System

Abstract

To provide a system that provides information for the avoidance of train congestion and the comfortable use of trains.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis 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 the information received by the reception unit, ticket gate data, and sensor data to propose a boarding time and train car. The notification unit notifies the user of the information proposed by the analysis 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 does not provide sufficient information to help people avoid crowded trains and use them comfortably, so there is room for improvement.

[0005] The system according to the embodiment aims to provide information for avoiding crowded trains and using them comfortably. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a notification unit. The reception unit receives input of a departure station, an arrival station, and a desired arrival time from a user. The analysis unit analyzes the information received by the reception unit, ticket gate data, and sensor data, and suggests a boarding time and a car number. The notification unit notifies the user of the information suggested by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can provide information for avoiding crowded trains and using them 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, aperture, and 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 that allows a user to avoid crowds and use trains comfortably. In this system, a user registers a departure station, an arrival station, and a desired arrival time in a messenger app, and AI technology analyzes ticket gate data held by the railway company and information about the user, and proposes the optimal boarding time and car number taking into consideration the desired arrival time. For example, if a user wants to arrive at 8 o'clock, the AI ​​analyzes the congestion situation during that time period and proposes a boarding time and car number of a train that is not crowded. This allows the user to avoid crowds and use trains comfortably. In addition, since information is provided in real time through the messenger app, the user can always obtain the latest information. This allows the congestion avoidance system to allow a user to avoid crowds and use trains comfortably.

[0029] The congestion avoidance system according to the embodiment includes a reception unit, an analysis unit, and a notification unit. The reception unit receives input of the user's departure station, arrival station, and desired arrival time. When the user inputs the departure station, arrival station, and desired arrival time, the reception unit can receive the information through, for example, a messenger app. The analysis unit analyzes the information received by the reception unit, ticket gate data, and sensor data, and proposes the optimal boarding time and car number. The analysis unit analyzes the congestion situation, taking into account the user's desired arrival time, for example, using AI technology. The analysis unit proposes a boarding time and car number of a train that is not crowded, based on, for example, the ticket gate data and the sensor data. The notification unit notifies the user of the information proposed by the analysis unit. The notification unit provides information in real time, for example, through a messenger app. This allows the user to always obtain the latest information. This allows the congestion avoidance system according to the embodiment to allow the user to avoid congestion and use the train comfortably.

[0030] The reception unit accepts input of the user's departure station, arrival station, and desired arrival time. When the user inputs the departure station, arrival station, and desired arrival time, the reception unit can accept the information through, for example, a messenger app. Specifically, the user uses a smartphone or a computer to access a messenger app and inputs the departure station, arrival station, and desired arrival time. The reception unit receives this information in real time and stores it in a database. Furthermore, the reception unit checks the user's input content and provides feedback to correct input errors or incomplete information as necessary. For example, if the input station name does not exist or the desired arrival time is a time in the past, a message is sent to the user prompting the user to make appropriate corrections. The reception unit also has a function of referring to the user's past usage history and automatically suggesting frequently used stations and time periods. This allows the user to input information quickly and accurately, improving the efficiency of the entire system. Furthermore, the reception unit can accommodate a variety of users by providing multilingual support and voice input functions. For example, multilingual support is introduced to enable foreign users to input information in their native language, and voice recognition technology is introduced to enable visually impaired users to provide information using voice input. This enables the reception unit to flexibly respond to the diverse needs of users, thereby improving convenience for users.

[0031] The analysis unit analyzes the information received by the reception unit, as well as the ticket gate data and the sensor data, and proposes the optimal boarding time and car number. The analysis unit, for example, uses AI technology to analyze the congestion situation, taking into account the user's desired arrival time. Specifically, the AI ​​learns past ticket gate data and sensor data, and predicts the congestion pattern for a specific time period and day of the week. For example, it can predict the congestion situation during weekday morning rush hours and on days when a specific event is held. Furthermore, based on the ticket gate data and sensor data collected in real time, it grasps the current congestion situation and proposes the optimal boarding time and car number to the user. For example, the analysis unit analyzes the number of passengers boarding and alighting at each station from the ticket gate data, and evaluates the congestion level of each car from the sensor data. This makes it possible to identify the time period and car number at which the user can board comfortably. Furthermore, the analysis unit calculates the optimal boarding time, taking into account the user's desired arrival time. For example, it proposes a boarding time with a margin so that the user is not late for the desired arrival time. In addition, the analysis unit presents multiple boarding options and allows the user to select from them. For example, it provides an option to board earlier to avoid congestion, or to prioritize arrival time in exchange for allowing some congestion. This enables the analysis unit to make flexible suggestions based on the user's needs and provide a comfortable riding experience.

[0032] The notification unit notifies the user of the information proposed by the analysis unit. The notification unit provides information in real time, for example, through a messenger app. Specifically, the analysis unit notifies the user of the optimal boarding time and car number information proposed by the analysis unit to the user's smartphone or computer. The notification is provided in the form of a text message, push notification, email, or the like, so that the user can check it immediately. Furthermore, the notification unit notifies at an appropriate time, taking into consideration the user's current location and movement status. For example, when the user approaches the departure station, the notification unit notifies the user of the boarding time and car number information again to encourage the user to prepare for boarding. In addition, the notification unit accepts feedback from the user and continuously improves the accuracy and effectiveness of the proposal content. For example, the notification unit evaluates whether the user is satisfied with the proposed boarding time and car number, and adjusts the analysis algorithm based on the feedback. Furthermore, the notification unit also has a function for responding to emergencies and abnormal situations. For example, if a train is delayed or suspended, the notification unit notifies the user of an alternative route and the next optimal boarding time. This allows the user to always obtain the latest information, avoiding confusion and allowing smooth travel. The notification unit provides prompt and accurate information to maximize user convenience and support a comfortable riding experience.

