System

Abstract

An object of a system according to an embodiment is to provide a specific traffic line and a specific time zone for avoiding a crowded train in a commuting time zone.SOLUTION: A system according to an embodiment includes a people flow analysis unit, a congestion analysis unit, and a visualization unit. The human flow analysis unit analyzes a flow of people at a station. The congestion analysis unit analyzes a congestion situation in the train. The visualization unit visualizes a traffic line and a time zone for congestion avoidance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks information on specific routes and time periods to avoid crowded trains during rush hour, leaving room for improvement.

[0005] The system according to the embodiment aims to provide specific routes and time periods for avoiding crowded trains during rush hour. [Means for solving the problem]

[0006] The system according to the embodiment includes a people flow analysis unit, a congestion analysis unit, and a visualization unit. The people flow analysis unit analyzes the flow of people at stations. The congestion analysis unit analyzes the congestion situation on trains. The visualization unit visualizes traffic patterns and time periods to avoid congestion. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific routes and time periods for avoiding crowded trains during rush hour. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A congestion avoidance system according to an embodiment of the present invention is a system that analyzes the flow of people at stations and the congestion situation on trains based on data, and visualizes the flow of people and time periods for avoiding congestion. This allows the user to know the optimal flow of people and time periods for avoiding congestion.

[0029] The congestion avoidance system according to the embodiment includes a people flow analysis unit, a congestion analysis unit, and a visualization unit. The people flow analysis unit analyzes the flow of people at a station. For example, it analyzes video data obtained from cameras at station ticket gates and on platforms to identify congested times and locations. The people flow analysis unit can also use AI to identify individual user movement patterns and propose individual congestion avoidance routes. For example, it can track which ticket gates a specific user uses and which platforms they head to, and propose an optimal route to avoid congestion. The congestion analysis unit analyzes the congestion situation on a train. For example, it can analyze data obtained from sensors and cameras installed in each car to identify empty and crowded cars. The congestion analysis unit can also identify users' seat selection patterns and propose optimal seats. For example, it can analyze which seats are popular during specific times and propose available seats. The visualization unit visualizes traffic patterns and time periods for avoiding congestion. For example, it can display congestion avoidance routes on station platforms or suggest optimal time periods for avoiding congestion via an app. The visualization unit can also propose individual optimal traffic patterns and time periods based on users' past travel history. For example, the congestion avoidance system according to the embodiment may provide a route to avoid congestion during a specific time period based on past data, thereby enabling the user to know the optimal route and time period for avoiding congestion.

[0030] The people flow analysis unit can analyze video data obtained from cameras to identify congested times and locations. For example, the people flow analysis unit analyzes video data obtained from cameras at station ticket gates and on platforms to identify congested times and locations. For example, it can identify that the center of a platform is particularly congested during certain times of the day. The people flow analysis unit can also use AI to identify the movement patterns of individual users and suggest individual routes to avoid congestion. For example, it can track which ticket gate a specific user uses and which platform they head to, and suggest the optimal route to avoid congestion. This allows users to accurately identify congested times and locations.

[0031] The congestion analysis unit can analyze data obtained from sensors and cameras to identify empty and crowded cars. The congestion analysis unit, for example, analyzes data obtained from sensors and cameras installed in each car to identify empty and crowded cars. For example, it can identify that car 3 is relatively empty during a specific time period. The congestion analysis unit can also identify the user's seat selection pattern and suggest the optimal seat. For example, it can analyze which seats are popular during a specific time period and suggest an empty seat. This allows the user to select an empty car to board.

[0032] The visualization unit can display routes to avoid congestion on the platform. The visualization unit displays routes to avoid congestion on, for example, a station platform. For example, during times when the center of the platform is crowded, a route to the edge of the platform is displayed. The visualization unit can also take into account the user's past movement history and suggest optimal individual flow lines and time periods. For example, based on past data, it provides routes to avoid congestion during specific time periods. This allows the user to visually confirm specific routes to avoid congestion.

[0033] The visualization unit can present the optimal time period for avoiding crowds on the app. For example, if congestion is predicted during a specific time period, the visualization unit can notify the user to avoid that time period. The visualization unit can also take into account the user's past movement history and suggest optimal individual routes and time periods. For example, the visualization unit can provide a route for avoiding congestion during a specific time period based on past data. This allows the user to know the optimal time period for avoiding crowds.

