System

Abstract

An object of a system according to an embodiment is to propose an optimal point or route for a taxi driver to efficiently catch a passenger.SOLUTION: A system according to an embodiment includes a people-flow data collection unit, an analysis unit, and a proposal unit. The human-flow data collector collects human-flow data. The analysis unit analyzes the human-flow data collected by the human-flow data collection unit. The suggestion unit suggests an optimal passenger capture point and route based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 has the problem that it is difficult for taxi drivers to find the optimal points and routes to efficiently catch passengers.

[0005] The system according to the embodiment aims to suggest optimal points and routes for taxi drivers to efficiently catch passengers. [Means for solving the problem]

[0006] The system according to the embodiment includes a people flow data collection unit, an analysis unit, and a proposal unit. The people flow data collection unit collects people flow data. The analysis unit analyzes the people flow data collected by the people flow data collection unit. The proposal unit proposes optimal passenger capture points and routes based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal points and routes for taxi drivers to efficiently catch passengers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The taxi driver assistance system according to an embodiment of the present invention is a system that collects people flow data, analyzes it using AI, and proposes optimal passenger capture points and routes. This allows taxi drivers to efficiently capture passengers.

[0029] The taxi driver assistance system according to the embodiment includes a people flow data collection unit, an analysis unit, and a proposal unit. The people flow data collection unit collects people flow data. For example, the people flow data collection unit collects GPS data. The people flow data collection unit can also collect Wi-Fi connection data. The people flow data collection unit can also collect camera video data. The analysis unit analyzes the people flow data collected by the people flow data collection unit. For example, the analysis unit analyzes the people flow data using data mining technology. The analysis unit can also analyze the people flow data using statistical analysis technology. The analysis unit can also analyze the people flow data using a machine learning algorithm. The proposal unit proposes optimal passenger capture points and routes based on the results of the analysis by the analysis unit. For example, the proposal unit proposes locations with many passengers. The proposal unit can also propose locations with good accessibility. The proposal unit can also propose routes that take traffic conditions into consideration. This enables the taxi driver assistance system according to the embodiment to efficiently capture passengers.

[0030] The analysis unit can analyze people flow data in downtown areas during specific time periods and identify locations where passengers are likely to gather. For example, the analysis unit collects information about specific events and predicts changes in people flow associated with the event. For example, it analyzes people flow data before and after the start of an event to identify times and locations where passengers are likely to gather. The analysis unit also collects weather data and predicts changes in people flow associated with weather changes. For example, since passenger numbers tend to increase at stations and bus stops during rainy weather, the analysis unit suggests passenger capture points based on this information. The analysis unit also analyzes people flow data collected in real time and updates the analysis results in response to changes in events and weather. For example, it dynamically adjusts passenger capture points in response to changes in circumstances such as sudden rain or the cancellation of an event. This makes it possible to identify passenger capture points in downtown areas during specific time periods.

[0031] The analysis unit can propose optimal routes taking into account real-time traffic conditions. For example, the analysis unit collects past crime data and proposes passenger capture points that avoid areas with high crime rates. For example, it avoids areas where crime is prevalent at night and prioritizes proposing safe locations. The analysis unit also collects local safety information and identifies areas with good public safety. For example, it considers the location of police stations and security cameras and proposes safe passenger capture points. The analysis unit also analyzes safety information in real time and immediately changes passenger capture points in the event of a sudden incident or accident. For example, it proposes to avoid the area immediately after an incident occurs. This makes it possible to propose optimal routes taking into account real-time traffic conditions.

[0032] The suggestion unit can suggest passenger capture points to new drivers to efficiently capture passengers. For example, the suggestion unit uses an emotion estimation function to analyze people's emotional states at specific locations and times of day, and prioritizes suggesting locations with a high percentage of positive emotions. For example, AI collects emotion data from social media and review sites and analyzes people's emotional states at specific locations and times of day. For example, the suggestion unit suggests locations with a high percentage of positive emotions as passenger capture points. The suggestion unit also uses the emotion estimation function to analyze reviews of events and stores, and identify locations with a high percentage of positive reviews. For example, the suggestion unit suggests locations near popular cafes and restaurants as passenger capture points. The suggestion unit also analyzes emotion data in real time and dynamically suggests locations with a high percentage of positive emotions. For example, if there is a high percentage of positive emotions near an event venue, the suggestion unit prioritizes suggesting those locations. This allows the suggestion unit to suggest locations for new drivers to efficiently capture passengers.