[0033] The congestion avoidance system includes a collection unit that collects ticket gate data. The collection unit collects the ticket gate data. The ticket gate data includes, for example, the time when passing through the ticket gate, the user's ID, and the like, but is not limited to such examples. The collection unit can, for example, record the time when passing through the ticket gate and obtain the user's ID. This improves the accuracy of the analysis by collecting the ticket gate data. A 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 time when passing through the ticket gate and the user's ID into AI and have the AI ​​collect the data.

[0034] The congestion avoidance system includes a collection unit that collects sensor data. The collection unit collects the sensor data. The sensor data includes, for example, the number of passengers, temperature, humidity, and the like, but is not limited to such examples. The collection unit can, for example, count the number of passengers and measure temperature and humidity. Thereby, by collecting the sensor data, the accuracy of the analysis is improved. A 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 data on the number of passengers, temperature, and humidity into AI and cause AI to collect the data.

[0035] The analysis unit can suggest a boarding time and a car number based on the user's desired arrival time. The analysis unit suggests the optimal boarding time and car number taking into account the user's desired arrival time. For example, if the user wants to arrive at 8 o'clock, the analysis unit analyzes the congestion situation during that time period and suggests the boarding time and car number of a train that is not crowded. This allows for more appropriate suggestions by taking into account the user's desired arrival time. Some 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 user's desired arrival time and congestion situation into AI and have the AI ​​execute the suggestion of the optimal boarding time and car number.

[0036] The notification unit can notify the user of the proposed information in real time. The notification unit notifies the user of the proposed information in real time. For example, the notification unit provides the information in real time through a messenger app. This allows the user to obtain the latest information by notifying the information in real time. Some 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 proposed information to an AI and cause the AI ​​to execute the real-time notification.

[0037] The collection unit can collect sensor data on the number of passengers or the number of cars in real time. The collection unit collects sensor data on the number of passengers and the number of cars in real time. For example, the collection unit can count the number of passengers in real time and collect sensor data on the number of cars in real time. This improves the accuracy of analysis by collecting data in real time. 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 sensor data on the number of passengers and the number of cars to AI and cause AI to collect data in real time.

[0038] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit analyzes the user's past input history and suggests 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 reception unit can preferentially suggest the input method (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict and suggest the departure station, arrival station, and desired arrival time to be used in a specific time period from the user's past input history. As a result, the efficiency of input is improved by suggesting the optimal input method based on the past input history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, 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 suggestion of the optimal input method.

[0039] The reception unit can automatically acquire the user's current location information when inputting the departure station, arrival station, and desired arrival time to simplify the input. The reception unit automatically acquires the user's current location information when inputting the departure station, arrival station, and desired arrival time to simplify the input. For example, when the user opens the app, the reception unit automatically acquires the current location and sets it as the departure station. In addition, when the user inputs the arrival station, the reception unit can also suggest the optimal candidate location in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the departure station. This saves the effort of inputting by automatically acquiring the 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.

[0040] The reception unit can automatically suggest candidate locations by referring to the user's past movement history when inputting the departure station, arrival station, and desired arrival time. The reception unit automatically suggests candidate locations by referring to the user's past movement history when inputting the departure station, arrival station, and desired arrival time. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. In addition, it is also possible to predict places that the user will visit on a specific day of the week or time period and suggest them as candidate locations. Furthermore, it is also possible to analyze the user's past movement patterns and suggest optimal candidate locations. As a result, by referring to the past movement history, the efficiency of input is improved. 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.

[0041] 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, it is also possible to propose a place related to a specific event as a candidate location from the user's calendar information. Furthermore, it is also possible to propose an optimal route according to the schedule based on the user's calendar information. As a result, it is possible to make an optimal proposal based on the schedule by referring to the calendar information. A 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 calendar information into AI and cause the AI ​​to execute a proposal based on the schedule.

[0042] The analysis unit can analyze the user's past riding history and suggest the optimal boarding time and car number. The analysis unit analyzes the user's past riding history and suggests the optimal boarding time and car number. For example, the analysis unit suggests the optimal boarding time and car number based on the route the user has used in the past. In addition, the analysis unit can suggest a boarding time and car number that avoids congestion from the user's past riding history. Furthermore, the analysis unit can analyze the user's past riding history and suggest the most efficient boarding time and car number. As a result, the analysis unit makes optimal suggestions based on the past riding history, improving user convenience. Some 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 user's past riding history into AI and cause AI to execute the suggestion of the optimal boarding time and car number.

[0043] The analysis unit can propose an optimal boarding time in consideration of the user's current schedule. The analysis unit proposes an optimal boarding time in consideration of the user's current schedule. For example, the analysis unit proposes an optimal boarding time that matches the schedule based on the user's calendar information. In addition, a boarding time with ample time can be proposed based on the user's schedule. Furthermore, a boarding time that allows the user to arrive in the shortest time can be proposed in consideration of the user's schedule. This makes it possible to make more appropriate proposals by considering the current schedule. Some 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 user's schedule information into AI and cause the AI ​​to execute the proposal of the optimal boarding time.

[0044] The analysis unit can propose the optimal boarding time and car number taking into consideration the geographical location information of the user. The analysis unit proposes the optimal boarding time and car number taking into consideration the geographical location information of the user. For example, the analysis unit proposes the optimal boarding time and car number based on the station closest to the user's current location. In addition, the analysis unit can propose a boarding time and car number that avoids congestion based on the geographical location information of the user. Furthermore, the analysis unit can propose the most efficient boarding time and car number taking into consideration the geographical location information of the user. As a result, more appropriate proposals can be made by taking the geographical location information into consideration. 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 geographical location information of the user to AI and cause the AI ​​to execute the proposal of the optimal boarding time and car number.

