital signage.

[0089] The providing unit can reflect the passenger's current location information in the operation information it provides and propose an optimal travel route. Examples of the passenger's current location information include, but are not limited to, GPS data and location information services. For example, the providing unit can reflect the passenger's current location information in the operation information it provides and propose an optimal travel route. The providing unit can also reflect the passenger's current location information in the operation information it provides and propose the nearest evacuation site. Furthermore, the providing unit can reflect the passenger's current location information in the operation information it provides and propose an alternative means of transportation. By reflecting the passenger's current location information, an optimal travel route can be proposed. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the passenger's current location information into the generating AI and cause the generating AI to propose an optimal travel route.

[0090] The providing unit can reflect passenger feedback in the operation information to be provided, thereby improving the accuracy of the information provided. Passenger feedback includes, but is not limited to, for example, questionnaires and app rating functions. The providing unit can, for example, reflect passenger feedback in the operation information to be provided, thereby improving the accuracy of the information provided. The providing unit can also reflect passenger feedback in real time in the operation information to be provided, thereby improving the accuracy of the information provided. Furthermore, the providing unit can analyze passenger feedback in the operation information to be provided and use it in the next information provision. In this way, by reflecting passenger feedback, the accuracy of the information provided is improved. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input passenger feedback data into the generating AI and cause the generating AI to improve the accuracy of the information provided. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, recombination unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on traffic disruptions using the camera 42 and microphone 38B of the smart device 14, and also collects information from social networking sites and news sites using the control unit 46A. The recombination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and proposes optimal timetable reorganization. The provision unit provides operation information in multiple languages ​​using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, recombination unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on traffic disruptions using the camera 42 and microphone 238 of the smart glasses 214, and also collects information from social networking sites and news sites via the control unit 46A. The recombination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and proposes optimal schedule reorganization. The provision unit provides operation information in multiple languages ​​using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, recombination unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects information on traffic disruptions using the camera 42 and microphone 238 of the headset terminal 314, and also collects information from social networking sites and news sites via the control unit 46A. The recombination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and proposes optimal timetable reorganization. The provision unit provides operation information in multiple languages ​​using, for example, the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, recombination unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on traffic obstructions using the camera 42 and microphone 238 of the robot 414, and also collects information from social media and news sites using the control unit 46A. The recombination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and proposes optimal timetable reorganization. The provision unit provides operation information in multiple languages ​​using, for example, the speaker 240 of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The rescheduling unit can estimate passenger emotions and prioritize timetable rearrangements based on the estimated passenger emotions. For example, if passengers are feeling anxious, the rescheduling unit can prioritize resuming service by quickly rearranging the timetable. Furthermore, if passengers are irritated, the rescheduling unit can prioritize timetable rearrangements to minimize delays. Furthermore, if passengers are calm, the rescheduling unit can carefully rearrange the timetable so as not to widen the scope of the impact. This allows for more appropriate operation by prioritizing timetable rearrangements based on passenger emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the rescheduling unit can be performed using, for example, an AI, or without an AI. For example, the rescheduling unit can input passenger emotion data into the generation AI and have the generation AI determine the priority of timetable rearrangements.

[0093] The collection unit can collect video or data from the scene using a drone or a sensor. Examples of drones and sensors include, but are not limited to, camera-equipped drones and temperature sensors. The collection unit, for example, flies a drone to collect aerial video of the accident scene and grasps the situation in real time. The collection unit can also install sensors to collect environmental data (temperature, humidity, gas concentration, etc.) from the accident scene. Furthermore, the collection unit can link the drone and the sensor to create a detailed 3D map of the accident scene and grasp the extent of the impact. This allows detailed information from the scene to be collected using the drone or sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the drone or sensor into the generation AI and have the generation AI collect video and data from the scene.

[0094] The providing unit can estimate passenger emotions and adjust the method of providing operation information based on the estimated passenger emotions. For example, if a passenger feels anxious, detailed operation information can be provided to reassure the passenger. Furthermore, if a passenger feels irritated, concise and prompt operation information can be provided to reduce stress. Furthermore, if a passenger feels calm, overall operation status can be provided to deepen understanding. This allows for more appropriate information provision by adjusting the method of providing operation information according to the passenger's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input passenger emotion data into the generation AI and cause the generation AI to adjust the method of providing operation information.

[0095] The collection unit can compare and analyze past delay data with current traffic conditions to predict delay times. Past delay data includes, for example, delay records from the past year and delay causes, but is not limited to, examples. For example, the collection unit can predict delay times by comparing the past delay data with current traffic conditions. The collection unit can also combine the past delay data with current weather information to more accurately predict delay times. Furthermore, the collection unit can predict delay times by comparing the past delay data with the current number of passengers. This improves the accuracy of delay time predictions by comparing the past data with current conditions. Some or all of the above-described processing in the collection unit may be performed, for example, using AI, or may be performed without AI. For example, the collection unit can input past delay data and current traffic condition data into a generation AI and cause the generation AI to predict delay times.

[0096] When proposing a timetable rearrangement, the rearrangement unit can consider cooperation with other transportation modes to improve the overall transportation network. Cooperation with other transportation modes includes, but is not limited to, information sharing protocols and cooperation procedures. For example, the rearrangement unit can consider cooperation with other railway companies to propose a comprehensive timetable rearrangement. The rearrangement unit can also consider cooperation with bus companies to propose alternative routes. Furthermore, the rearrangement unit can consider cooperation with taxi companies to ensure passenger transportation. This enables the optimization of the overall transportation network by considering cooperation with other transportation modes. Some or all of the above-described processing in the rearrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the rearrangement unit can input cooperation data with other transportation modes into the generation AI and cause the generation AI to improve the overall transportation network.

