System

Abstract

To provide a system that enables a user to comfortably use trains while avoiding crowded conditions.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, an assignment unit, and a notification unit. The reception unit receives a user's input for a departure station, an arrival station, and a desired arrival time. The analysis unit analyzes railway company data based on the information received by the reception unit. The assignment unit allocates an optimal boarding time and train car, based on the analysis result obtained by the analysis unit. The notification unit notifies the user of the information assigned by the assignment 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that it is difficult for users to avoid crowds and travel comfortably on trains.

[0005] The system according to the embodiment aims to enable users to avoid crowds and use trains comfortably. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an allocation unit, and a notification unit. The reception unit receives input of a user's departure station, arrival station, and desired arrival time. The analysis unit analyzes data from the railway company based on the information received by the reception unit. The allocation unit allocates an optimal boarding time and car number based on the analysis results obtained by the analysis unit. The notification unit notifies the user of the information allocated by the allocation unit. Effect of the Invention

[0007] The system according to the embodiment can enable users to avoid crowding and use trains comfortably. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The congestion avoidance system according to the embodiment of the present invention is a system for users to use trains comfortably. In this system, the user registers the departure station, arrival station, and desired arrival time in a messenger app, and the AI ​​analyzes the data of the railway company, assigns the optimal boarding time and car number, and notifies the user. For example, when the user inputs "I want to travel from Shinjuku Station to Tokyo Station and arrive at 9:00 a.m.", this information is input to the AI. Next, the AI ​​analyzes the data of the railway company (the number of passengers passing through the ticket gates at each station, each user's commuter pass information and the stations where the user usually gets on and off, and the number of passengers detected by the sensors at the entrances and exits of each car) and assigns the optimal boarding time and car number after considering the user's desired arrival time. For example, the AI ​​determines that "If you leave Shinjuku Station at 8:30 a.m. and get on car 3, you will arrive at Tokyo Station at 9:00 a.m. and avoid congestion." Finally, the user is notified through the messenger app. For example, the user's smartphone receives a notification saying, "Please leave Shinjuku Station at 8:30 a.m. and get on car 3." This allows the user to avoid congestion and use the train comfortably. This system allows users to avoid crowds and ride trains comfortably. It also allows railway companies to disperse crowds. For example, by dispersing users on board, they will not be concentrated in specific times or cars, and overall congestion will be alleviated. In this way, the crowd avoidance system allows users to ride trains comfortably.

[0029] The congestion avoidance system according to the embodiment includes a reception unit, an analysis unit, an allocation unit, and a notification unit. The reception unit accepts input of the user's departure station, arrival station, and desired arrival time. For example, the user inputs "I want to travel from Shinjuku Station to Tokyo Station and arrive at 9:00 a.m." This information is input to the AI. The analysis unit analyzes the data of the railway company based on the information accepted by the reception unit. Specifically, data such as the number of passengers passing through the ticket gates at each station, each user's commuter pass information, the station where the user usually gets on and off, and the number of passengers detected by sensors at the entrances and exits of each car are collected and analyzed by the AI. For example, the AI ​​analyzes the number of passengers passing through the ticket gates at Shinjuku Station and the number of passengers in each car to grasp the congestion situation. The allocation unit allocates the optimal boarding time and car based on the analysis results obtained by the analysis unit. For example, the AI ​​determines that "if you leave Shinjuku Station at 8:30 a.m. and board car 3, you will arrive at Tokyo Station at 9:00 a.m. and avoid congestion." The notification unit notifies the user of the information allocated by the allocation unit. For example, a notification is sent to the user's smartphone saying, "Depart Shinjuku Station at 8:30 a.m. and board car No. 3." In this way, the congestion avoidance system according to the embodiment allows the user to use the train comfortably. The elements of the reception unit, the analysis unit, the allocation unit, and the notification unit are sequentially related to each other. In this way, the congestion avoidance system allows the user to use the train comfortably.

[0030] The reception unit accepts the input of the user's departure station, arrival station, and desired arrival time. For example, the user enters "I want to travel from Shinjuku Station to Tokyo Station and arrive at 9:00 a.m." This information is input into the AI. The reception unit provides an interface that allows the user to easily input information through a smartphone or computer. The interface has an intuitive and easy-to-use design, allowing the user to input the necessary information without hesitation. For example, the departure station and arrival station can be selected from a drop-down menu, and the desired arrival time can be easily set using a calendar or clock icon. There is also a function to remember information previously entered by the user, and it can automatically suggest frequently used stations and time periods, reducing the effort of inputting information. Furthermore, the reception unit transmits the input information to the AI ​​in real time so that analysis can be started immediately. This allows the user to proceed to the next step smoothly without waiting. In order to protect the user's privacy, the reception unit encrypts and transmits the input information to ensure the security of the data. It also has a function to check whether the information entered by the user is correct. For example, if the departure station and arrival station are the same or the desired arrival time is a time in the past, an error message is displayed and the user is prompted to re-enter the information. This allows the reception unit to support the user in inputting information accurately and quickly, thereby improving the efficiency of the entire system.

[0031] The analysis unit analyzes the data of the railway company based on the information received by the reception unit. Specifically, data such as the number of passengers passing through the ticket gates at each station, commuter pass information for each user, stations where passengers usually get on and off, and the number of passengers detected by sensors at the entrances and exits of each car are collected and analyzed by AI. For example, AI analyzes the number of passengers passing through the ticket gates at Shinjuku Station and the number of passengers in each car to grasp the congestion situation. The analysis unit collects these data in real time, and AI analyzes them using advanced algorithms. AI uses machine learning and deep learning technologies to compare past data with current data and predict congestion patterns and trends. For example, it can learn the tendency of congestion on specific days of the week and time periods and predict future congestion situations with high accuracy. In addition, the analysis unit can detect unusual congestion situations and abnormal data early using anomaly detection algorithms. This makes it possible to minimize the impact of congestion due to sudden events, accidents, etc. In addition, the analysis unit can provide optimal ride plans for individual users by utilizing the user's commuter pass information and data on stations where passengers usually get on and off. For example, the analysis unit can suggest the optimal boarding time and car number to avoid crowding, taking into account the stations and time periods that a particular user usually uses. This allows the analysis unit to provide customized services for each user and support comfortable travel.

[0032] The allocation unit allocates the optimal boarding time and car number based on the analysis results obtained by the analysis unit. For example, the AI ​​determines that "if you leave Shinjuku Station at 8:30 a.m. and board car number 3, you will arrive at Tokyo Station at 9 a.m. and avoid congestion." The allocation unit calculates the most comfortable and efficient boarding plan for the user based on the congestion prediction data provided by the analysis unit. Specifically, it selects the optimal departure time and car number to avoid congestion while taking into account the user's desired arrival time. For example, if a user wants to arrive at Tokyo Station at 9 a.m., the allocation unit compares the operation schedules of multiple trains and selects the train and car number with the least congestion. In addition, the allocation unit can prioritize the car number and boarding position that the user usually uses, taking into account the user's past boarding history and commuter pass information. This allows the user to travel comfortably in a familiar environment. Furthermore, the allocation unit can flexibly adjust the user's boarding plan based on congestion data updated in real time. For example, if sudden congestion or delays occur, the allocation unit immediately calculates a new plan and notifies the user. This allows the user to always use the optimal boarding plan based on the latest information. The allocation unit supports comfortable travel for users and provides an effective means for avoiding congestion.

[0033] The notification unit notifies the user of the information assigned by the allocation unit. For example, the user's smartphone receives a notification saying, "Depart Shinjuku Station at 8:30 a.m., board car number 3." The notification unit uses multiple notification methods to ensure that the user receives the information. Specifically, important information is delivered reliably by combining smartphone push notifications, emails, SMS, and even voice calls. In addition, the notification content is customized according to the user's language settings and preferences, and is displayed in an easy-to-understand manner. For example, the boarding position and the location of the car can be intuitively indicated using visual icons and maps. Furthermore, the notification unit also has a function to check whether the user has received the notification, and if there are unread notifications, it will send the notification again. This allows the user to take action without missing important information. The notification unit also has a function to accept feedback from the user, and can receive questions and opinions about the notification content in real time, for example. This can be used to improve the entire system. The notification unit plays an important role in providing users with quick and accurate information and supporting comfortable travel.