[0045] The analysis unit can analyze the user's social media activity and suggest the relevant boarding time and car number. The analysis unit can analyze the user's social media activity and suggest the relevant boarding time and car number. For example, the analysis unit can suggest the optimal boarding time and car number based on the location information shared by the user on social media. In addition, the analysis unit can suggest the boarding time and car number that avoids crowding from the user's social media activity. Furthermore, the analysis unit can analyze the user's social media activity and suggest the most efficient boarding time and car number. As a result, the analysis unit can improve the user's convenience by making optimal suggestions based on social media activity. Some 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 user's social media activity data into AI and cause AI to execute the suggestion of the optimal boarding time and car number.

[0046] The notification unit can analyze the user's past notification history and propose the optimal notification method. The notification unit analyzes the user's past notification history and proposes the optimal notification method. For example, the notification unit preferentially proposes notification methods (push notification, email, etc.) that the user has preferred in the past. In addition, the notification unit can perform notifications at the optimal timing based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and propose the most effective notification method. As a result, the efficiency of notifications is improved by proposing the optimal notification method based on the past notification history. 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 input the user's past notification history into AI and have the AI ​​execute the proposal of the optimal notification method.

[0047] The notification unit can select the optimal notification means in consideration of the user's current situation. The notification unit selects the optimal notification means in consideration of the user's current situation. For example, when the user is on the move, the notification unit prioritizes push notification for notification. Also, when the user is doing desk work, the notification unit can also prioritize email notification for notification. Furthermore, when the user is in a meeting, the notification unit can also prioritize vibration notification for notification. This allows for more appropriate notification by considering the current situation. 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 cause AI to select the optimal notification means.

[0048] The notification unit can propose an optimal notification method in consideration of the geographical location information of the user. The notification unit proposes an optimal notification method in consideration of the geographical location information of the user. For example, the optimal notification method is proposed based on the station closest to the user's current location. In addition, a notification method that avoids congestion can be proposed based on the geographical location information of the user. Furthermore, the most efficient notification method can be proposed in consideration of the geographical location information of the user. As a result, more appropriate notification is possible by taking the geographical location information into consideration. A part 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 input the geographical location information of the user to AI and have the AI ​​execute the proposal of the optimal notification method.

[0049] The notification unit can analyze the user's social media activity and propose related notification content. The notification unit can analyze the user's social media activity and propose related notification content. For example, the notification unit can propose optimal notification content based on location information shared by the user on social media. In addition, notification content that avoids congestion can be proposed based on the user's social media activity. Furthermore, the notification unit can analyze the user's social media activity and propose the most efficient notification content. As a result, the user's convenience is improved by proposing optimal notification content based on social media activity. A part 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 input the user's social media activity data into AI and have the AI ​​execute the proposal of optimal notification content.

[0050] The collection unit can analyze the user's past data collection history and propose the optimal collection method. The collection unit analyzes the user's past data collection history and proposes the optimal collection method. For example, the collection unit preferentially collects data types that the user has collected in the past. In addition, data can be collected at the optimal timing from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and propose the most effective collection method. As a result, the efficiency of data collection is improved by proposing the optimal collection method based on the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI and have the AI ​​execute the proposal of the optimal collection method.

[0051] The collection unit can select the optimal data collection means in consideration of the user's current situation. The collection unit selects the optimal data collection means in consideration of the user's current situation. For example, when the user is on the move, the collection unit prioritizes mobile data collection. Also, when the user is doing desk work, the collection unit can also collect data using Wi-Fi. Furthermore, when the user is in a meeting, the collection unit can also prioritize vibration notification for data collection. This enables more appropriate data collection by considering the current situation. Part 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 user's current situation data into AI and cause AI to select the optimal data collection means.

[0052] The collection unit can propose an optimal data collection method in consideration of the geographical location information of the user. The collection unit proposes an optimal data collection method in consideration of the geographical location information of the user. For example, the optimal data collection method is proposed based on the station closest to the user's current location. In addition, a data collection method that avoids congestion can be proposed based on the geographical location information of the user. Furthermore, the most efficient data collection method can be proposed in consideration of the geographical location information of the user. As a result, more appropriate data collection is possible by taking the geographical location information into consideration. A 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 geographical location information of the user to AI and have the AI ​​execute the proposal of the optimal data collection method.

[0053] The collection unit can analyze the user's social media activity and suggest a related data collection method. The collection unit analyzes the user's social media activity and suggests a related data collection method. For example, the collection unit suggests an optimal data collection method based on location information shared by the user on social media. In addition, a data collection method that avoids congestion can be suggested from the user's social media activity. Furthermore, the collection unit can analyze the user's social media activity and suggest the most efficient data collection method. As a result, the efficiency of data collection is improved by suggesting the optimal data collection method based on the social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​execute the suggestion of the optimal data collection method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can also suggest the optimal boarding time and car number taking into account the user's health data. For example, it can monitor the user's heart rate and stress level, and if the user's health condition is not good, it can suggest a less crowded car number or a time when it is easier to secure a seat. Also, if the user is not getting enough exercise, it can suggest a boarding time and car number that will require a slightly longer walking distance. Furthermore, it is also possible to select a car number with a comfortable temperature and humidity based on the user's health data. This makes it possible to provide the optimal boarding environment according to the user's health condition.

[0056] The collection unit can also suggest the optimal boarding time and car number based on the user's purchasing history. For example, if the user has a history of frequent purchases at a particular store, the collection unit can suggest a boarding time from a station near that store. Also, if the user tends to make purchases during a particular time period, the collection unit can suggest a boarding time that matches that time period. Furthermore, it is also possible to suggest the optimal car number to avoid congestion based on the user's purchasing history. This makes it possible to provide the optimal boarding environment according to the user's purchasing activities.