[0097] The providing unit can provide the operation information in multiple languages ​​to accommodate foreign passengers. Examples of multilingual support include, but are not limited to, English, Chinese, and Korean. The providing unit can provide the operation information in multiple languages, such as English, Chinese, and Korean. The providing unit can also display the operation information in multiple languages ​​at stations with many foreign passengers. Furthermore, the providing unit can announce the operation information in multiple languages ​​during times when there are many foreign passengers. This multilingual support enables appropriate information to be provided to foreign passengers. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the multilingual version of the operation information into a generating AI and have the generating AI provide the information.

[0098] The collection unit can estimate passenger emotions and determine the priority of information collection based on the estimated passenger emotions. For example, if passengers are feeling anxious, detailed accident information can be collected with priority and provided promptly. Furthermore, if passengers are irritated, delay time forecast information can be collected with priority and accurate information can be provided. Furthermore, if passengers are calm, the extent of the impact can be collected with priority and the overall situation can be grasped. This enables more appropriate information to be provided by determining the priority of information collection according to passenger emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input passenger emotion data into the generation AI and have the generation AI determine the priority of information collection.

[0099] The rescheduling unit can analyze the collected information and refer to past success stories when calculating the optimal timetable rearrangement. Past success stories include, but are not limited to, past successful timetable rearrangement cases and factors behind success. The rescheduling unit, for example, calculates the optimal timetable rearrangement based on past successful timetable rearrangement cases during delays. The rescheduling unit can also calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during accidents. Furthermore, the rescheduling unit can calculate the optimal timetable rearrangement based on past successful timetable rearrangement cases during weather-related delays. This enables more effective timetable rearrangement by referring to past successful cases. Some or all of the above-described processing in the rescheduling unit may be performed using, for example, AI, or may be performed without using AI. For example, the rescheduling unit can input data from past successful cases into the generation AI and have the generation AI calculate the optimal timetable rearrangement.

[0100] The providing unit can develop an interface for visually displaying the operation information to be provided. Specific methods for visually displaying the information include, but are not limited to, interface design and color usage. For example, the providing unit can use icons and color coding to visually display the operation information to be provided. The providing unit can also use maps and graphs to visually display the operation information to be provided. Furthermore, the providing unit can use animation to visually display the operation information to be provided. This makes it easier for passengers to understand the information by displaying it visually. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the development of a visually easy-to-understand interface to a generating AI and have the generating AI execute the interface design.

[0101] The providing unit can estimate passenger emotions and adjust the frequency of providing operation information based on the estimated passenger emotions. For example, if a passenger feels anxious, the frequency of providing operation information can be increased. Furthermore, if a passenger feels irritated, the frequency of providing operation information can be increased. Furthermore, if a passenger feels calm, the frequency of providing operation information can be kept normal. This allows for more appropriate information provision by adjusting the frequency of providing information according to the passenger emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input passenger emotion data into the generation AI and cause the generation AI to adjust the frequency of providing operation information.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection department collects information on traffic disruptions. Traffic disruptions include accidents, natural disasters, and construction work. The collection department collects detailed information such as the location of the accident, the extent of the impact, and the delay time. It also collects information from social media and news sites to gain a comprehensive understanding of the situation. For example, it collects posts from social media to understand eyewitness accounts of the accident and the situation at the scene. Step 2: The rescheduling unit proposes an appropriate timetable rearrangement based on the information collected by the collection unit. An appropriate timetable rearrangement includes reducing delays and improving passenger convenience. The rescheduling unit analyzes the collected information and calculates the optimal timetable rearrangement. It also monitors the effectiveness of the proposed timetable rearrangement in real time and rearranges the timetable again if necessary. For example, if the proposed timetable rearrangement is not working well, it will make a new proposal and rearrange the timetable again. Step 3: The provision unit provides the results of the timetable rearrangement proposed by the rearrangement unit. The provision unit provides operation information based on the results of the proposed timetable rearrangement. The operation information is also made multilingual to accommodate foreign passengers. For example, the operation information provided can be in multiple languages ​​such as English, Chinese, and Korean.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting traffic obstruction information; a reorganization unit that proposes an appropriate schedule reorganization based on the information collected by the collection unit; A providing unit that provides the results of the diamond reorganization proposed by the reorganization unit. A system characterized by:

2. The collecting unit Collect detailed information, including the location of the accident, the extent of the impact, and the delay time.

2. The system of claim 1.

3. The recombination part is Analyze the collected information and calculate appropriate timetable changes 2. The system of claim 1.

4. The providing unit Providing operation information based on the results of the proposed timetable changes 2. The system of claim 1.

5. The recombination part is Monitor the effectiveness of the proposed timetable changes in real time and reschedule the timetable 2. The system of claim 1.

6. The collecting unit Estimate passenger emotions and prioritize information gathering based on the estimated emotions 2. The system of claim 1.

7. The collecting unit Use drones or sensors to collect on-site footage or data 2. The system of claim 1.

8. The collecting unit Comparing and analyzing past delay data with current traffic conditions to predict delay times 2. The system of claim 1.

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