[0034] The congestion avoidance system includes a collection unit that collects commuter pass information of a user or data on stations where the user usually gets on and off. The collection unit collects commuter pass information of a user and data on stations where the user usually gets on and off. For example, the collection unit collects information such as the type of commuter pass, the section used, and the expiration date. In addition, as data on stations where the user usually gets on and off, the collection unit collects information such as the frequency of stations where the user gets on and off, the time period, and the day of the week. As a result, the collection unit can perform more accurate analysis by collecting the commuter pass information of a user and the data on stations where the user usually gets on and off. Some or all of the above-mentioned 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 commuter pass information of a user to AI and have the AI ​​analyze the commuter pass information.

[0035] The congestion avoidance system includes a collection unit that collects the number of passengers passing through the ticket gate at each station or the number of passengers in each car. The collection unit collects the number of passengers passing through the ticket gate at each station and the number of passengers in each car. For example, the collection unit collects information such as ticket gate count data and the number of passengers passing through by time period. In addition, the collection unit collects information such as data from an in-car sensor and the number of tickets sold as the number of passengers in each car. In this way, the collection unit can grasp the congestion situation by collecting the number of passengers passing through the ticket gate at each station and the number of passengers in each car. Some or all of the above-mentioned 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 ticket gate count data to AI and have the AI ​​analyze the number of passengers passing through the ticket gate.

[0036] The notification unit can provide notifications in real time. The notification unit provides notifications in real time. For example, notifications are sent to the user's smartphone in real time. Specific definitions and standards of real time include seconds, minutes, and frequency of data updates. This allows the notification unit to provide the user with the latest information by providing notifications in real time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can cause AI to perform the real-time notification.

[0037] The analysis unit can analyze the number of passengers passing through the ticket gates at each station and the number of passengers in each car to grasp the congestion situation. The analysis unit analyzes the number of passengers passing through the ticket gates at each station and the number of passengers in each car to grasp the congestion situation. For example, the analysis unit analyzes count data of the number of passengers passing through the ticket gates and sensor data of the number of passengers in each car. Specific evaluation criteria and analysis methods for the congestion situation include the boarding rate, passenger density, congestion index, etc. As a result, the analysis unit can grasp the congestion situation by analyzing the number of passengers passing through the ticket gates at each station and the number of passengers in each car. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the number of passengers passing through the ticket gates to AI and have AI execute the analysis of the congestion situation.

[0038] The allocation unit can allocate an appropriate boarding time and a car number based on the user's desired arrival time. The allocation unit allocates an appropriate boarding time and a car number based on the user's desired arrival time. For example, if the user wants to arrive at 9:00 a.m., a boarding time departing at 8:30 a.m. is allocated. Specific criteria and methods for determining the appropriate boarding time include congestion level, required time, user's preference, etc. Specific criteria and methods for selecting a car number include congestion level, user's preference, presence or absence of specific facilities, etc. In this way, the allocation unit can allocate an optimal boarding time and a car number by considering the user's desired arrival time. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's desired arrival time to AI and cause AI to execute the allocation of the optimal boarding time and a car number.

[0039] The reception unit can analyze the user's past input history and propose the optimal input method. The reception unit analyzes the user's past input history and proposes the optimal input method. For example, the departure station, arrival station, and desired arrival time frequently input by the user in the past are automatically displayed as candidates. In addition, the input method (voice, text, etc.) used by the user in the past is preferentially proposed. Furthermore, the departure station, arrival station, and desired arrival time to be used in a specific time period are predicted and proposed from the user's past input history. In this way, the reception unit can propose the optimal input method by analyzing the user's past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI and have the AI ​​execute the proposal of the optimal input method.

[0040] The reception unit can simplify input by automatically acquiring the user's current location information when inputting the departure station, arrival station, and desired arrival time. The reception unit can simplify input by automatically acquiring the user's current location information when inputting the departure station, arrival station, and desired arrival time. For example, when the user opens the app, the current location is automatically acquired and set as the departure station. In addition, when the user inputs the arrival station, the optimal candidate location is proposed in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the current location is updated in real time and reflected as the departure station. Thereby, the reception unit can simplify input by automatically acquiring the user's current location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's current location information to AI and cause AI to execute simplification of input.

[0041] The reception unit can automatically suggest candidate locations by referring to the user's past movement history when the departure station, arrival station, and desired arrival time are input. The reception unit automatically suggests candidate locations by referring to the user's past movement history when the departure station, arrival station, and desired arrival time are input. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. In addition, the reception unit predicts places that the user will visit on a specific day of the week or time period and suggests them as candidate locations. Furthermore, the reception unit analyzes the user's past movement patterns and suggests optimal candidate locations. As a result, the reception unit can automatically suggest candidate locations by referring to the user's past movement history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past movement history into AI and cause AI to automatically suggest candidate locations.

[0042] The reception unit can make a proposal based on the schedule by referring to the calendar information of the user when the departure station, arrival station, and desired arrival time are input. The reception unit makes a proposal based on the schedule by referring to the calendar information of the user when the departure station, arrival station, and desired arrival time are input. For example, the reception unit automatically sets the departure station, arrival station, and desired arrival time by referring to the schedule registered in the user's calendar. In addition, a place related to a specific event is proposed as a candidate location from the calendar information of the user. Furthermore, an optimal route according to the schedule is proposed based on the calendar information of the user. This allows the reception unit to make a proposal based on the schedule by referring to the calendar information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the calendar information of the user to the AI ​​and cause the AI ​​to execute a proposal based on the schedule.

[0043] The analysis unit can analyze data of the railway company in real time and predict the congestion situation. The analysis unit analyzes data of the railway company in real time and predicts the congestion situation. For example, the number of people passing through the ticket gate at each station is analyzed in real time and the congestion situation is predicted. Also, the number of passengers in each car is analyzed in real time and the congestion situation is predicted. Furthermore, the commuter pass information of each user and the station where the user usually gets on and off are analyzed in real time and the congestion situation is predicted. Specific definitions and standards of real time include seconds, minutes, and frequency of data updates. This allows the analysis unit to predict the congestion situation by analyzing data of the railway company in real time. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the railway company to AI and cause AI to execute a prediction of the congestion situation.

[0044] The analysis unit can analyze the number of passengers passing through the ticket gate at each station and the number of passengers in each car for each time period, and grasp the detailed congestion pattern. The analysis unit analyzes the number of passengers passing through the ticket gate at each station and the number of passengers in each car for each time period, and grasp the detailed congestion pattern. For example, the number of passengers passing through the ticket gate at each station is analyzed for each time period, and grasp the detailed congestion pattern. In addition, the number of passengers in each car is analyzed for each time period, and grasp the detailed congestion pattern. Furthermore, the commuter pass information of each user and the station where the user usually gets on and off are analyzed for each time period, and grasp the detailed congestion pattern. Specific classifications and analysis methods for each time period include peak time, off-peak time, and classification by time period. As a result, the analysis unit can grasp the detailed congestion pattern by analyzing the number of passengers passing through the ticket gate at each station and the number of passengers in each car for each time period. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the number of passengers passing through the ticket gate at each station to AI, and cause AI to execute the analysis of the congestion pattern.

[0045] The analysis unit can incorporate weather data and event information into the analysis in addition to the data of the railway company. The analysis unit incorporates weather data and event information into the analysis in addition to the data of the railway company. For example, the analysis incorporates weather data into the analysis to predict the congestion situation in rainy weather. Also, the analysis incorporates event information into the analysis to predict the congestion situation at the time of a specific event. Furthermore, the weather data and the event information are combined and analyzed to perform a detailed congestion prediction. Specific types and acquisition methods of weather data include temperature, precipitation, wind speed, etc. Also, specific types and acquisition methods of event information include the type, date, time, location, etc. of the event. As a result, the analysis unit can incorporate the weather data and event information into the analysis to perform a more detailed congestion prediction. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input weather data and event information into AI and cause AI to execute congestion prediction.

[0046] The analysis unit can analyze data of other transportation means and provide optimal transfer guidance. The analysis unit analyzes data of other transportation means and provides optimal transfer guidance. For example, the operation status of other transportation means is analyzed and optimal transfer guidance is provided. In addition, the congestion status of other transportation means is analyzed and optimal transfer guidance is provided. Furthermore, the operation schedule of other transportation means is analyzed and optimal transfer guidance is provided. Specific types and acquisition methods of data of other transportation means include bus operation information, taxi position information, and the like. As a result, the analysis unit can analyze data of other transportation means and provide optimal transfer guidance. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data of other transportation means to AI and cause AI to execute transfer guidance.