[0034] For a monthly fee, the system can provide real-time congestion information and information on how to avoid congestion. For example, by paying a monthly fee, the system can provide real-time congestion information and information on how to avoid congestion. For example, it can allow users to check real-time congestion information on an app. The system can also take into account the user's past movement history and suggest optimal individual routes and time periods. For example, it can provide routes to avoid congestion during specific time periods based on past data. This allows users to obtain real-time congestion information and information on how to avoid congestion for a fee.

[0035] The system can analyze past data and visualize trends such as "Many people get off at this station at this time of day, on this car." For example, the system can analyze past data and visualize trends such as "Many people get off at this station at this time of day, on this car." For example, based on past data, the system can identify that many people get off at a particular car at a particular time of day. The system can also take into account the user's past travel history and suggest optimal individual routes and time periods. For example, based on past data, the system can provide a route that avoids congestion at a particular time of day. This allows the user to obtain accurate information based on past data.

[0036] The people flow analysis unit uses AI to identify users' movement patterns and can suggest individual routes to avoid congestion. For example, the people flow analysis unit analyzes video data obtained from cameras at station ticket gates and on platforms to identify the movement patterns of individual users. For example, it tracks which ticket gate a specific user passes through and which platform they head to, and suggests the optimal route to avoid congestion. The people flow analysis unit can also use AI to suggest optimal movement lines, taking into account the user's walking speed and the size of their luggage. For example, for users who walk slowly, it suggests routes that use wider corridors or elevators to avoid congestion. This allows users to know their individual routes to avoid congestion.

[0037] The people flow analysis unit uses AI to consider a user's walking speed and the size of their luggage and can suggest optimal routes. For example, the people flow analysis unit analyzes camera footage at a station to identify a user's walking speed. For example, for a user who walks slowly, it can suggest a route that uses wide corridors or elevators to avoid crowds. The people flow analysis unit can also consider the size of a user's luggage and suggest a car that makes it easy to store their luggage. For example, for a user with large luggage, it can suggest a car that makes it easy to store their luggage. This allows users to know the optimal route to take based on their walking speed and the size of their luggage.

[0038] The people flow analysis unit can predict congestion by taking into account weather and event information around the station. For example, the people flow analysis unit collects weather data around the station in real time and reflects it in the congestion prediction. For example, it predicts that the number of users will increase on rainy days and suggests routes to avoid congestion. The people flow analysis unit can also collect event information around the station and reflect it in the congestion prediction. For example, it predicts that the number of users will increase on days when a specific event is held and suggests routes to avoid congestion. This allows users to know the congestion prediction that takes into account weather and event information.

[0039] The people flow analysis unit can propose optimal travel routes by taking into account the interaction with different modes of transportation. For example, the people flow analysis unit collects bus operation information around stations and proposes optimal travel routes. For example, it considers bus operation status and proposes routes that avoid congestion. The people flow analysis unit can also collect bicycle sharing information around stations and propose optimal travel routes. For example, it considers the location of bicycle sharing stations and proposes routes that avoid congestion. This allows users to know the optimal travel route that takes into account different modes of transportation.

[0040] The congestion analysis unit can identify the user's seat selection pattern and suggest the optimal seat. The congestion analysis unit, for example, analyzes camera footage inside the train to identify the user's seat selection pattern. For example, it analyzes which seats are popular during a specific time period and suggests an available seat. The congestion analysis unit can also suggest the optimal seat by taking into account the user's past seat selection history. For example, it can suggest an available seat during a specific time period based on past data. This allows the user to know the optimal seat.

[0041] The congestion analysis unit can predict the user's boarding and alighting times and suggest the optimal timing for boarding and alighting. For example, the congestion analysis unit analyzes camera footage inside the train to identify the user's boarding and alighting times. For example, it analyzes which doors will be crowded during a specific time period and suggests the optimal timing for boarding and alighting. The congestion analysis unit can also take into account the user's past boarding and alighting history and suggest the optimal timing for boarding and alighting. For example, it provides boarding and alighting times to avoid congestion during a specific time period based on past data. This allows the user to know the optimal timing for boarding and alighting.