[0057] The collection unit can also suggest the optimal boarding time and car number based on the user's hobbies and interests. For example, if the user is interested in a particular event or watching a sporting event, the collection unit can suggest a boarding time that matches the time when the event is being held. Also, if the user is interested in a particular tourist spot, the collection unit can suggest a car number that has good access to the tourist spot. Furthermore, it is also possible to select a car number that provides a comfortable boarding environment based on the user's hobbies and interests. This makes it possible to provide the user with the optimal boarding environment according to their hobbies and interests.

[0058] The analysis unit can also suggest the optimal boarding time and car number based on the user's past travel history. For example, the analysis unit can suggest the optimal boarding time and car number based on the places the user has visited in the past and the means of transportation used by the user. The analysis unit can also suggest the optimal car number to avoid congestion, taking into account the routes and time periods that the user has preferred in the past. Furthermore, it is also possible to select a car number that provides a comfortable riding environment from the user's past travel history. This makes it possible to provide the optimal riding environment according to the user's travel history.

[0059] The notification unit can also suggest the optimal boarding time and car number taking into account the user's weather information. For example, when it is raining, a car number that is a short travel distance from the station can be suggested. Also, on a hot day, a car number that is air-conditioned can be suggested. Furthermore, if the user is sensitive to the weather, it is also possible to select a car number that provides a comfortable boarding environment based on the weather information. In this way, it is possible to provide the optimal boarding environment taking into account the weather information.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit receives input of the user's departure station, arrival station, and desired arrival time. When the user inputs the departure station, arrival station, and desired arrival time, the reception unit can receive the information through, for example, a messenger app. Step 2: The analysis unit analyzes the information received by the reception unit, as well as the ticket gate data and sensor data, and proposes the optimal boarding time and car number. The analysis unit, for example, uses AI technology to analyze the congestion situation, taking into account the user's desired arrival time. The analysis unit proposes a boarding time and car number for a train that is not crowded, based on the ticket gate data and sensor data, for example. Step 3: The notification unit notifies the user of the information proposed by the analysis unit. The notification unit provides the information in real time, for example, through a messenger app. This allows the user to always obtain the latest information.

[0062] (Example 2) The congestion avoidance system according to the embodiment of the present invention is a system that allows a user to avoid crowds and use trains comfortably. In this system, a user registers a departure station, an arrival station, and a desired arrival time in a messenger app, and AI technology analyzes ticket gate data held by the railway company and information about the user, and proposes the optimal boarding time and car number taking into consideration the desired arrival time. For example, if a user wants to arrive at 8 o'clock, the AI ​​analyzes the congestion situation during that time period and proposes a boarding time and car number of a train that is not crowded. This allows the user to avoid crowds and use trains comfortably. In addition, since information is provided in real time through the messenger app, the user can always obtain the latest information. This allows the congestion avoidance system to allow a user to avoid crowds and use trains comfortably.

[0063] The congestion avoidance system according to the embodiment includes a reception unit, an analysis unit, and a notification unit. The reception unit receives input of the user's departure station, arrival station, and desired arrival time. When the user inputs the departure station, arrival station, and desired arrival time, the reception unit can receive the information through, for example, a messenger app. The analysis unit analyzes the information received by the reception unit, ticket gate data, and sensor data, and proposes the optimal boarding time and car number. The analysis unit analyzes the congestion situation, taking into account the user's desired arrival time, for example, using AI technology. The analysis unit proposes a boarding time and car number of a train that is not crowded, based on, for example, the ticket gate data and the sensor data. The notification unit notifies the user of the information proposed by the analysis unit. The notification unit provides information in real time, for example, through a messenger app. This allows the user to always obtain the latest information. This allows the congestion avoidance system according to the embodiment to allow the user to avoid congestion and use the train comfortably.

[0064] The reception unit accepts input of the user's departure station, arrival station, and desired arrival time. When the user inputs the departure station, arrival station, and desired arrival time, the reception unit can accept the information through, for example, a messenger app. Specifically, the user uses a smartphone or a computer to access a messenger app and inputs the departure station, arrival station, and desired arrival time. The reception unit receives this information in real time and stores it in a database. Furthermore, the reception unit checks the user's input content and provides feedback to correct input errors or incomplete information as necessary. For example, if the input station name does not exist or the desired arrival time is a time in the past, a message is sent to the user prompting the user to make appropriate corrections. The reception unit also has a function of referring to the user's past usage history and automatically suggesting frequently used stations and time periods. This allows the user to input information quickly and accurately, improving the efficiency of the entire system. Furthermore, the reception unit can accommodate a variety of users by providing multilingual support and voice input functions. For example, multilingual support is introduced to enable foreign users to input information in their native language, and voice recognition technology is introduced to enable visually impaired users to provide information using voice input. This enables the reception unit to flexibly respond to the diverse needs of users, thereby improving convenience for users.

[0065] The analysis unit analyzes the information received by the reception unit, as well as the ticket gate data and sensor data, and proposes the optimal boarding time and car number. The analysis unit, for example, uses AI technology to analyze the congestion situation, taking into account the user's desired arrival time. Specifically, the AI ​​learns past ticket gate data and sensor data, and predicts the congestion pattern for a specific time period and day of the week. For example, it can predict the congestion situation during weekday morning rush hours and on days when a specific event is held. Furthermore, based on the ticket gate data and sensor data collected in real time, it grasps the current congestion situation and proposes the optimal boarding time and car number to the user. For example, the analysis unit analyzes the number of passengers boarding and alighting at each station from the ticket gate data, and evaluates the congestion level of each car from the sensor data. This makes it possible to identify the time period and car number at which the user can board comfortably. Furthermore, the analysis unit calculates the optimal boarding time, taking into account the user's desired arrival time. For example, it proposes a boarding time with a margin so that the user is not late for the desired arrival time. In addition, the analysis unit presents multiple boarding options and allows the user to select from them. For example, it provides an option to board earlier to avoid congestion, or to prioritize arrival time in exchange for allowing some congestion. This enables the analysis unit to make flexible suggestions based on the user's needs and provide a comfortable riding experience.