[0047] The allocation unit can analyze the user's past riding history and customize the optimal boarding time and car number. The allocation unit analyzes the user's past riding history and customizes the optimal boarding time and car number. For example, the optimal boarding time and car number are proposed based on the boarding time and car number used by the user in the past. Also, a boarding time and car number that avoids congestion are proposed from the user's past riding history. Furthermore, the user's past riding history is analyzed to propose the most efficient boarding time and car number. Specific contents and analysis methods of the past riding history include the boarding date and time, the boarding route, and the frequency. As a result, the allocation unit can customize the optimal boarding time and car number by analyzing the user's past riding history. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's past riding history into AI and cause AI to execute customization of the optimal boarding time and car number.

[0048] The allocation unit can propose an appropriate boarding time based on the user's current schedule. The allocation unit proposes an appropriate boarding time based on the user's current schedule. For example, the allocation unit refers to the user's calendar information and proposes an optimal boarding time. Also, the allocation unit considers the user's current schedule and proposes an optimal boarding time. Furthermore, the allocation unit proposes an optimal boarding time according to the user's plan. Specific contents and reference methods of the current schedule include the type of plan, date and time, location, etc. As a result, the allocation unit can propose an optimal boarding time by considering the user's current schedule. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's current schedule to AI and cause AI to execute the proposal of an optimal boarding time.

[0049] The allocation unit can allocate an appropriate car number to avoid congestion based on the boarding plans of other users. The allocation unit allocates an appropriate car number to avoid congestion based on the boarding plans of other users. For example, the allocation unit considers the boarding plans of other users and proposes an optimal car number to avoid congestion. In addition, the boarding plans of other users are analyzed and the most efficient car number is proposed. Furthermore, the optimal car number to avoid congestion is allocated based on the boarding plans of other users. The specific contents and acquisition methods of the boarding plans of other users include the boarding date and time, the boarding route, the number of people, etc. As a result, the allocation unit can allocate an optimal car number to avoid congestion by considering the boarding plans of other users. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the boarding plans of other users to AI and cause AI to execute the car number allocation.

[0050] The allocation unit can propose the optimal boarding time and car number in consideration of the user's health condition. The allocation unit proposes the optimal boarding time and car number in consideration of the user's health condition. For example, if the user is tired, the shortest route is proposed. Also, if the user is looking for healthy exercise, a slightly longer route is proposed. Furthermore, if the user is not feeling well, a route including a rest point is proposed. Specific evaluation criteria and acquisition methods for the health condition include heart rate, blood pressure, body temperature, etc. As a result, the allocation unit can propose the optimal boarding time and car number by considering the user's health condition. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's health condition data into AI and cause AI to execute the proposal of the optimal boarding time and car number.

[0051] The notification unit can analyze the user's past notification history and select the optimal notification method. The notification unit analyzes the user's past notification history and selects the optimal notification method. For example, the notification unit preferentially suggests notification methods (push notification, email, etc.) that the user has used in the past. In addition, the notification unit suggests the optimal notification method for a specific time period from the user's past notification history. Furthermore, the notification unit analyzes the user's past notification history and selects the most effective notification method. Specific contents and analysis methods of the past notification history include notification date and time, notification contents, reactions, etc. As a result, the notification unit can select the optimal notification method by analyzing the user's past notification history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history into AI and have the AI ​​select the optimal notification method.

[0052] The notification unit can customize the optimal notification content by considering the current situation of the user. The notification unit customizes the optimal notification content by considering the current situation of the user. For example, when the user is on the move, a concise notification content is provided. Also, when the user is at home, a detailed notification content is provided. Furthermore, when the user is in a meeting, the notification is performed sparingly. Specific contents of the current situation and acquisition methods include location information, activity status, surrounding environment, etc. As a result, the notification unit can customize the optimal notification content by considering the current situation of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's current situation data into AI and have the AI ​​execute customization of the notification content.

[0053] The notification unit can select the optimal notification method by considering the device information of the user. The notification unit selects the optimal notification method by considering the device information of the user. For example, if the user is using a smartphone, push notification is given priority. Also, if the user is using a tablet, email notification is given priority. Furthermore, if the user is using a smartwatch, vibration notification is given priority. Specific types and acquisition methods of device information include the smartphone model, OS version, connection status, and the like. As a result, the notification unit can select the optimal notification method by considering the device information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the device information of the user to AI and cause the AI ​​to select the optimal notification method.

[0054] The notification unit can propose the optimal notification timing in consideration of the user's schedule. The notification unit proposes the optimal notification timing in consideration of the user's schedule. For example, the notification unit proposes the optimal notification timing by referring to the user's calendar information. Also, the notification unit proposes the optimal notification timing in consideration of the user's current schedule. Furthermore, the notification unit proposes the optimal notification timing in accordance with the user's schedule. Specific contents of the schedule and the reference method include the type of schedule, date and time, location, etc. As a result, the notification unit can propose the optimal notification timing by considering the user's schedule. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's schedule into AI and have the AI ​​execute the proposal of the optimal notification timing.

[0055] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit preferentially proposes a data collection method (GPS, Wi-Fi, etc.) that the user has used in the past. In addition, the collection unit proposes the optimal collection method for a specific time period from the user's past data collection history. Furthermore, the collection unit analyzes the user's past data collection history and selects the most effective collection method. Specific contents and analysis methods of the past data collection history include collection date and time, collection contents, frequency, etc. As a result, the collection unit can select the optimal collection method by analyzing the user's past data collection history. Part 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 the user's past data collection history to AI and cause AI to select the optimal collection method.

[0056] The collection unit can propose optimal data collection timing in consideration of the user's current situation. The collection unit proposes optimal data collection timing in consideration of the user's current situation. For example, when the user is on the move, data is collected in real time. Also, when the user is at home, data is collected periodically. Furthermore, when the user is in a meeting, data collection is performed sparingly. Specific contents and acquisition methods of the current situation include location information, activity status, surrounding environment, etc. As a result, the collection unit can propose optimal data collection timing by considering the user's current situation. Part 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 the user's current situation data to AI and have the AI ​​execute the proposal of data collection timing.

[0057] The collection unit can analyze the social media activity of the user and collect related data. The collection unit analyzes the social media activity of the user and collects related data. For example, the collection unit analyzes the social media activity of the user and collects related location information. In addition, the collection unit analyzes the social media activity of the user and collects related event information. In addition, the collection unit analyzes the social media activity of the user and collects related interest data. Specific contents and analysis methods of social media activities include posted contents, number of likes, comments, etc. As a result, the collection unit can collect related data by analyzing the social media activity of the user. Part 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 the social media activity data of the user to AI and cause AI to collect related data.

[0058] The collection unit can propose an optimal data collection method in consideration of the user's health condition. The collection unit proposes an optimal data collection method in consideration of the user's health condition. For example, if the user is tired, the data collection frequency is reduced. Also, if the user is seeking healthy exercise, the data collection frequency is increased. Furthermore, if the user is in poor health, data collection is performed sparingly. Specific evaluation criteria and acquisition methods for the health condition include heart rate, blood pressure, body temperature, etc. As a result, the collection unit can propose an optimal data collection method by considering the user's health condition. Part 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 the user's health condition data into AI and have the AI ​​execute the proposal of the optimal data collection method.

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

[0060] The analysis unit can analyze the user's past riding history and suggest the optimal boarding time and car number to avoid crowding. For example, the analysis unit can analyze data on boarding times and car numbers that the user has chosen in the past to avoid crowding, and suggest similar patterns. In addition, new suggestions for avoiding crowding can be made based on data on boarding times and car numbers used by the user in the past. Furthermore, the analysis unit can analyze the user's past riding history and suggest the optimal boarding time and car number to avoid crowding during a specific time period or day of the week. In this way, the analysis unit can provide a more comfortable riding experience by utilizing the user's past riding history.

[0061] The notification unit can suggest the optimal boarding time and car number in real time based on the user's current location information. For example, when the user arrives at a station, the current location information is acquired and the optimal boarding time and car number is suggested. Also, when the user is moving, the location information can be updated in real time and the optimal boarding time and car number can be suggested. Furthermore, when the user is in a specific area, the optimal boarding time and car number can be suggested taking into account the congestion situation in that area. This allows the notification unit to make more appropriate suggestions by utilizing the user's current location information.