[0042] The congestion analysis unit can suggest a comfortable car by taking into account the temperature and humidity inside the car. The congestion analysis unit, for example, analyzes data obtained from temperature and humidity sensors inside the train to identify a comfortable car. For example, it can suggest a car with an appropriate temperature and low humidity. The congestion analysis unit can also estimate the user's comfort level in real time and suggest a comfortable car. For example, it can analyze the user's facial expressions from camera footage to estimate the comfort level. This allows the user to know a comfortable car that takes into account the temperature and humidity.

[0043] The congestion analysis unit can consider the size of the user's luggage and suggest a car in which it is easy to place the luggage. The congestion analysis unit, for example, analyzes camera footage inside the train and identifies the size of the user's luggage. For example, for a user with large luggage, it can suggest a car in which it is easy to place the luggage. The congestion analysis unit can also consider the user's past luggage size and suggest the most suitable car. For example, it can suggest a car in which it is easy to place the luggage based on past data. This allows the user to know which car it is easy to place the luggage in.

[0044] The visualization unit can consider the user's past movement history and propose optimal individual movement lines and time periods. For example, the visualization unit analyzes the user's past movement history and proposes optimal movement lines and time periods to avoid congestion. For example, it provides a route to avoid congestion during a specific time period based on past data. The visualization unit can also consider the user's health condition (difficulty walking, pregnancy, etc.) and propose optimal movement lines and time periods. For example, it proposes a route that uses elevators or escalators for a user who has difficulty walking. This allows the user to know the optimal movement lines and time periods based on their past movement history.

[0045] The visualization unit can propose optimal movement lines and time periods taking into account the user's health condition. For example, the visualization unit analyzes the user's health condition from camera footage and proposes optimal movement lines and time periods. For example, for a user who has difficulty walking, it proposes a route that uses elevators or escalators. The visualization unit can also propose optimal movement lines and time periods taking into account the user's self-reported health condition. For example, for a pregnant user, it proposes a route that uses wide corridors and elevators to avoid crowds. This allows the user to know the optimal movement lines and time periods based on their health condition.

[0046] The visualization unit can propose an optimal travel route by taking into account the congestion status of commercial facilities around stations. The visualization unit, for example, collects congestion status of commercial facilities around stations in real time and proposes an optimal travel route. For example, it provides a route that avoids congestion at commercial facilities. The visualization unit can also propose an optimal travel route by taking into account the congestion status of the user's destination (office, school, etc.). For example, it provides a route that avoids congestion at offices and schools. This allows the user to know the optimal travel route that takes into account the congestion status of commercial facilities.

[0047] The visualization unit can propose an optimal travel route by taking into account the congestion situation at the user's destination. For example, the visualization unit collects information on the congestion situation at the user's destination in real time and proposes an optimal travel route. For example, it provides a route that avoids crowded offices or schools. The visualization unit can also propose optimal movement lines and time periods by taking into account the user's health condition (difficulty walking, pregnancy, etc.). For example, for a user who has difficulty walking, it can propose a route that uses elevators or escalators. This allows the user to know the optimal travel route that takes into account the congestion situation at the destination.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The congestion avoidance system can further include a voice guidance unit. The voice guidance unit provides real-time voice guidance based on the user's current location and destination. For example, when passing through a station ticket gate, it provides voice guidance on the optimal route to the platform. The voice guidance unit can also take into account the user's past movement history and provide voice guidance on the optimal individual flow line. For example, it provides voice guidance on routes to avoid congestion during specific time periods. This allows the user to receive voice guidance in addition to visual information.

[0050] The congestion avoidance system can further include an energy consumption analysis unit. The energy consumption analysis unit analyzes energy consumption associated with the user's movements and proposes optimal movement lines. For example, it proposes a route that reduces energy consumption by recommending the use of escalators and elevators. The energy consumption analysis unit can also propose a route that minimizes energy consumption by taking into account the user's health condition. For example, it proposes a route that uses elevators for users who have difficulty walking. This allows the user to know the optimal movement line that reduces energy consumption.

[0051] The congestion avoidance system can further include a barrier-free support unit. The barrier-free support unit takes into account the physical limitations of the user and proposes the optimal route. For example, it proposes a route that uses elevators or ramps for wheelchair users. The barrier-free support unit can also propose routes that take into account the location of tactile paving blocks for visually impaired people. For example, it provides a route that follows the tactile paving blocks. This allows the user to know the optimal route to follow their physical limitations.