[0066] The notification unit notifies the user of the information proposed by the analysis unit. The notification unit provides information in real time, for example, through a messenger app. Specifically, the analysis unit notifies the user of the optimal boarding time and car number information proposed by the analysis unit to the user's smartphone or computer. The notification is provided in the form of a text message, push notification, email, or the like, so that the user can check it immediately. Furthermore, the notification unit notifies at an appropriate time, taking into consideration the user's current location and movement status. For example, when the user approaches the departure station, the notification unit notifies the user of the boarding time and car number information again to encourage the user to prepare for boarding. In addition, the notification unit accepts feedback from the user and continuously improves the accuracy and effectiveness of the proposal content. For example, the notification unit evaluates whether the user is satisfied with the proposed boarding time and car number, and adjusts the analysis algorithm based on the feedback. Furthermore, the notification unit also has a function for responding to emergencies and abnormal situations. For example, if a train is delayed or suspended, the notification unit notifies the user of an alternative route and the next optimal boarding time. This allows the user to always obtain the latest information, avoiding confusion and allowing smooth travel. The notification unit provides prompt and accurate information to maximize user convenience and support a comfortable riding experience.

[0067] The congestion avoidance system includes a collection unit that collects ticket gate data. The collection unit collects the ticket gate data. The ticket gate data includes, for example, the time when passing through the ticket gate, the user's ID, and the like, but is not limited to such examples. The collection unit can, for example, record the time when passing through the ticket gate and obtain the user's ID. This improves the accuracy of the analysis by collecting the ticket gate data. A 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 time when passing through the ticket gate and the user's ID into AI and have the AI ​​collect the data.

[0068] The congestion avoidance system includes a collection unit that collects sensor data. The collection unit collects the sensor data. The sensor data includes, for example, the number of passengers, temperature, humidity, and the like, but is not limited to such examples. The collection unit can, for example, count the number of passengers and measure temperature and humidity. Thereby, by collecting the sensor data, the accuracy of the analysis is improved. A 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 data on the number of passengers, temperature, and humidity into AI and cause AI to collect the data.

[0069] The analysis unit can suggest a boarding time and a car number based on the user's desired arrival time. The analysis unit suggests the optimal boarding time and car number taking into account the user's desired arrival time. For example, if the user wants to arrive at 8 o'clock, the analysis unit analyzes the congestion situation during that time period and suggests the boarding time and car number of a train that is not crowded. This allows for more appropriate suggestions by taking into account the user's desired arrival time. Some 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 user's desired arrival time and congestion situation into AI and have the AI ​​execute the suggestion of the optimal boarding time and car number.

[0070] The notification unit can notify the user of the proposed information in real time. The notification unit notifies the user of the proposed information in real time. For example, the notification unit provides the information in real time through a messenger app. This allows the user to obtain the latest information by notifying the information in real time. Some 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 proposed information to an AI and cause the AI ​​to execute the real-time notification.

[0071] The collection unit can collect sensor data on the number of passengers or the number of cars in real time. The collection unit collects sensor data on the number of passengers and the number of cars in real time. For example, the collection unit can count the number of passengers in real time and collect sensor data on the number of cars in real time. This improves the accuracy of analysis by collecting data in real time. 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 sensor data on the number of passengers and the number of cars to AI and cause AI to collect data in real time.

[0072] 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, the reception unit provides a simple interface and minimizes the input procedure. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input and enables the user to quickly input the departure station, arrival station, and desired arrival time. This improves user convenience by providing an input method according to 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 emotional data into the AI ​​and have the AI ​​adjust the input method.

[0073] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit analyzes the user's past input history and suggests 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 reception unit can preferentially suggest the input method (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict and suggest the departure station, arrival station, and desired arrival time to be used in a specific time period from the user's past input history. As a result, the efficiency of input is improved by suggesting the optimal input method based on the past input history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, 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 suggestion of the optimal input method.

[0074] The reception unit can automatically acquire the user's current location information when inputting the departure station, arrival station, and desired arrival time to simplify the input. The reception unit automatically acquires the user's current location information when inputting the departure station, arrival station, and desired arrival time to simplify the input. For example, when the user opens the app, the reception unit automatically acquires the current location and sets it as the departure station. In addition, when the user inputs the arrival station, the reception unit can also suggest the optimal candidate location in consideration of the distance from the current location. Furthermore, when the user uses the app while moving, the reception unit can update the current location in real time and reflect it as the departure station. This saves the effort of inputting by automatically acquiring the 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.

[0075] 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, the reception unit provides an interface with a calm color scheme to reduce visual stress. Also, when the user is having fun, the reception unit can provide an interface with a bright color scheme to make the input work enjoyable. Furthermore, when the user is tired, the reception unit can provide a simple and highly visible interface to make the input work easier. This improves the user's convenience by providing an interface according to 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 an AI and cause the AI ​​to execute the design adjustment of the interface.

[0076] The reception unit can automatically suggest candidate locations by referring to the user's past movement history when inputting the departure station, arrival station, and desired arrival time. The reception unit automatically suggests candidate locations by referring to the user's past movement history when inputting the departure station, arrival station, and desired arrival time. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. In addition, it is also possible to predict places that the user will visit on a specific day of the week or time period and suggest them as candidate locations. Furthermore, it is also possible to analyze the user's past movement patterns and suggest optimal candidate locations. As a result, by referring to the past movement history, the efficiency of input is improved. 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.

[0077] 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, it is also possible to propose a place related to a specific event as a candidate location from the user's calendar information. Furthermore, it is also possible to propose an optimal route according to the schedule based on the user's calendar information. As a result, it is possible to make an optimal proposal based on the schedule by referring to the calendar information. A 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 calendar information into AI and cause the AI ​​to execute a proposal based on the schedule.