[0062] The allocation unit can suggest the optimal boarding time and car number by taking into account the user's health condition. For example, if the user is tired, the shortest route is suggested. Also, if the user is looking for healthy exercise, a slightly longer route is suggested. Furthermore, if the user is feeling unwell, a route including rest points is suggested. Specific evaluation criteria and acquisition methods for the health condition include heart rate, blood pressure, body temperature, etc. This allows the allocation unit to suggest the optimal boarding time and car number by taking into account the user's health condition.

[0063] The analysis unit can incorporate weather data into the analysis to predict congestion. For example, it can predict congestion on rainy days and suggest optimal boarding times and car numbers to users. It can also predict congestion on hot and cold days and make suggestions based on that. Furthermore, it can combine weather data with other data for analysis to make more detailed congestion predictions. This allows the analysis unit to make more accurate congestion predictions by utilizing weather data.

[0064] The notification unit can select the optimal notification method by taking into account the device information of the user. For example, if the user is using a smartphone, push notification is given priority. Also, if the user is using a tablet, email notification is given priority. Furthermore, if the user is using a smartwatch, vibration notification is given priority. Specific types and acquisition methods of device information include the smartphone model, OS version, connection status, etc. This allows the notification unit to select the optimal notification method by taking into account the device information of the user.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit accepts the user's input of the departure station, arrival station, and desired arrival time. For example, the user inputs, "I want to travel from Shinjuku Station to Tokyo Station, arriving at 9:00 a.m." This information is input into the AI. Step 2: The analysis unit analyzes the railway company's data based on the information received by the reception unit. Specifically, data such as the number of passengers passing through the ticket gates at each station, each user's commuter pass information and usual stations where passengers get on and off, and the number of passengers detected by sensors at the entrances and exits of each car are collected and analyzed by AI. For example, AI analyzes the number of passengers passing through the ticket gates at Shinjuku Station and the number of passengers in each car to grasp the congestion situation. Step 3: The allocation unit allocates the optimal boarding time and car number based on the analysis results obtained by the analysis unit. For example, the AI ​​may determine that "if you depart Shinjuku Station at 8:30 a.m. and board car number 3, you will arrive at Tokyo Station at 9 a.m. and avoid crowding." Step 4: The notification unit notifies the user of the information assigned by the allocation unit. For example, the user's smartphone receives a notification saying, "Depart Shinjuku Station at 8:30 a.m., board car No. 3."

[0067] (Example 2) The congestion avoidance system according to the embodiment of the present invention is a system for users to use trains comfortably. In this system, the user registers the departure station, arrival station, and desired arrival time in a messenger app, and the AI ​​analyzes the data of the railway company, assigns the optimal boarding time and car number, and notifies the user. For example, when the user inputs "I want to travel from Shinjuku Station to Tokyo Station and arrive at 9:00 a.m.", this information is input to the AI. Next, the AI ​​analyzes the data of the railway company (the number of passengers passing through the ticket gates at each station, each user's commuter pass information and the stations where the user usually gets on and off, and the number of passengers detected by the sensors at the entrances and exits of each car) and assigns the optimal boarding time and car number after considering the user's desired arrival time. For example, the AI ​​determines that "If you leave Shinjuku Station at 8:30 a.m. and get on car 3, you will arrive at Tokyo Station at 9:00 a.m. and avoid congestion." Finally, the user is notified through the messenger app. For example, the user's smartphone receives a notification saying, "Please leave Shinjuku Station at 8:30 a.m. and get on car 3." This allows the user to avoid congestion and use the train comfortably. This system allows users to avoid crowds and ride trains comfortably. It also allows railway companies to disperse crowds. For example, by dispersing users on board, they will not be concentrated in specific times or cars, and overall congestion will be alleviated. In this way, the crowd avoidance system allows users to ride trains comfortably.

[0068] The congestion avoidance system according to the embodiment includes a reception unit, an analysis unit, an allocation unit, and a notification unit. The reception unit accepts input of the user's departure station, arrival station, and desired arrival time. For example, the user inputs "I want to travel from Shinjuku Station to Tokyo Station and arrive at 9:00 a.m." This information is input to the AI. The analysis unit analyzes the data of the railway company based on the information accepted by the reception unit. Specifically, data such as the number of passengers passing through the ticket gates at each station, each user's commuter pass information, the station where the user usually gets on and off, and the number of passengers detected by sensors at the entrances and exits of each car are collected and analyzed by the AI. For example, the AI ​​analyzes the number of passengers passing through the ticket gates at Shinjuku Station and the number of passengers in each car to grasp the congestion situation. The allocation unit allocates the optimal boarding time and car based on the analysis results obtained by the analysis unit. For example, the AI ​​determines that "if you leave Shinjuku Station at 8:30 a.m. and board car 3, you will arrive at Tokyo Station at 9:00 a.m. and avoid congestion." The notification unit notifies the user of the information allocated by the allocation unit. For example, a notification is sent to the user's smartphone saying, "Depart Shinjuku Station at 8:30 a.m. and board car No. 3." In this way, the congestion avoidance system according to the embodiment allows the user to use the train comfortably. The elements of the reception unit, the analysis unit, the allocation unit, and the notification unit are sequentially related to each other. In this way, the congestion avoidance system allows the user to use the train comfortably.

[0069] The reception unit accepts the input of the user's departure station, arrival station, and desired arrival time. For example, the user enters "I want to travel from Shinjuku Station to Tokyo Station and arrive at 9:00 a.m." This information is input into the AI. The reception unit provides an interface that allows the user to easily input information through a smartphone or computer. The interface has an intuitive and easy-to-use design, allowing the user to input the necessary information without hesitation. For example, the departure station and arrival station can be selected from a drop-down menu, and the desired arrival time can be easily set using a calendar or clock icon. There is also a function to remember information previously entered by the user, and it can automatically suggest frequently used stations and time periods, reducing the effort of inputting information. Furthermore, the reception unit transmits the input information to the AI ​​in real time so that analysis can be started immediately. This allows the user to proceed to the next step smoothly without waiting. In order to protect the user's privacy, the reception unit encrypts and transmits the input information to ensure the security of the data. It also has a function to check whether the information entered by the user is correct. For example, if the departure station and arrival station are the same or the desired arrival time is a time in the past, an error message is displayed and the user is prompted to re-enter the information. This allows the reception unit to support the user in inputting information accurately and quickly, thereby improving the efficiency of the entire system.

[0070] The analysis unit analyzes the data of the railway company based on the information received by the reception unit. Specifically, data such as the number of passengers passing through the ticket gates at each station, commuter pass information for each user, stations where passengers usually get on and off, and the number of passengers detected by sensors at the entrances and exits of each car are collected and analyzed by AI. For example, AI analyzes the number of passengers passing through the ticket gates at Shinjuku Station and the number of passengers in each car to grasp the congestion situation. The analysis unit collects these data in real time, and AI analyzes them using advanced algorithms. AI uses machine learning and deep learning technologies to compare past data with current data and predict congestion patterns and trends. For example, it can learn the tendency of congestion on specific days of the week and time periods and predict future congestion situations with high accuracy. In addition, the analysis unit can detect unusual congestion situations and abnormal data early using anomaly detection algorithms. This makes it possible to minimize the impact of congestion due to sudden events, accidents, etc. In addition, the analysis unit can provide optimal ride plans for individual users by utilizing the user's commuter pass information and data on stations where passengers usually get on and off. For example, the analysis unit can suggest the optimal boarding time and car number to avoid crowding, taking into account the stations and time periods that a particular user usually uses. This allows the analysis unit to provide customized services for each user and support comfortable travel.