[0033] The analysis unit can predict passenger demand based on past people flow data and event information. For example, AI collects bus and train schedules and predicts when there will be a large number of passengers based on the arrival and departure times of public transportation. For example, it suggests passenger capture points around stations after the last train. The analysis unit also collects public transportation delay information in real time and identifies locations where there will be a large number of passengers when a delay occurs. For example, it suggests locations when an increase in passengers wait at a station due to a delay. The analysis unit also integrates public transportation operation data and people flow data to predict times and locations when there will be a large number of passengers. For example, it suggests passenger capture points around stations during rush hour. This makes it possible to predict passenger demand based on past data.

[0034] The analysis unit can predict fluctuations in pedestrian flow in response to specific events or changes in weather, and update the analysis results in real time. For example, the analysis unit collects information about specific events and predicts fluctuations in pedestrian flow associated with the event. For example, it analyzes pedestrian flow data before and after the start of the event to identify times and locations where many passengers will be present. The analysis unit also collects weather data and predicts fluctuations in pedestrian flow associated with changes in weather. For example, since the number of passengers at stations and bus stops tends to increase during rainy weather, the analysis unit suggests passenger capture points based on this information. The analysis unit also analyzes pedestrian flow data collected in real time and updates the analysis results in response to changes in events or weather. For example, it dynamically adjusts passenger capture points in response to changes in circumstances such as sudden rain or the cancellation of an event. This makes it possible to predict fluctuations in pedestrian flow in response to specific events or changes in weather, and update the analysis results in real time.

[0035] The analysis unit can identify safe passenger capture points by taking into account past crime data and safety information. For example, the analysis unit collects past crime data and suggests passenger capture points that avoid areas with high crime rates. For example, it avoids areas where crime is frequent at night and preferentially suggests safe locations. The analysis unit also collects local safety information and identifies areas with good public safety. For example, it takes into account the location of police stations and security cameras to suggest safe passenger capture points. The analysis unit also analyzes safety information in real time and immediately changes passenger capture points in the event of a sudden incident or accident. For example, it suggests avoiding an area immediately after an incident occurs. In this way, it is possible to identify safe passenger capture points by taking into account past crime data and safety information.

[0036] The analysis unit integrates public transportation operation data and can predict when there will be a large number of passengers. For example, AI collects bus and train schedules and predicts when there will be a large number of passengers based on the arrival and departure times of public transportation. For example, it suggests passenger collection points around stations after the last train. The analysis unit also collects public transportation delay information in real time and identifies locations where there will be a large number of passengers when a delay occurs. For example, if an increase in passengers causes a delay to wait at a station, it suggests such locations. The analysis unit also integrates public transportation operation data and people flow data to predict times and locations where there will be a large number of passengers. For example, it suggests passenger collection points around stations during rush hour. This makes it possible to integrate public transportation operation data and predict when there will be a large number of passengers.

[0037] The analysis unit incorporates business information from tourist destinations and commercial facilities, and can predict the trends of tourists and shoppers. The analysis unit, for example, collects business hours and event information from tourist destinations, and predicts the times and locations when many tourists will be present. For example, it suggests passenger capture points during tourist destination opening hours and when events are held. The analysis unit also collects business information from commercial facilities, and predicts the times and locations when many shoppers will be present. For example, it suggests passenger capture points around shopping malls during sales periods. The analysis unit also uses AI to integrate business information from tourist destinations and commercial facilities with people flow data, and predict the trends of tourists and shoppers. For example, it suggests passenger capture points during tourist seasons and sales periods. This makes it possible to incorporate business information from tourist destinations and commercial facilities, and predict the trends of tourists and shoppers.

[0038] The suggestion unit can learn from past successes and failures and improve the accuracy of its suggestions. For example, the suggestion unit uses AI to learn from past successes and failures of passenger capture points and improve the accuracy of its suggestions based on that data. For example, it prioritizes suggestions of points with a high success rate. The suggestion unit also analyzes past data and learns from successes at specific time periods and locations. For example, it suggests points that were successful when a specific event was held. The suggestion unit also learns from failures and makes suggestions to avoid similar failures. For example, it suggests avoiding places that have had few passengers in the past. In this way, it can learn from past successes and failures and improve the accuracy of its suggestions.

[0039] The suggestion unit can customize the optimal passenger capture point by taking into account the individual driving style and past performance of each driver. The suggestion unit, for example, collects the driving style and past performance of each driver and customizes the optimal passenger capture point based on that data. For example, it prioritizes suggesting areas in which a particular driver excels. The suggestion unit also analyzes the driver's past performance and suggests points with a high success rate. For example, it suggests locations where a particular driver has caught many passengers in the past. The suggestion unit also customizes the optimal point according to the driver's driving style. For example, it suggests areas with heavy traffic for a driver who is good at driving. In this way, the optimal point can be customized by taking into account the individual driving style and past performance of each driver.