[0078] 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, if the user is relaxed, the analysis unit performs an analysis that proceeds at a leisurely pace. Also, if the user is in a hurry, the analysis unit can perform an analysis that emphasizes the shortest route. Furthermore, if the user is excited, the analysis unit can perform an analysis that adds a visually stimulating effect. This enables more appropriate suggestions by performing an analysis according to 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 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 AI ​​and cause the AI ​​to adjust the analysis algorithm.

[0079] The analysis unit can analyze the user's past riding history and suggest the optimal boarding time and car number. The analysis unit analyzes the user's past riding history and suggests the optimal boarding time and car number. For example, the analysis unit suggests the optimal boarding time and car number based on the route the user has used in the past. In addition, the analysis unit can suggest a boarding time and car number that avoids congestion from the user's past riding history. Furthermore, the analysis unit can analyze the user's past riding history and suggest the most efficient boarding time and car number. As a result, the analysis unit makes optimal suggestions based on the past riding history, improving user convenience. Some 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 user's past riding history into AI and cause AI to execute the suggestion of the optimal boarding time and car number.

[0080] The analysis unit can propose an optimal boarding time in consideration of the user's current schedule. The analysis unit proposes an optimal boarding time in consideration of the user's current schedule. For example, the analysis unit proposes an optimal boarding time that matches the schedule based on the user's calendar information. In addition, a boarding time with ample time can be proposed based on the user's schedule. Furthermore, a boarding time that allows the user to arrive in the shortest time can be proposed in consideration of the user's schedule. This makes it possible to make more appropriate proposals by considering the current schedule. Some 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 user's schedule information into AI and cause the AI ​​to execute the proposal of the optimal boarding time.

[0081] 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, if the user is nervous, the analysis unit provides a simple and highly visible display method. In addition, if the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This improves the user's convenience by providing a display method according to 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 an AI and cause the AI ​​to adjust the display method.

[0082] The analysis unit can propose the optimal boarding time and car number taking into consideration the geographical location information of the user. The analysis unit proposes the optimal boarding time and car number taking into consideration the geographical location information of the user. For example, the analysis unit proposes the optimal boarding time and car number based on the station closest to the user's current location. In addition, the analysis unit can propose a boarding time and car number that avoids congestion based on the geographical location information of the user. Furthermore, the analysis unit can propose the most efficient boarding time and car number taking into consideration the geographical location information of the user. As a result, more appropriate proposals can be made by taking the geographical location information into consideration. 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 geographical location information of the user to AI and cause the AI ​​to execute the proposal of the optimal boarding time and car number.

[0083] The analysis unit can analyze the user's social media activity and suggest the relevant boarding time and car number. The analysis unit can analyze the user's social media activity and suggest the relevant boarding time and car number. For example, the analysis unit can suggest the optimal boarding time and car number based on the location information shared by the user on social media. In addition, the analysis unit can suggest the boarding time and car number that avoids crowding from the user's social media activity. Furthermore, the analysis unit can analyze the user's social media activity and suggest the most efficient boarding time and car number. As a result, the analysis unit can improve the user's convenience by making optimal suggestions based on social media activity. Some 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 user's social media activity data into AI and cause AI to execute the suggestion of the optimal boarding time and car number.

[0084] 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 unit can provide a notification at a relaxed timing. Also, when the user is in a hurry, the notification unit can provide a notification quickly. Furthermore, when the user is excited, the notification unit can provide a notification with a visually stimulating effect. This improves the user's convenience by providing a notification timing according to 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 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 AI ​​and cause the AI ​​to adjust the timing of the notification.

[0085] The notification unit can analyze the user's past notification history and propose the optimal notification method. The notification unit analyzes the user's past notification history and proposes the optimal notification method. For example, the notification unit preferentially proposes notification methods (push notification, email, etc.) that the user has preferred in the past. In addition, the notification unit can perform notifications at the optimal timing based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and propose the most effective notification method. As a result, the efficiency of notifications is improved by proposing the optimal notification method based on the past notification history. 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 input the user's past notification history into AI and have the AI ​​execute the proposal of the optimal notification method.

[0086] The notification unit can select the optimal notification means in consideration of the user's current situation. The notification unit selects the optimal notification means in consideration of the user's current situation. For example, when the user is on the move, the notification unit prioritizes push notification for notification. Also, when the user is doing desk work, the notification unit can also prioritize email notification for notification. Furthermore, when the user is in a meeting, the notification unit can also prioritize vibration notification for notification. This allows for more appropriate notification by considering the current situation. 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 cause AI to select the optimal notification means.

[0087] The notification unit can estimate the user's emotion and adjust the notification content based on the estimated user's emotion. The notification unit estimates the user's emotion and adjusts the notification content based on the estimated user's emotion. For example, if the user is nervous, the notification unit provides notification content that is simple and highly visible. Also, if the user is relaxed, the notification unit can provide notification content that includes detailed information. Furthermore, if the user is in a hurry, the notification unit can provide notification content that covers the main points. This improves the user's convenience by providing notification content according to 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 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 into an AI and cause the AI ​​to adjust the notification content.

[0088] The notification unit can propose an optimal notification method in consideration of the geographical location information of the user. The notification unit proposes an optimal notification method in consideration of the geographical location information of the user. For example, the optimal notification method is proposed based on the station closest to the user's current location. In addition, a notification method that avoids congestion can be proposed based on the geographical location information of the user. Furthermore, the most efficient notification method can be proposed in consideration of the geographical location information of the user. As a result, more appropriate notification is possible by taking the geographical location information into consideration. A part 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 input the geographical location information of the user to AI and have the AI ​​execute the proposal of the optimal notification method.