[0071] The allocation unit allocates the optimal boarding time and car number based on the analysis results obtained by the analysis unit. For example, the AI ​​determines that "if you leave Shinjuku Station at 8:30 a.m. and board car number 3, you will arrive at Tokyo Station at 9 a.m. and avoid congestion." The allocation unit calculates the most comfortable and efficient boarding plan for the user based on the congestion prediction data provided by the analysis unit. Specifically, it selects the optimal departure time and car number to avoid congestion while taking into account the user's desired arrival time. For example, if a user wants to arrive at Tokyo Station at 9 a.m., the allocation unit compares the operation schedules of multiple trains and selects the train and car number with the least congestion. In addition, the allocation unit can prioritize the car number and boarding position that the user usually uses, taking into account the user's past boarding history and commuter pass information. This allows the user to travel comfortably in a familiar environment. Furthermore, the allocation unit can flexibly adjust the user's boarding plan based on congestion data updated in real time. For example, if sudden congestion or delays occur, the allocation unit immediately calculates a new plan and notifies the user. This allows the user to always use the optimal boarding plan based on the latest information. The allocation unit supports comfortable travel for users and provides an effective means for avoiding congestion.

[0072] The notification unit notifies the user of the information assigned by the allocation unit. For example, the user's smartphone receives a notification saying, "Depart Shinjuku Station at 8:30 a.m., board car number 3." The notification unit uses multiple notification methods to ensure that the user receives the information. Specifically, important information is delivered reliably by combining smartphone push notifications, emails, SMS, and even voice calls. In addition, the notification content is customized according to the user's language settings and preferences, and is displayed in an easy-to-understand manner. For example, the boarding position and the location of the car can be intuitively indicated using visual icons and maps. Furthermore, the notification unit also has a function to check whether the user has received the notification, and if there are unread notifications, it will send the notification again. This allows the user to take action without missing important information. The notification unit also has a function to accept feedback from the user, and can receive questions and opinions about the notification content in real time, for example. This can be used to improve the entire system. The notification unit plays an important role in providing users with quick and accurate information and supporting comfortable travel.

[0073] The congestion avoidance system includes a collection unit that collects commuter pass information of a user or data on stations where the user usually gets on and off. The collection unit collects commuter pass information of a user and data on stations where the user usually gets on and off. For example, the collection unit collects information such as the type of commuter pass, the section used, and the expiration date. In addition, as data on stations where the user usually gets on and off, the collection unit collects information such as the frequency of stations where the user gets on and off, the time period, and the day of the week. As a result, the collection unit can perform more accurate analysis by collecting the commuter pass information of a user and the data on stations where the user usually gets on and off. Some or all of the above-mentioned 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 commuter pass information of a user to AI and have the AI ​​analyze the commuter pass information.

[0074] The congestion avoidance system includes a collection unit that collects the number of passengers passing through the ticket gate at each station or the number of passengers in each car. The collection unit collects the number of passengers passing through the ticket gate at each station and the number of passengers in each car. For example, the collection unit collects information such as ticket gate count data and the number of passengers passing through by time period. In addition, the collection unit collects information such as data from an in-car sensor and the number of tickets sold as the number of passengers in each car. In this way, the collection unit can grasp the congestion situation by collecting the number of passengers passing through the ticket gate at each station and the number of passengers in each car. Some or all of the above-mentioned 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 ticket gate count data to AI and have the AI ​​analyze the number of passengers passing through the ticket gate.

[0075] The notification unit can provide notifications in real time. The notification unit provides notifications in real time. For example, notifications are sent to the user's smartphone in real time. Specific definitions and standards of real time include seconds, minutes, and frequency of data updates. This allows the notification unit to provide the user with the latest information by providing notifications in real time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can cause AI to perform the real-time notification.

[0076] The analysis unit can analyze the number of passengers passing through the ticket gates at each station and the number of passengers in each car to grasp the congestion situation. The analysis unit analyzes the number of passengers passing through the ticket gates at each station and the number of passengers in each car to grasp the congestion situation. For example, the analysis unit analyzes count data of the number of passengers passing through the ticket gates and sensor data of the number of passengers in each car. Specific evaluation criteria and analysis methods for the congestion situation include the boarding rate, passenger density, congestion index, etc. As a result, the analysis unit can grasp the congestion situation by analyzing the number of passengers passing through the ticket gates at each station and the number of passengers in each car. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the number of passengers passing through the ticket gates to AI and have AI execute the analysis of the congestion situation.

[0077] The allocation unit can allocate an appropriate boarding time and a car number based on the user's desired arrival time. The allocation unit allocates an appropriate boarding time and a car number based on the user's desired arrival time. For example, if the user wants to arrive at 9:00 a.m., a boarding time departing at 8:30 a.m. is allocated. Specific criteria and methods for determining the appropriate boarding time include congestion level, required time, user's preference, etc. Specific criteria and methods for selecting a car number include congestion level, user's preference, presence or absence of specific facilities, etc. In this way, the allocation unit can allocate an optimal boarding time and a car number by considering the user's desired arrival time. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's desired arrival time to AI and cause AI to execute the allocation of the optimal boarding time and a car number.

[0078] The reception unit can estimate the user's emotion and adjust the input method of the departure station, arrival station, and desired arrival time based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the input method of the departure station, arrival station, and desired arrival time based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided and the input procedure is minimized. Also, when the user is relaxed, detailed input options are provided and a customizable input method is suggested. Furthermore, when the user is in a hurry, voice input is prioritized and the departure station, arrival station, and desired arrival time can be input quickly. In this way, the reception unit can provide an interface that is easy for the user to use by adjusting the input method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0079] The reception unit can analyze the user's past input history and propose the optimal input method. The reception unit analyzes the user's past input history and proposes the optimal input method. For example, the departure station, arrival station, and desired arrival time frequently input by the user in the past are automatically displayed as candidates. In addition, the input method (voice, text, etc.) used by the user in the past is preferentially proposed. Furthermore, the departure station, arrival station, and desired arrival time to be used in a specific time period are predicted and proposed from the user's past input history. In this way, the reception unit can propose the optimal input method by analyzing the user's past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI and have the AI ​​execute the proposal of the optimal input method.

[0080] The reception unit can simplify input by automatically acquiring the user's current location information when inputting the departure station, arrival station, and desired arrival time. The reception unit can simplify input by automatically acquiring the user's current location information when inputting the departure station, arrival station, and desired arrival time. For example, when the user opens the app, the current location is automatically acquired and set as the departure station. In addition, when the user inputs the arrival station, the optimal candidate location is proposed in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the current location is updated in real time and reflected as the departure station. Thereby, the reception unit can simplify input by automatically acquiring the user's current location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's current location information to AI and cause AI to execute simplification of input.

[0081] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the design of the input interface based on the estimated user's emotion. For example, when the user is nervous, an interface with calm colors is provided to reduce visual stress. Furthermore, when the user is having fun, an interface with bright colors is provided to make the input work enjoyable. Furthermore, when the user is tired, an interface with simple and high visibility is provided to facilitate the input work. In this way, the reception unit can provide an interface that is easy for the user to use by adjusting the design of the input interface based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The reception unit can automatically suggest candidate locations by referring to the user's past movement history when the departure station, arrival station, and desired arrival time are input. The reception unit automatically suggests candidate locations by referring to the user's past movement history when the departure station, arrival station, and desired arrival time are input. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. In addition, the reception unit predicts places that the user will visit on a specific day of the week or time period and suggests them as candidate locations. Furthermore, the reception unit analyzes the user's past movement patterns and suggests optimal candidate locations. As a result, the reception unit can automatically suggest candidate locations by referring to the user's past movement history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past movement history into AI and cause AI to automatically suggest candidate locations.

[0083] The reception unit can make a proposal based on the schedule by referring to the calendar information of the user when the departure station, arrival station, and desired arrival time are input. The reception unit makes a proposal based on the schedule by referring to the calendar information of the user when the departure station, arrival station, and desired arrival time are input. For example, the reception unit automatically sets the departure station, arrival station, and desired arrival time by referring to the schedule registered in the user's calendar. In addition, a place related to a specific event is proposed as a candidate location from the calendar information of the user. Furthermore, an optimal route according to the schedule is proposed based on the calendar information of the user. This allows the reception unit to make a proposal based on the schedule by referring to the calendar information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the calendar information of the user to the AI ​​and cause the AI ​​to execute a proposal based on the schedule.

[0084] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the analysis algorithm based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis is performed and an optimal route is proposed. Also, when the user is in a hurry, a quick analysis is performed and the shortest route is proposed. Furthermore, when the user is excited, an analysis result with a visually stimulating effect is provided. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the analysis algorithm based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the analysis algorithm.