[0052] The congestion avoidance system may further include an entertainment provider. The entertainment provider provides entertainment content to allow users to spend their waiting time effectively. For example, it may stream movies or music on station platforms or inside trains. The entertainment provider may also suggest content based on the user's preferences. For example, it may suggest movies or music that the user might be interested in based on their past viewing history. This allows users to spend their waiting time effectively.

[0053] The congestion avoidance system can further include a health management unit. The health management unit monitors the user's health condition and suggests the optimal route. For example, it measures the user's heart rate and blood pressure and suggests a route based on the user's health condition. The health management unit can also suggest rest points taking into account the user's health condition. For example, a user with a high heart rate can be guided to the location of a bench where the user can rest. This allows the user to know the optimal route based on their health condition.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The people flow analysis unit analyzes the flow of people at the station. For example, it analyzes video data obtained from cameras at station ticket gates and on platforms to identify crowded times and locations. The people flow analysis unit can also use AI to identify the movement patterns of individual users and suggest individual routes to avoid crowds. For example, it can track which ticket gate a specific user uses and which platform they head to, and suggest the optimal route to avoid crowds. Step 2: The congestion analysis unit analyzes the congestion situation on the train. For example, it analyzes data obtained from sensors and cameras installed in each car and identifies which cars are empty and which are crowded. The congestion analysis unit can also identify the user's seat selection patterns and suggest the most suitable seats. For example, it analyzes which seats are popular during a specific time period and suggests available seats. Step 3: The visualization unit visualizes the routes and time periods for avoiding crowds. For example, it may display routes for avoiding crowds on station platforms or suggest optimal times for avoiding crowds on the app. The visualization unit can also consider the user's past movement history and suggest optimal routes and time periods for each individual. For example, it may provide routes for avoiding crowds during specific times based on past data.

[0056] (Example 2) A congestion avoidance system according to an embodiment of the present invention is a system that analyzes the flow of people at stations and the congestion situation on trains based on data, and visualizes the flow of people and time periods for avoiding congestion. This allows the user to know the optimal flow of people and time periods for avoiding congestion.

[0057] The congestion avoidance system according to the embodiment includes a people flow analysis unit, a congestion analysis unit, and a visualization unit. The people flow analysis unit analyzes the flow of people at a station. For example, it analyzes video data obtained from cameras at station ticket gates and on platforms to identify congested times and locations. The people flow analysis unit can also use AI to identify individual user movement patterns and propose individual congestion avoidance routes. For example, it can track which ticket gates a specific user uses and which platforms they head to, and propose an optimal route to avoid congestion. The congestion analysis unit analyzes the congestion situation on a train. For example, it can analyze data obtained from sensors and cameras installed in each car to identify empty and crowded cars. The congestion analysis unit can also identify users' seat selection patterns and propose optimal seats. For example, it can analyze which seats are popular during specific times and propose available seats. The visualization unit visualizes traffic patterns and time periods for avoiding congestion. For example, it can display congestion avoidance routes on station platforms or suggest optimal time periods for avoiding congestion via an app. The visualization unit can also propose individual optimal traffic patterns and time periods based on users' past travel history. For example, the congestion avoidance system according to the embodiment may provide a route to avoid congestion during a specific time period based on past data, thereby enabling the user to know the optimal route and time period for avoiding congestion.

[0058] The people flow analysis unit can analyze video data obtained from cameras to identify congested times and locations. For example, the people flow analysis unit analyzes video data obtained from cameras at station ticket gates and on platforms to identify congested times and locations. For example, it can identify that the center of a platform is particularly congested during certain times of the day. The people flow analysis unit can also use AI to identify the movement patterns of individual users and suggest individual routes to avoid congestion. For example, it can track which ticket gate a specific user uses and which platform they head to, and suggest the optimal route to avoid congestion. This allows users to accurately identify congested times and locations.

[0059] The congestion analysis unit can analyze data obtained from sensors and cameras to identify empty and crowded cars. The congestion analysis unit, for example, analyzes data obtained from sensors and cameras installed in each car to identify empty and crowded cars. For example, it can identify that car 3 is relatively empty during a specific time period. The congestion analysis unit can also identify the user's seat selection pattern and suggest the optimal seat. For example, it can analyze which seats are popular during a specific time period and suggest an empty seat. This allows the user to select an empty car to board.