[0040] The suggestion unit incorporates information about nearby restaurants and entertainment facilities and can identify locations where passengers are likely to gather. The suggestion unit, for example, collects information about nearby restaurants and entertainment facilities and identifies locations where passengers are likely to gather. For example, it suggests areas around popular restaurants and cafes as passenger capture points. The suggestion unit also uses AI to analyze business information about restaurants and entertainment facilities and predict times and locations where passengers will be in high numbers. For example, it suggests passenger capture points during dinner time or after movies. The suggestion unit also analyzes reviews of restaurants and entertainment facilities and identifies locations with many positive reviews. For example, it suggests areas around popular facilities as passenger capture points. This makes it possible to incorporate information about nearby restaurants and entertainment facilities and identify locations where passengers are likely to gather.

[0041] The suggestion unit can integrate local event calendars and identify locations where many passengers will gather when an event is held. The suggestion unit, for example, collects local event calendars and identifies locations where many passengers will gather when an event is held. For example, it suggests areas around concert or festival venues as passenger capture points. The suggestion unit also uses AI to integrate event calendars with people flow data and predict passenger trends when an event is held. For example, it suggests passenger capture points for the time periods before and after the start of an event. The suggestion unit also identifies locations where many passengers will gather depending on the type and scale of the event. For example, it suggests areas around venues for large-scale or popular events. In this way, it is possible to integrate local event calendars and identify locations where many passengers will gather when an event is held.

[0042] The suggestion unit can take past traffic accident data into consideration and prioritize suggest safer routes. For example, the suggestion unit uses AI to collect past traffic accident data and prioritize suggesting routes with a low accident rate. For example, it sets a route to avoid intersections and roads where accidents frequently occur. The suggestion unit also analyzes traffic accident data and proposes routes that take into consideration the risk of accidents occurring at specific times of the day or in specific weather conditions. For example, it proposes safer routes at night or in rainy weather. The suggestion unit also collects traffic accident information in real time and immediately changes the route if an accident occurs. For example, it reconfigures the route to avoid the accident site. This makes it possible to take past traffic accident data into consideration and prioritize suggesting safer routes.

[0043] The suggestion unit can suggest optimal rest points by taking into account the driver's driving history and fatigue level. For example, the suggestion unit collects the driver's driving history and suggests appropriate rest points when driving for long periods of time. For example, it suggests service areas and parking areas as rest points. The suggestion unit also monitors the driver's fatigue level in real time and encourages the driver to take a break if fatigue is accumulating. For example, it evaluates fatigue level based on driving time and heart rate and suggests rest points. The suggestion unit also uses AI to integrate the driver's driving history and fatigue level data and dynamically suggest optimal rest points. For example, it suggests taking a break at an appropriate time when driving long distances. This makes it possible to suggest optimal rest points by taking into account the driver's driving history and fatigue level.

[0044] The suggestion unit can incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists. For example, the suggestion unit collects information on tourist attractions and famous places and proposes optimal routes for tourists. For example, it proposes a route that passes through major tourist spots. The suggestion unit also uses AI to analyze the opening hours and event information of tourist attractions and famous places and propose routes that tourists can enjoy. For example, it proposes a route that coincides with the opening hours of a tourist attraction. The suggestion unit also analyzes reviews and ratings from tourists and proposes routes that pass through tourist attractions with many positive ratings. For example, it proposes a route that goes around popular tourist spots. This makes it possible to incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists.

[0045] The suggestion unit can integrate real-time weather information and propose the optimal route depending on the weather. For example, the suggestion unit collects real-time weather information and proposes the optimal route depending on the weather. For example, it proposes a route that avoids slippery roads when it is raining. The suggestion unit also analyzes weather data and proposes a route that takes safety into consideration under specific weather conditions. For example, it proposes a safe route on snowy roads or in strong winds. The suggestion unit also uses AI to integrate weather information and traffic data and dynamically propose the optimal route depending on the weather. For example, it reconfigures the route in response to sudden changes in weather. This makes it possible to integrate real-time weather information and propose the optimal route depending on the weather.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The suggestion unit can monitor the driver's health condition and suggest passenger capture points according to the health condition. For example, it can monitor the driver's heart rate and blood pressure in real time, and if an abnormality is detected, suggest nearby medical facilities or rest areas. The suggestion unit can also suggest routes to avoid long driving times based on the driver's health condition. For example, it can prioritize suggesting points where passengers can be captured in a short time. The suggestion unit can also suggest routes that put less strain on driving according to the driver's health condition. This makes it possible to suggest passenger capture points that take the driver's health condition into consideration.