[0085] The analysis unit can analyze data of the railway company in real time and predict the congestion situation. The analysis unit analyzes data of the railway company in real time and predicts the congestion situation. For example, the number of people passing through the ticket gate at each station is analyzed in real time and the congestion situation is predicted. Also, the number of passengers in each car is analyzed in real time and the congestion situation is predicted. Furthermore, the commuter pass information of each user and the station where the user usually gets on and off are analyzed in real time and the congestion situation is predicted. Specific definitions and standards of real time include seconds, minutes, and frequency of data updates. This allows the analysis unit to predict the congestion situation by analyzing data of the railway company in real time. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the railway company to AI and cause AI to execute a prediction of the congestion situation.

[0086] The analysis unit can analyze the number of passengers passing through the ticket gate at each station and the number of passengers in each car for each time period, and grasp the detailed congestion pattern. The analysis unit analyzes the number of passengers passing through the ticket gate at each station and the number of passengers in each car for each time period, and grasp the detailed congestion pattern. For example, the number of passengers passing through the ticket gate at each station is analyzed for each time period, and grasp the detailed congestion pattern. In addition, the number of passengers in each car is analyzed for each time period, and grasp the detailed congestion pattern. Furthermore, the commuter pass information of each user and the station where the user usually gets on and off are analyzed for each time period, and grasp the detailed congestion pattern. Specific classifications and analysis methods for each time period include peak time, off-peak time, and classification by time period. As a result, the analysis unit can grasp the detailed congestion pattern by analyzing the number of passengers passing through the ticket gate at each station and the number of passengers in each car for each time period. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the number of passengers passing through the ticket gate at each station to AI, and cause AI to execute the analysis of the congestion pattern.

[0087] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information is provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points is provided. As a result, the analysis unit adjusts the display method of the analysis result based on the user's emotion, thereby enabling a display that is easy for the user to see. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0088] The analysis unit can incorporate weather data and event information into the analysis in addition to the data of the railway company. The analysis unit incorporates weather data and event information into the analysis in addition to the data of the railway company. For example, the analysis incorporates weather data into the analysis to predict the congestion situation in rainy weather. Also, the analysis incorporates event information into the analysis to predict the congestion situation at the time of a specific event. Furthermore, the weather data and the event information are combined and analyzed to perform a detailed congestion prediction. Specific types and acquisition methods of weather data include temperature, precipitation, wind speed, etc. Also, specific types and acquisition methods of event information include the type, date, time, location, etc. of the event. As a result, the analysis unit can incorporate the weather data and event information into the analysis to perform a more detailed congestion prediction. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input weather data and event information into AI and cause AI to execute congestion prediction.

[0089] The analysis unit can analyze data of other transportation means and provide optimal transfer guidance. The analysis unit analyzes data of other transportation means and provides optimal transfer guidance. For example, the operation status of other transportation means is analyzed and optimal transfer guidance is provided. In addition, the congestion status of other transportation means is analyzed and optimal transfer guidance is provided. Furthermore, the operation schedule of other transportation means is analyzed and optimal transfer guidance is provided. Specific types and acquisition methods of data of other transportation means include bus operation information, taxi position information, and the like. As a result, the analysis unit can analyze data of other transportation means and provide optimal transfer guidance. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data of other transportation means to AI and cause AI to execute transfer guidance.

[0090] The allocation unit can estimate the user's emotion and adjust the allocation algorithm based on the estimated user's emotion. The allocation unit estimates the user's emotion and adjusts the allocation algorithm based on the estimated user's emotion. For example, when the user is relaxed, detailed allocation is performed and the optimal boarding time and car number are suggested. Also, when the user is in a hurry, quick allocation is performed and the shortest route is suggested. Furthermore, when the user is excited, an allocation result with a visually stimulating effect is provided. In this way, the allocation unit can provide a more appropriate allocation result by adjusting the allocation algorithm based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the allocation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the allocation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the allocation algorithm.

[0091] The allocation unit can analyze the user's past riding history and customize the optimal boarding time and car number. The allocation unit analyzes the user's past riding history and customizes the optimal boarding time and car number. For example, the optimal boarding time and car number are proposed based on the boarding time and car number used by the user in the past. Also, a boarding time and car number that avoids congestion are proposed from the user's past riding history. Furthermore, the user's past riding history is analyzed to propose the most efficient boarding time and car number. Specific contents and analysis methods of the past riding history include the boarding date and time, the boarding route, and the frequency. As a result, the allocation unit can customize the optimal boarding time and car number by analyzing the user's past riding history. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's past riding history into AI and cause AI to execute customization of the optimal boarding time and car number.

[0092] The allocation unit can propose an appropriate boarding time based on the user's current schedule. The allocation unit proposes an appropriate boarding time based on the user's current schedule. For example, the allocation unit refers to the user's calendar information and proposes an optimal boarding time. Also, the allocation unit considers the user's current schedule and proposes an optimal boarding time. Furthermore, the allocation unit proposes an optimal boarding time according to the user's plan. Specific contents and reference methods of the current schedule include the type of plan, date and time, location, etc. As a result, the allocation unit can propose an optimal boarding time by considering the user's current schedule. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's current schedule to AI and cause AI to execute the proposal of an optimal boarding time.

[0093] The allocation unit can estimate the user's emotion and adjust the display method of the allocation result based on the estimated user's emotion. The allocation unit estimates the user's emotion and adjusts the display method of the allocation result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. When the user is relaxed, a display method including detailed information is provided. When the user is in a hurry, a display method that focuses on the main points is provided. As a result, the allocation unit adjusts the display method of the allocation result based on the user's emotion, thereby enabling a display that is easy for the user to see. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the allocation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the allocation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the allocation result.

[0094] The allocation unit can allocate an appropriate car number to avoid congestion based on the boarding plans of other users. The allocation unit allocates an appropriate car number to avoid congestion based on the boarding plans of other users. For example, the allocation unit considers the boarding plans of other users and proposes an optimal car number to avoid congestion. In addition, the boarding plans of other users are analyzed and the most efficient car number is proposed. Furthermore, the optimal car number to avoid congestion is allocated based on the boarding plans of other users. The specific contents and acquisition methods of the boarding plans of other users include the boarding date and time, the boarding route, the number of people, etc. As a result, the allocation unit can allocate an optimal car number to avoid congestion by considering the boarding plans of other users. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the boarding plans of other users to AI and cause AI to execute the car number allocation.

[0095] The allocation unit can propose the optimal boarding time and car number in consideration of the user's health condition. The allocation unit proposes the optimal boarding time and car number in consideration of the user's health condition. For example, if the user is tired, the shortest route is proposed. Also, if the user is looking for healthy exercise, a slightly longer route is proposed. Furthermore, if the user is not feeling well, a route including a rest point is proposed. Specific evaluation criteria and acquisition methods for the health condition include heart rate, blood pressure, body temperature, etc. As a result, the allocation unit can propose the optimal boarding time and car number by considering the user's health condition. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI, or may be performed without using AI. For example, the allocation unit can input the user's health condition data into AI and cause AI to execute the proposal of the optimal boarding time and car number.

[0096] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the timing of the notification based on the estimated user's emotion. For example, when the user is relaxed, the notification is performed at a time with plenty of time. Also, when the user is in a hurry, the notification is performed quickly. Furthermore, when the user is stressed, the notification is performed sparingly. In this way, the notification unit can adjust the timing of the notification based on the user's emotion and perform the notification at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of the notification.

[0097] The notification unit can analyze the user's past notification history and select the optimal notification method. The notification unit analyzes the user's past notification history and selects the optimal notification method. For example, the notification unit preferentially suggests notification methods (push notification, email, etc.) that the user has used in the past. In addition, the notification unit suggests the optimal notification method for a specific time period from the user's past notification history. Furthermore, the notification unit analyzes the user's past notification history and selects the most effective notification method. Specific contents and analysis methods of the past notification history include notification date and time, notification contents, reactions, etc. As a result, the notification unit can select the optimal notification method by analyzing the user's past notification history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history into AI and have the AI ​​select the optimal notification method.

[0098] The notification unit can customize the optimal notification content by considering the current situation of the user. The notification unit customizes the optimal notification content by considering the current situation of the user. For example, when the user is on the move, a concise notification content is provided. Also, when the user is at home, a detailed notification content is provided. Furthermore, when the user is in a meeting, the notification is performed sparingly. Specific contents of the current situation and acquisition methods include location information, activity status, surrounding environment, etc. As a result, the notification unit can customize the optimal notification content by considering the current situation of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's current situation data into AI and have the AI ​​execute customization of the notification content.