[0060] The visualization unit can display routes to avoid congestion on the platform. The visualization unit displays routes to avoid congestion on, for example, a station platform. For example, during times when the center of the platform is crowded, a route to the edge of the platform is displayed. The visualization unit can also take into account the user's past movement history and suggest optimal individual flow lines and time periods. For example, based on past data, it provides routes to avoid congestion during specific time periods. This allows the user to visually confirm specific routes to avoid congestion.

[0061] The visualization unit can present the optimal time period for avoiding crowds on the app. For example, if congestion is predicted during a specific time period, the visualization unit can notify the user to avoid that time period. The visualization unit can also take into account the user's past movement history and suggest optimal individual routes and time periods. For example, the visualization unit can provide a route for avoiding congestion during a specific time period based on past data. This allows the user to know the optimal time period for avoiding crowds.

[0062] For a monthly fee, the system can provide real-time congestion information and information on how to avoid congestion. For example, by paying a monthly fee, the system can provide real-time congestion information and information on how to avoid congestion. For example, it can allow users to check real-time congestion information on an app. The system can also take into account the user's past movement history and suggest optimal individual routes and time periods. For example, it can provide routes to avoid congestion during specific time periods based on past data. This allows users to obtain real-time congestion information and information on how to avoid congestion for a fee.

[0063] The system can analyze past data and visualize trends such as "Many people get off at this station at this time of day, on this car." For example, the system can analyze past data and visualize trends such as "Many people get off at this station at this time of day, on this car." For example, based on past data, the system can identify that many people get off at a particular car at a particular time of day. The system can also take into account the user's past travel history and suggest optimal individual routes and time periods. For example, based on past data, the system can provide a route that avoids congestion at a particular time of day. This allows the user to obtain accurate information based on past data.

[0064] The people flow analysis unit uses AI to identify users' movement patterns and can suggest individual routes to avoid congestion. For example, the people flow analysis unit analyzes video data obtained from cameras at station ticket gates and on platforms to identify the movement patterns of individual users. For example, it tracks which ticket gate a specific user passes through and which platform they head to, and suggests the optimal route to avoid congestion. The people flow analysis unit can also use AI to suggest optimal movement lines, taking into account the user's walking speed and the size of their luggage. For example, for users who walk slowly, it suggests routes that use wider corridors or elevators to avoid congestion. This allows users to know their individual routes to avoid congestion.

[0065] The people flow analysis unit uses AI to consider a user's walking speed and the size of their luggage and can suggest optimal routes. For example, the people flow analysis unit analyzes camera footage at a station to identify a user's walking speed. For example, for a user who walks slowly, it can suggest a route that uses wide corridors or elevators to avoid crowds. The people flow analysis unit can also consider the size of a user's luggage and suggest a car that makes it easy to store their luggage. For example, for a user with large luggage, it can suggest a car that makes it easy to store their luggage. This allows users to know the optimal route to take based on their walking speed and the size of their luggage.

[0066] The people flow analysis unit can use emotion estimation functions to estimate a user's stress level in real time and suggest a less stressful route. For example, the people flow analysis unit analyzes a user's facial expressions from camera footage to estimate their stress level. For example, for a user with high stress levels, it can suggest a route that uses wider corridors or elevators to avoid crowds. The people flow analysis unit can also collect a user's biometric data (heart rate and electrodermal activity) using sensors and analyze their stress level using an emotion estimation algorithm. For example, it calculates a stress score based on heart rate fluctuations. This allows the user to know which route will be less stressful.

[0067] The people flow analysis unit can predict congestion by taking into account weather and event information around the station. For example, the people flow analysis unit collects weather data around the station in real time and reflects it in the congestion prediction. For example, it predicts that the number of users will increase on rainy days and suggests routes to avoid congestion. The people flow analysis unit can also collect event information around the station and reflect it in the congestion prediction. For example, it predicts that the number of users will increase on days when a specific event is held and suggests routes to avoid congestion. This allows users to know the congestion prediction that takes into account weather and event information.