[0089] The notification unit can analyze the user's social media activity and propose related notification content. The notification unit can analyze the user's social media activity and propose related notification content. For example, the notification unit can propose optimal notification content based on location information shared by the user on social media. In addition, notification content that avoids congestion can be proposed based on the user's social media activity. Furthermore, the notification unit can analyze the user's social media activity and propose the most efficient notification content. As a result, the user's convenience is improved by proposing optimal notification content based on social media activity. A part 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 input the user's social media activity data into AI and have the AI ​​execute the proposal of optimal notification content.

[0090] 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 stressed, the collection unit reduces the frequency of data collection to reduce the load on the system. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed information. Furthermore, when the user is in a hurry, the collection unit can collect the minimum amount of data necessary and prioritize quick processing. This reduces the load on the system by providing a data collection frequency according to 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 collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to an AI and cause the AI ​​to adjust the data collection frequency.

[0091] The collection unit can analyze the user's past data collection history and propose the optimal collection method. The collection unit analyzes the user's past data collection history and proposes the optimal collection method. For example, the collection unit preferentially collects data types that the user has collected in the past. In addition, data can be collected at the optimal timing from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and propose the most effective collection method. As a result, the efficiency of data collection is improved by proposing the optimal collection method based on the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI and have the AI ​​execute the proposal of the optimal collection method.

[0092] The collection unit can select the optimal data collection means in consideration of the user's current situation. The collection unit selects the optimal data collection means in consideration of the user's current situation. For example, when the user is on the move, the collection unit prioritizes mobile data collection. Also, when the user is doing desk work, the collection unit can also collect data using Wi-Fi. Furthermore, when the user is in a meeting, the collection unit can also prioritize vibration notification for data collection. This enables more appropriate data collection by considering the current situation. Part 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 user's current situation data into AI and cause AI to select the optimal data collection means.

[0093] The collection unit can estimate the user's emotion and determine the priority of the collected data based on the estimated user's emotion. The collection unit estimates the user's emotion and determines the priority of the collected data based on the estimated user's emotion. For example, when the user is nervous, the collection unit prioritizes collecting important data. Also, when the user is relaxed, the collection unit can also prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting the minimum necessary data. This improves the efficiency of data collection by providing data priority according to 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 an AI and cause the AI ​​to execute the priority determination of the collected data.

[0094] The collection unit can propose an optimal data collection method in consideration of the geographical location information of the user. The collection unit proposes an optimal data collection method in consideration of the geographical location information of the user. For example, the optimal data collection method is proposed based on the station closest to the user's current location. In addition, a data collection method that avoids congestion can be proposed based on the geographical location information of the user. Furthermore, the most efficient data collection method can be proposed in consideration of the geographical location information of the user. As a result, more appropriate data collection is possible by taking the geographical location information into consideration. A 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 geographical location information of the user to AI and have the AI ​​execute the proposal of the optimal data collection method.

[0095] The collection unit can analyze the user's social media activity and suggest a related data collection method. The collection unit analyzes the user's social media activity and suggests a related data collection method. For example, the collection unit suggests an optimal data collection method based on location information shared by the user on social media. In addition, a data collection method that avoids congestion can be suggested from the user's social media activity. Furthermore, the collection unit can analyze the user's social media activity and suggest the most efficient data collection method. As a result, the efficiency of data collection is improved by suggesting the optimal data collection method based on the social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​execute the suggestion of the optimal data collection method.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can also suggest the optimal boarding time and car number taking into account the user's health data. For example, it can monitor the user's heart rate and stress level, and if the user's health condition is not good, it can suggest a less crowded car number or a time when it is easier to secure a seat. Also, if the user is not getting enough exercise, it can suggest a boarding time and car number that will require a slightly longer walking distance. Furthermore, it is also possible to select a car number with a comfortable temperature and humidity based on the user's health data. This makes it possible to provide the optimal boarding environment according to the user's health condition.

[0098] The collection unit can also suggest the optimal boarding time and car number based on the user's purchasing history. For example, if the user has a history of frequent purchases at a particular store, the collection unit can suggest a boarding time from a station near that store. Also, if the user tends to make purchases during a particular time period, the collection unit can suggest a boarding time that matches that time period. Furthermore, it is also possible to suggest the optimal car number to avoid congestion based on the user's purchasing history. This makes it possible to provide the optimal boarding environment according to the user's purchasing activities.

[0099] The collection unit can also suggest the optimal boarding time and car number based on the user's hobbies and interests. For example, if the user is interested in a particular event or watching a sporting event, the collection unit can suggest a boarding time that matches the time when the event is being held. Also, if the user is interested in a particular tourist spot, the collection unit can suggest a car number that has good access to the tourist spot. Furthermore, it is also possible to select a car number that provides a comfortable boarding environment based on the user's hobbies and interests. This makes it possible to provide the user with the optimal boarding environment according to their hobbies and interests.

[0100] The analysis unit can also suggest the optimal boarding time and car number based on the user's past travel history. For example, the analysis unit can suggest the optimal boarding time and car number based on the places the user has visited in the past and the means of transportation used by the user. The analysis unit can also suggest the optimal car number to avoid congestion, taking into account the routes and time periods that the user has preferred in the past. Furthermore, it is also possible to select a car number that provides a comfortable riding environment from the user's past travel history. This makes it possible to provide the optimal riding environment according to the user's travel history.

[0101] The notification unit can also suggest the optimal boarding time and car number taking into account the user's weather information. For example, when it is raining, a car number that is a short travel distance from the station can be suggested. Also, on a hot day, a car number that is air-conditioned can be suggested. Furthermore, if the user is sensitive to the weather, it is also possible to select a car number that provides a comfortable boarding environment based on the weather information. In this way, it is possible to provide the optimal boarding environment taking into account the weather information.

[0102] The analysis unit can estimate the user's emotions and suggest a boarding time and car number based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can suggest a less crowded car number or a time slot when it is easy to secure a seat. Also, if the user is relaxed, the analysis unit can suggest a car number with a good view or a comfortable seat. Furthermore, if the user is in a hurry, the analysis unit can also suggest a boarding time and car number that will allow the user to arrive in the shortest time. This makes it possible to provide an optimal boarding environment according to the user's emotions.