[0099] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. For example, if the user is nervous, important notifications are given priority. Also, if the user is relaxed, normal notifications are given priority. Furthermore, if the user is in a hurry, emergency notifications are given priority. In this way, the notification unit can give priority to important notifications by determining the priority of notifications based on the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of notifications.

[0100] The notification unit can select the optimal notification method by considering the device information of the user. The notification unit selects the optimal notification method by considering the device information of the user. For example, if the user is using a smartphone, push notification is given priority. Also, if the user is using a tablet, email notification is given priority. Furthermore, if the user is using a smartwatch, vibration notification is given priority. Specific types and acquisition methods of device information include the smartphone model, OS version, connection status, and the like. As a result, the notification unit can select the optimal notification method by considering the device information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the device information of the user to AI and cause the AI ​​to select the optimal notification method.

[0101] The notification unit can propose the optimal notification timing in consideration of the user's schedule. The notification unit proposes the optimal notification timing in consideration of the user's schedule. For example, the notification unit proposes the optimal notification timing by referring to the user's calendar information. Also, the notification unit proposes the optimal notification timing in consideration of the user's current schedule. Furthermore, the notification unit proposes the optimal notification timing in accordance with the user's schedule. Specific contents of the schedule and the reference method include the type of schedule, date and time, location, etc. As a result, the notification unit can propose the optimal notification timing by considering the user's schedule. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's schedule into AI and have the AI ​​execute the proposal of the optimal notification timing.

[0102] The collection unit can estimate the user's emotion and adjust the frequency of data collection based on the estimated user's emotion. The collection unit estimates the user's emotion and adjusts the frequency of data collection based on the estimated user's emotion. For example, when the user is relaxed, the normal data collection frequency is maintained. Also, when the user is in a hurry, the data collection frequency is reduced. Furthermore, when the user is stressed, the data collection frequency is reduced. As a result, the collection unit adjusts the frequency of data collection based on the user's emotion, thereby enabling more appropriate data collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 the user's emotion data to the generation AI and cause the generation AI to adjust the frequency of data collection.

[0103] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit preferentially proposes a data collection method (GPS, Wi-Fi, etc.) that the user has used in the past. In addition, the collection unit proposes the optimal collection method for a specific time period from the user's past data collection history. Furthermore, the collection unit analyzes the user's past data collection history and selects the most effective collection method. Specific contents and analysis methods of the past data collection history include collection date and time, collection contents, frequency, etc. As a result, the collection unit can select the optimal collection method by analyzing the user's past data collection history. Part 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 the user's past data collection history to AI and cause AI to select the optimal collection method.

[0104] The collection unit can propose optimal data collection timing in consideration of the user's current situation. The collection unit proposes optimal data collection timing in consideration of the user's current situation. For example, when the user is on the move, data is collected in real time. Also, when the user is at home, data is collected periodically. Furthermore, when the user is in a meeting, data collection is performed sparingly. Specific contents and acquisition methods of the current situation include location information, activity status, surrounding environment, etc. As a result, the collection unit can propose optimal data collection timing by considering the user's current situation. Part 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 the user's current situation data to AI and have the AI ​​execute the proposal of data collection timing.

[0105] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, if the user is nervous, important data is collected preferentially. Furthermore, if the user is relaxed, normal data is collected. Furthermore, if the user is in a hurry, urgent data is collected preferentially. In this way, the collection unit can prioritize important data by determining the priority of data to be collected based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the data.

[0106] The collection unit can analyze the social media activity of the user and collect related data. The collection unit analyzes the social media activity of the user and collects related data. For example, the collection unit analyzes the social media activity of the user and collects related location information. In addition, the collection unit analyzes the social media activity of the user and collects related event information. In addition, the collection unit analyzes the social media activity of the user and collects related interest data. Specific contents and analysis methods of social media activities include posted contents, number of likes, comments, etc. As a result, the collection unit can collect related data by analyzing the social media activity of the user. Part 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 the social media activity data of the user to AI and cause AI to collect related data.

[0107] The collection unit can propose an optimal data collection method in consideration of the user's health condition. The collection unit proposes an optimal data collection method in consideration of the user's health condition. For example, if the user is tired, the data collection frequency is reduced. Also, if the user is seeking healthy exercise, the data collection frequency is increased. Furthermore, if the user is in poor health, data collection is performed sparingly. Specific evaluation criteria and acquisition methods for the health condition include heart rate, blood pressure, body temperature, etc. As a result, the collection unit can propose an optimal data collection method by considering the user's health condition. Part 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 the user's health condition data into AI and have the AI ​​execute the proposal of the optimal data collection method.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The analysis unit can analyze the user's past riding history and suggest the optimal boarding time and car number to avoid crowding. For example, the analysis unit can analyze data on boarding times and car numbers that the user has chosen in the past to avoid crowding, and suggest similar patterns. In addition, new suggestions for avoiding crowding can be made based on data on boarding times and car numbers used by the user in the past. Furthermore, the analysis unit can analyze the user's past riding history and suggest the optimal boarding time and car number to avoid crowding during a specific time period or day of the week. In this way, the analysis unit can provide a more comfortable riding experience by utilizing the user's past riding history.

[0110] The notification unit can suggest the optimal boarding time and car number in real time based on the user's current location information. For example, when the user arrives at a station, the current location information is acquired and the optimal boarding time and car number is suggested. Also, when the user is moving, the location information can be updated in real time and the optimal boarding time and car number can be suggested. Furthermore, when the user is in a specific area, the optimal boarding time and car number can be suggested taking into account the congestion situation in that area. This allows the notification unit to make more appropriate suggestions by utilizing the user's current location information.

[0111] The allocation unit can suggest the optimal boarding time and car number by taking into account the user's health condition. For example, if the user is tired, the shortest route is suggested. Also, if the user is looking for healthy exercise, a slightly longer route is suggested. Furthermore, if the user is feeling unwell, a route including rest points is suggested. Specific evaluation criteria and acquisition methods for the health condition include heart rate, blood pressure, body temperature, etc. This allows the allocation unit to suggest the optimal boarding time and car number by taking into account the user's health condition.

[0112] The analysis unit can incorporate weather data into the analysis to predict congestion. For example, it can predict congestion on rainy days and suggest optimal boarding times and car numbers to users. It can also predict congestion on hot and cold days and make suggestions based on that. Furthermore, it can combine weather data with other data for analysis to make more detailed congestion predictions. This allows the analysis unit to make more accurate congestion predictions by utilizing weather data.

[0113] The notification unit can select the optimal notification method by taking into account the device information of the user. For example, if the user is using a smartphone, push notification is given priority. Also, if the user is using a tablet, email notification is given priority. Furthermore, if the user is using a smartwatch, vibration notification is given priority. Specific types and acquisition methods of device information include the smartphone model, OS version, connection status, etc. This allows the notification unit to select the optimal notification method by taking into account the device information of the user.

[0114] The reception unit can estimate the user's emotions and adjust the input method for the departure station, arrival station, and desired arrival time based on the estimated user's emotions. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. When the user is relaxed, detailed input options are provided and a customizable input method is suggested. Furthermore, when the user is in a hurry, voice input is prioritized to enable the user to quickly input the departure station, arrival station, and desired arrival time. In this way, the reception unit can provide an easy-to-use interface for the user by adjusting the input method based on the user's emotions.

[0115] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis is performed and an optimal route is proposed. If the user is in a hurry, a quick analysis is performed and the shortest route is proposed. Furthermore, if the user is excited, an analysis result with a visually stimulating effect is provided. This allows the analysis unit to provide a more appropriate analysis result by adjusting the analysis algorithm based on the user's emotions.

[0116] The allocation unit can estimate the user's emotions and adjust the allocation algorithm based on the estimated user's emotions. For example, when the user is relaxed, detailed allocation is performed and the optimal boarding time and car number are suggested. When the user is in a hurry, quick allocation is performed and the shortest route is suggested. Furthermore, when the user is excited, an allocation result with a visually stimulating effect is provided. This allows the allocation unit to provide a more appropriate allocation result by adjusting the allocation algorithm based on the user's emotions.