[0068] The people flow analysis unit can propose optimal travel routes by taking into account the interaction with different modes of transportation. For example, the people flow analysis unit collects bus operation information around stations and proposes optimal travel routes. For example, it considers bus operation status and proposes routes that avoid congestion. The people flow analysis unit can also collect bicycle sharing information around stations and propose optimal travel routes. For example, it considers the location of bicycle sharing stations and proposes routes that avoid congestion. This allows users to know the optimal travel route that takes into account different modes of transportation.

[0069] The congestion analysis unit can identify the user's seat selection pattern and suggest the optimal seat. The congestion analysis unit, for example, analyzes camera footage inside the train to identify the user's seat selection pattern. For example, it analyzes which seats are popular during a specific time period and suggests an available seat. The congestion analysis unit can also suggest the optimal seat by taking into account the user's past seat selection history. For example, it can suggest an available seat during a specific time period based on past data. This allows the user to know the optimal seat.

[0070] The congestion analysis unit can predict the user's boarding and alighting times and suggest the optimal timing for boarding and alighting. For example, the congestion analysis unit analyzes camera footage inside the train to identify the user's boarding and alighting times. For example, it analyzes which doors will be crowded during a specific time period and suggests the optimal timing for boarding and alighting. The congestion analysis unit can also take into account the user's past boarding and alighting history and suggest the optimal timing for boarding and alighting. For example, it provides boarding and alighting times to avoid congestion during a specific time period based on past data. This allows the user to know the optimal timing for boarding and alighting.

[0071] The congestion analysis unit can estimate the user's comfort level in real time using an emotion estimation function and suggest a comfortable vehicle. The congestion analysis unit, for example, analyzes the user's facial expressions from camera footage to estimate the comfort level. For example, it can suggest an empty vehicle to a user with a high comfort level. The congestion analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the comfort level using an emotion estimation algorithm. For example, it can calculate a comfort level score based on heart rate fluctuations. This allows the user to know which vehicle is comfortable.

[0072] The congestion analysis unit can suggest a comfortable car by taking into account the temperature and humidity inside the car. The congestion analysis unit, for example, analyzes data obtained from temperature and humidity sensors inside the train to identify a comfortable car. For example, it can suggest a car with an appropriate temperature and low humidity. The congestion analysis unit can also estimate the user's comfort level in real time and suggest a comfortable car. For example, it can analyze the user's facial expressions from camera footage to estimate the comfort level. This allows the user to know a comfortable car that takes into account the temperature and humidity.

[0073] The congestion analysis unit can consider the size of the user's luggage and suggest a car in which it is easy to place the luggage. The congestion analysis unit, for example, analyzes camera footage inside the train and identifies the size of the user's luggage. For example, for a user with large luggage, it can suggest a car in which it is easy to place the luggage. The congestion analysis unit can also consider the user's past luggage size and suggest the most suitable car. For example, it can suggest a car in which it is easy to place the luggage based on past data. This allows the user to know which car it is easy to place the luggage in.

[0074] The congestion analysis unit can use the emotion estimation function to optimize the content of in-car announcements based on the user's emotions, thereby improving user satisfaction. For example, the congestion analysis unit analyzes the user's facial expressions from camera footage to estimate their emotions. For example, it displays relevant announcements to users with positive emotions. The congestion analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the congestion analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This allows the user to receive the optimal in-car announcements based on their emotions.

[0075] The visualization unit can consider the user's past movement history and propose optimal individual movement lines and time periods. For example, the visualization unit analyzes the user's past movement history and proposes optimal movement lines and time periods to avoid congestion. For example, it provides a route to avoid congestion during a specific time period based on past data. The visualization unit can also consider the user's health condition (difficulty walking, pregnancy, etc.) and propose optimal movement lines and time periods. For example, it proposes a route that uses elevators or escalators for a user who has difficulty walking. This allows the user to know the optimal movement lines and time periods based on their past movement history.

[0076] The visualization unit can propose optimal movement lines and time periods taking into account the user's health condition. For example, the visualization unit analyzes the user's health condition from camera footage and proposes optimal movement lines and time periods. For example, for a user who has difficulty walking, it proposes a route that uses elevators or escalators. The visualization unit can also propose optimal movement lines and time periods taking into account the user's self-reported health condition. For example, for a pregnant user, it proposes a route that uses wide corridors and elevators to avoid crowds. This allows the user to know the optimal movement lines and time periods based on their health condition.