[0103] The notification unit can estimate the user's emotions and adjust the notification content based on the estimated user's emotions. For example, if the user is nervous, the notification unit can provide notification content that is simple and highly visible. If the user is relaxed, the notification unit can also provide notification content that includes detailed information. Furthermore, if the user is in a hurry, the notification unit can also provide notification content that focuses on the main points. This improves user convenience by providing notification content according to the user's emotions.

[0104] 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, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the load on the system. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed information. Furthermore, when the user is in a hurry, the collection unit can collect the minimum amount of data necessary and prioritize quick processing. In this way, the load on the system can be reduced by providing a data collection frequency according to the user's emotions.

[0105] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user's emotions. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This improves user convenience by providing an interface according to the user's emotions.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This improves user convenience by providing a display method according to the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit receives input of the user's departure station, arrival station, and desired arrival time. When the user inputs the departure station, arrival station, and desired arrival time, the reception unit can receive the information through, for example, a messenger app. Step 2: The analysis unit analyzes the information received by the reception unit, as well as the ticket gate data and sensor data, and proposes the optimal boarding time and car number. The analysis unit, for example, uses AI technology to analyze the congestion situation, taking into account the user's desired arrival time. The analysis unit proposes a boarding time and car number for a train that is not crowded, based on the ticket gate data and sensor data, for example. Step 3: The notification unit notifies the user of the information proposed by the analysis unit. The notification unit provides the information in real time, for example, through a messenger app. This allows the user to always obtain the latest information.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements including the reception unit, analysis unit, notification unit, and collection 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 through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes ticket gate data and sensor data using AI technology to propose 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 provides information in real time through a messenger app. The collection unit collects ticket gate data and sensor data using, for example, the camera 42 and sensor of the smart device 14. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements including the reception unit, analysis unit, notification unit, and collection unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the 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 through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes ticket gate data and sensor data using AI technology to propose 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 provides information in real time through a messenger app. The collection unit collects ticket gate data and sensor data using, for example, the camera 42 and sensor of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements including the above-mentioned reception unit, analysis unit, notification unit, and collection 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 through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes ticket gate data and sensor data using AI technology to propose 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 provides information in real time through a messenger app. The collection unit collects ticket gate data and sensor data using, for example, the camera 42 and sensor of the headset type terminal 314. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements including the reception unit, analysis unit, notification unit, and collection 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 through a messenger app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes ticket gate data and sensor data using AI technology to propose the optimal boarding time and car number. The notification unit is realized, for example, by the control unit 46A of the robot 414, and provides information in real time through a messenger app. The collection unit collects ticket gate data and sensor data using, for example, the camera 42 and sensor of the robot 414. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] These emotions are distributed in the 3 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.

[0165] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (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 the information received by the reception unit and ticket gate data or sensor data, and proposes a boarding time and a car number; A notification unit that notifies a user of the information proposed by the analysis unit. A system characterized by: (Appendix 2) Equipped with a collection unit that collects ticket gate data 2. The system of claim 1, (Appendix 3) A collection unit for collecting sensor data is provided. 2. The system of claim 1, (Appendix 4) The analysis unit is Suggest boarding times and car numbers based on the user's desired arrival time 2. The system of claim 1, (Appendix 5) The notification unit is Notify users of suggested information in real time 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collect real-time passenger count or car sensor data 3. The system of claim 2, (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 Analyze the user's past riding history and suggest the optimal boarding time and car number 2. The system of claim 1, (Appendix 15) The analysis unit is Suggest optimal ride times based on the user's current schedule 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 Suggest ride times and cars based on the user's geographic location 2. The system of claim 1, (Appendix 18) The analysis unit is Analyze users' social media activity to suggest relevant ride times and cars 2. The system of claim 1, (Appendix 19) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The notification unit is Analyze the user's past notification history and suggest the optimal notification method 2. The system of claim 1, (Appendix 21) The notification unit is Select the most appropriate notification method based on the user's current situation 2. The system of claim 1, (Appendix 22) The notification unit is Estimate user emotions and adjust notification content based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The notification unit is Considering the user's geographic location, suggest the best notification method 2. The system of claim 1, (Appendix 24) The notification unit is Analyze your social media activity and suggest relevant notification content 2. The system of claim 1, (Appendix 25) The collecting unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 3. The system of claim 2, (Appendix 26) The collecting unit includes: Analyze the user's past data collection history and suggest the optimal collection method 3. The system of claim 2, (Appendix 27) The collecting unit includes: Select the most appropriate data collection method based on the user's current situation 3. The system of claim 2, (Appendix 28) The collecting unit includes: Inferring user emotions and prioritizing collected data based on the estimated user emotions 3. The system of claim 2, (Appendix 29) The collecting unit includes: Considering the user's geographic location, suggest the best way to collect data 3. The system of claim 2, (Appendix 30) The collecting unit includes: Analyze users' social media activity and suggest relevant data collection methods 3. The system of claim 2, [Explanation of symbols]

[0181] 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 the information received by the reception unit, ticket gate data, and sensor data, and proposes a boarding time and a car number; A notification unit that notifies a user of the information proposed by the analysis unit. A system characterized by:

2. Equipped with a collection unit that collects ticket gate data 2. The system of claim 1 .

3. A collection unit for collecting sensor data is provided.

2. The system of claim 1 .

4. The analysis unit is Suggest boarding times and car numbers based on the user's desired arrival time 2. The system of claim 1 .

5. The notification unit is Notify users of suggested information in real time 2. The system of claim 1 .

6. The collecting unit includes: Collect real-time passenger count or car sensor data 3. The system of claim 2.

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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