[0117] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, when the user is relaxed, the notification is made at a time with plenty of time to spare. When the user is in a hurry, the notification is made quickly. Furthermore, when the user is feeling stressed, the notification is made less frequently. In this way, the notification unit can adjust the timing of the notification based on the user's emotion and make the notification at a more appropriate time.

[0118] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, if the user is relaxed, the normal data collection frequency is maintained. If the user is in a hurry, the data collection frequency is reduced. Furthermore, if the user is stressed, the data collection frequency is reduced. In this way, the collection unit can adjust the data collection frequency based on the user's emotions, thereby enabling more appropriate data collection.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit accepts the user's input of the departure station, arrival station, and desired arrival time. For example, the user inputs, "I want to travel from Shinjuku Station to Tokyo Station, arriving at 9:00 a.m." This information is input into the AI. Step 2: The analysis unit analyzes the railway company's data based on the information received by the reception unit. Specifically, data such as the number of passengers passing through the ticket gates at each station, each user's commuter pass information and usual stations where passengers get on and off, and the number of passengers detected by sensors at the entrances and exits of each car are collected and analyzed by AI. For example, AI analyzes the number of passengers passing through the ticket gates at Shinjuku Station and the number of passengers in each car to grasp the congestion situation. Step 3: The allocation unit allocates the optimal boarding time and car number based on the analysis results obtained by the analysis unit. For example, the AI ​​may determine that "if you depart Shinjuku Station at 8:30 a.m. and board car number 3, you will arrive at Tokyo Station at 9 a.m. and avoid crowding." Step 4: The notification unit notifies the user of the information assigned by the allocation unit. For example, the user's smartphone receives a notification saying, "Depart Shinjuku Station at 8:30 a.m., board car No. 3."

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0122] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the reception unit, analysis unit, allocation unit, and notification 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 reception unit is realized by the control unit 46A of the smart device 14, and receives input of the user's departure station, arrival station, and desired arrival time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data of the railway company. The allocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and allocates the optimal boarding time and car number. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user. The collection unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and collects the user's commuter pass information and data on stations where the user usually gets on and off. The collection unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and collects the number of passengers passing through the ticket gates at each station and the number of passengers in each car. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] Fig. 4 shows an example of 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.

[0133] 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.

[0134] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, analysis unit, allocation unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and receives input of the user's departure station, arrival station, and desired arrival time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data of a railway company. The allocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and allocates the optimal boarding time and car number. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and collects the user's commuter pass information and data on stations where the user usually gets on and off. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and collects the number of people passing through the ticket gates at each station and the number of passengers in each car. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, analysis unit, allocation unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives input of the user's departure station, arrival station, and desired arrival time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data of the railway company. The allocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and allocates the optimal boarding time and car number. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user. The collection unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and collects the user's commuter pass information and data on stations where the user usually gets on and off. The collection unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and collects the number of people passing through the ticket gates at each station and the number of passengers in each car. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0160] 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.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] 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 performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0165] 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.

[0166] 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.

[0167] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0173] Each of the multiple elements including the reception unit, analysis unit, allocation unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and receives input of the user's departure station, arrival station, and desired arrival time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data of the railway company. The allocation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and allocates the optimal boarding time and car number. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user. The collection unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and collects the user's commuter pass information and data on stations where the user usually gets on and off. The collection unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and collects the number of passengers passing through the ticket gates at each station and the number of passengers in each car. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] 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 Universal Serial Bus (USB) 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.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0187] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A reception unit that receives input of a departure station, an arrival station, and a desired arrival time of a user; an analysis unit that analyzes data of the railway company based on the information received by the reception unit; an allocation unit that allocates an optimal boarding time and car number based on the analysis result obtained by the analysis unit; a notification unit that notifies a user of the information assigned by the assignment unit; A system characterized by: (Appendix 2) Equipped with a collection unit that collects user commuter pass information or data on stations where users usually get on and off 2. The system of claim 1, (Appendix 3) Equipped with a collection unit that collects the number of passengers passing through the ticket gates at each station and the number of passengers in each car 2. The system of claim 1, (Appendix 4) The notification unit is Real-time notifications 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze the number of passengers passing through ticket gates at each station and the number of passengers in each car to grasp congestion levels 2. The system of claim 1, (Appendix 6) The allocation unit: Assign the appropriate boarding time and car number based on the user's desired arrival time 2. The system of claim 1, (Appendix 7) The reception unit is The system estimates the user's emotions and adjusts the input method for the departure station, arrival station, and desired arrival time based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is Simplify input by automatically acquiring the user's current location when entering the departure station, arrival station, and desired arrival time. 2. The system of claim 1, (Appendix 10) The reception unit is Estimating user emotions and adjusting the design of the input interface based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When the user inputs the departure station, arrival station, and desired arrival time, the system automatically suggests possible locations based on the user's past travel history. 2. The system of claim 1, (Appendix 12) The reception unit is When entering the departure station, arrival station, and desired arrival time, the system refers to the user's calendar information and makes suggestions based on the user's schedule. 2. The system of claim 1, (Appendix 13) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is Analyzing railway company data in real time to predict congestion 2. The system of claim 1, (Appendix 15) The analysis unit is Analyze the number of passengers passing through ticket gates at each station and the number of passengers in each car by time period to understand detailed congestion patterns 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is In addition to railway company data, weather data and event information are incorporated into the analysis 2. The system of claim 1, (Appendix 18) The analysis unit is Analyzing data from other transportation providers to provide optimal transfer information 2. The system of claim 1, (Appendix 19) The allocation unit: Inferring user sentiment and adjusting the allocation algorithm based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The allocation unit: Analyze the user's past riding history to customize the optimal boarding time and car number 2. The system of claim 1, (Appendix 21) The allocation unit: Suggest suitable ride times based on the user's current schedule 2. The system of claim 1, (Appendix 22) The allocation unit: Inferring user sentiment and adjusting how allocation results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The allocation unit: Based on other users' boarding plans, assign appropriate cars to avoid overcrowding 2. The system of claim 1, (Appendix 24) The allocation unit: Taking into account the user's health condition, the system suggests the optimal boarding time and car number 2. The system of claim 1, (Appendix 25) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The notification unit is Analyze the user's past notification history and select the optimal notification method 2. The system of claim 1, (Appendix 27) The notification unit is Customize the most appropriate notification content based on the user's current situation 2. The system of claim 1, (Appendix 28) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The notification unit is Select the optimal notification method based on the user's device information 2. The system of claim 1, (Appendix 30) The notification unit is Suggest optimal notification timing based on the user's schedule 2. The system of claim 1, (Appendix 31) The collecting unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 32) The collecting unit includes: Analyze the user's past data collection history and select the optimal collection method 2. The system of claim 1, (Appendix 33) The collecting unit includes: Suggest optimal data collection timing based on the user's current situation 2. The system of claim 1, (Appendix 34) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 35) The collecting unit includes: Analyze your social media activity and collect related data 2. The system of claim 1, (Appendix 36) The collecting unit includes: Propose the best data collection method based on the user's health condition 2. The system of claim 1, [Explanation of symbols]

[0193] 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 reception unit that receives input of a departure station, an arrival station, and a desired arrival time of a user; an analysis unit that analyzes data of the railway company based on the information received by the reception unit; an allocation unit that allocates an optimal boarding time and car number based on the analysis result obtained by the analysis unit; a notification unit that notifies a user of the information assigned by the assignment unit; A system characterized by:

2. Equipped with a collection unit that collects user commuter pass information or data on stations where users usually get on and off 2. The system of claim 1 .

3. Equipped with a collection unit that collects the number of passengers passing through the ticket gates at each station and the number of passengers in each car 2. The system of claim 1 .

4. The notification unit is Real-time notifications 2. The system of claim 1 .

5. The analysis unit is Analyze the number of passengers passing through ticket gates at each station and the number of passengers in each car to grasp congestion levels 2. The system of claim 1 .

6. The allocation unit, Assign the appropriate boarding time and car number based on the user's desired arrival time 2. The system of claim 1 .

7. The reception unit is The system estimates the user's emotions and adjusts the input method for the departure station, arrival station, and desired arrival time based on the estimated user emotions.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1 .

9. The reception unit is Simplify input by automatically acquiring the user's current location when entering the departure station, arrival station, and desired arrival time.

2. The system of claim 1 .

10. The reception unit is Estimating user emotions and adjusting the design of the input interface based on the estimated user emotions 2. The system of claim 1 .

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