[0077] The visualization unit can estimate a user's stress level in real time using an emotion estimation function and suggest routes and time periods that will reduce stress. For example, the visualization unit can analyze a user's facial expressions from camera footage to estimate their stress level. For example, for a user with high stress levels, the visualization unit can suggest routes that use wider corridors or elevators to avoid crowds. The visualization unit can also collect the user's biometric data (heart rate and electrodermal activity) using sensors and analyze their stress level using an emotion estimation algorithm. For example, it can calculate a stress score based on heart rate fluctuations. This allows the user to know routes and time periods that will reduce stress.

[0078] The visualization unit can propose an optimal travel route by taking into account the congestion status of commercial facilities around stations. The visualization unit, for example, collects congestion status of commercial facilities around stations in real time and proposes an optimal travel route. For example, it provides a route that avoids congestion at commercial facilities. The visualization unit can also propose an optimal travel route by taking into account the congestion status of the user's destination (office, school, etc.). For example, it provides a route that avoids congestion at offices and schools. This allows the user to know the optimal travel route that takes into account the congestion status of commercial facilities.

[0079] The visualization unit can propose an optimal travel route by taking into account the congestion situation at the user's destination. For example, the visualization unit collects information on the congestion situation at the user's destination in real time and proposes an optimal travel route. For example, it provides a route that avoids crowded offices or schools. The visualization unit can also propose optimal movement lines and time periods by taking into account the user's health condition (difficulty walking, pregnancy, etc.). For example, for a user who has difficulty walking, it can propose a route that uses elevators or escalators. This allows the user to know the optimal travel route that takes into account the congestion situation at the destination.

[0080] The visualization unit can use the emotion estimation function to optimize in-station guidance displays based on the user's emotions, thereby improving user satisfaction. For example, the visualization unit analyzes the user's facial expressions from camera footage to estimate their emotions. For example, it provides relevant guidance displays to users with positive emotions. The visualization unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the visualization unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This allows the user to receive optimal guidance displays based on their emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The congestion avoidance system can further include a voice guidance unit. The voice guidance unit provides real-time voice guidance based on the user's current location and destination. For example, when passing through a station ticket gate, it provides voice guidance on the optimal route to the platform. The voice guidance unit can also take into account the user's past movement history and provide voice guidance on the optimal individual flow line. For example, it provides voice guidance on routes to avoid congestion during specific time periods. This allows the user to receive voice guidance in addition to visual information.

[0083] The congestion avoidance system can further include an energy consumption analysis unit. The energy consumption analysis unit analyzes energy consumption associated with the user's movements and proposes optimal movement lines. For example, it proposes a route that reduces energy consumption by recommending the use of escalators and elevators. The energy consumption analysis unit can also propose a route that minimizes energy consumption by taking into account the user's health condition. For example, it proposes a route that uses elevators for users who have difficulty walking. This allows the user to know the optimal movement line that reduces energy consumption.

[0084] The congestion avoidance system can further include a barrier-free support unit. The barrier-free support unit takes into account the physical limitations of the user and proposes the optimal route. For example, it proposes a route that uses elevators or ramps for wheelchair users. The barrier-free support unit can also propose routes that take into account the location of tactile paving blocks for visually impaired people. For example, it provides a route that follows the tactile paving blocks. This allows the user to know the optimal route to follow their physical limitations.

[0085] The congestion avoidance system may further include an entertainment provider. The entertainment provider provides entertainment content to allow users to spend their waiting time effectively. For example, it may stream movies or music on station platforms or inside trains. The entertainment provider may also suggest content based on the user's preferences. For example, it may suggest movies or music that the user might be interested in based on their past viewing history. This allows users to spend their waiting time effectively.

[0086] The congestion avoidance system can further include a health management unit. The health management unit monitors the user's health condition and suggests the optimal route. For example, it measures the user's heart rate and blood pressure and suggests a route based on the user's health condition. The health management unit can also suggest rest points taking into account the user's health condition. For example, a user with a high heart rate can be guided to the location of a bench where the user can rest. This allows the user to know the optimal route based on their health condition.

[0087] The congestion avoidance system can also use emotion estimation functions to estimate a user's stress level in real time and suggest routes and time periods that will reduce stress. For example, it can analyze a user's facial expressions from camera footage to estimate their stress level. For users with high stress levels, it can suggest routes that use wider corridors or elevators to avoid crowds. It can also collect users' biometric data (heart rate and electrodermal activity) using sensors and analyze their stress level using emotion estimation algorithms. This allows users to know routes and time periods that will reduce stress.

[0088] The congestion avoidance system can also use emotion estimation to estimate the user's comfort level in real time and suggest a comfortable vehicle. For example, it can analyze the user's facial expressions from camera footage to estimate their comfort level. For users with a high comfort level, it can suggest an empty vehicle. It can also collect the user's biometric data (heart rate and electrodermal activity) using sensors and analyze their comfort level using an emotion estimation algorithm. This allows the user to know which vehicle is most comfortable.

[0089] The congestion avoidance system can also use emotion estimation to optimize the content of in-car announcements based on the user's emotions, improving user satisfaction. For example, the system can analyze the user's facial expressions from camera footage to estimate their emotions. For users with positive emotions, it can display relevant announcements. It can also record the user's voice and use voice analysis technology to estimate their emotions. This allows users to receive the most appropriate in-car announcements based on their emotions.

[0090] The congestion avoidance system can also use emotion estimation to optimize in-station guidance displays based on the user's emotions, improving user satisfaction. For example, the system can analyze the user's facial expressions from camera footage to estimate their emotions. Users with positive emotions can be provided with relevant guidance displays. It can also record the user's voice and use voice analysis technology to estimate their emotions. This allows users to receive optimal guidance displays based on their emotions.

[0091] The congestion avoidance system can also use emotion estimation functions to estimate a user's stress level in real time and suggest routes and time periods that will reduce stress. For example, it can analyze a user's facial expressions from camera footage to estimate their stress level. For users with high stress levels, it can suggest routes that use wider corridors or elevators to avoid crowds. It can also collect users' biometric data (heart rate and electrodermal activity) using sensors and analyze their stress level using emotion estimation algorithms. This allows users to know routes and time periods that will reduce stress.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The people flow analysis unit analyzes the flow of people at the station. For example, it analyzes video data obtained from cameras at station ticket gates and on platforms to identify crowded times and locations. The people flow analysis unit can also use AI to identify the movement patterns of individual users and suggest individual routes to avoid crowds. For example, it can track which ticket gate a specific user uses and which platform they head to, and suggest the optimal route to avoid crowds. Step 2: The congestion analysis unit analyzes the congestion situation on the train. For example, it analyzes data obtained from sensors and cameras installed in each car and identifies which cars are empty and which are crowded. The congestion analysis unit can also identify the user's seat selection patterns and suggest the most suitable seats. For example, it analyzes which seats are popular during a specific time period and suggests available seats. Step 3: The visualization unit visualizes the routes and time periods for avoiding crowds. For example, it may display routes for avoiding crowds on station platforms or suggest optimal times for avoiding crowds on the app. The visualization unit can also consider the user's past movement history and suggest optimal routes and time periods for each individual. For example, it may provide routes for avoiding crowds during specific times based on past data.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] 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.

[0120] 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.

[0121] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] 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.

[0136] 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.

[0137] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] 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.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] 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.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 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. The People Flow Analysis Department analyzes the flow of people at stations, A congestion analysis unit that analyzes the congestion situation on trains, A visualization unit that visualizes traffic flow and time periods to avoid congestion. A system characterized by:

2. The human flow analysis department is Analyzing video data obtained from cameras to identify busy times and locations 2. The system of claim 1.

3. The congestion analysis unit Analyzing data obtained from sensors or cameras installed in each vehicle to identify the empty or crowded vehicle.

2. The system of claim 1.

4. The visualization unit Displaying routes to avoid congestion on the platform 2. The system of claim 1.

5. The visualization unit The app will suggest the best time to avoid crowds 2. The system of claim 1.

6. The congestion analysis unit Identify user seat selection patterns and suggest optimal seats 2. The system of claim 1.

7. The visualization unit Taking into account the user's past movement history, the system proposes the optimal individual route and time period.

2. The system of claim 1.

8. The human flow analysis department is Estimates the user's stress level in real time and suggests a less stressful route 2. The system of claim 1.

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