[0048] The analysis unit can learn the driving style of each driver and suggest optimal passenger pickup points for each individual driver. For example, it can analyze past driving data to identify areas where a particular driver excels. The analysis unit can also suggest times and locations where there are many passengers based on the driver's driving style. For example, if a particular driver tends to pick up many passengers at night, it will suggest points that are suitable for those times. The analysis unit can also suggest optimal routes depending on the driver's driving style. This makes it possible to suggest passenger pickup points that take the driver's driving style into consideration.

[0049] The suggestion unit can suggest passenger capture points with a high success rate based on the driver's past performance. For example, it can prioritize suggestions of locations that have captured many passengers in the past. The suggestion unit can also analyze past data and learn successful cases at specific times and locations. For example, it can suggest points that were successful when a specific event was held. The suggestion unit can also learn from unsuccessful cases and make suggestions to avoid similar failures. For example, it can suggest avoiding locations that have had few passengers in the past. In this way, it can suggest passenger capture points based on past performance.

[0050] The suggestion unit can suggest optimal rest points by taking into account the driver's driving history and fatigue level. For example, it collects the driver's driving history and suggests appropriate rest points when driving for long periods of time. For example, it can suggest service areas or parking areas as rest points. The suggestion unit can also monitor the driver's fatigue level in real time and encourage them to take a break if fatigue is accumulating. For example, it can evaluate fatigue level based on driving time and heart rate and suggest rest points. The suggestion unit can also use AI to integrate the driver's driving history and fatigue level data and dynamically suggest optimal rest points. For example, it can suggest breaks at appropriate times when driving long distances. This makes it possible to suggest optimal rest points by taking into account the driver's driving history and fatigue level.

[0051] The suggestion unit can incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists. For example, it can collect information on tourist attractions and famous places and propose optimal routes for tourists. For example, it can propose routes that pass through major tourist spots. The suggestion unit also uses AI to analyze the opening hours and event information of tourist attractions and famous places and propose routes that tourists can enjoy. For example, it can propose routes that coincide with the opening hours of tourist attractions. The suggestion unit also analyzes reviews and ratings by tourists and proposes routes that pass through tourist attractions with many positive ratings. For example, it can propose routes that go around popular tourist spots. This makes it possible to incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The people flow data collection unit collects people flow data, such as GPS data, Wi-Fi connection data, and camera video data. Step 2: The analysis unit analyzes the people flow data collected by the people flow data collection unit. For example, the analysis can be performed using data mining techniques, statistical analysis techniques, or machine learning algorithms. Step 3: The proposal unit proposes optimal passenger capture points and routes based on the results of the analysis by the analysis unit. For example, it can propose routes that take into account locations with many passengers, locations with good access, and traffic conditions.

[0054] (Example 2) The taxi driver assistance system according to an embodiment of the present invention is a system that collects people flow data, analyzes it using AI, and proposes optimal passenger capture points and routes. This allows taxi drivers to efficiently capture passengers.

[0055] The taxi driver assistance system according to the embodiment includes a people flow data collection unit, an analysis unit, and a proposal unit. The people flow data collection unit collects people flow data. For example, the people flow data collection unit collects GPS data. The people flow data collection unit can also collect Wi-Fi connection data. The people flow data collection unit can also collect camera video data. The analysis unit analyzes the people flow data collected by the people flow data collection unit. For example, the analysis unit analyzes the people flow data using data mining technology. The analysis unit can also analyze the people flow data using statistical analysis technology. The analysis unit can also analyze the people flow data using a machine learning algorithm. The proposal unit proposes optimal passenger capture points and routes based on the results of the analysis by the analysis unit. For example, the proposal unit proposes locations with many passengers. The proposal unit can also propose locations with good accessibility. The proposal unit can also propose routes that take traffic conditions into consideration. This enables the taxi driver assistance system according to the embodiment to efficiently capture passengers.

[0056] The analysis unit can analyze people flow data in downtown areas during specific time periods and identify locations where passengers are likely to gather. For example, the analysis unit collects information about specific events and predicts changes in people flow associated with the event. For example, it analyzes people flow data before and after the start of an event to identify times and locations where passengers are likely to gather. The analysis unit also collects weather data and predicts changes in people flow associated with weather changes. For example, since passenger numbers tend to increase at stations and bus stops during rainy weather, the analysis unit suggests passenger capture points based on this information. The analysis unit also analyzes people flow data collected in real time and updates the analysis results in response to changes in events and weather. For example, it dynamically adjusts passenger capture points in response to changes in circumstances such as sudden rain or the cancellation of an event. This makes it possible to identify passenger capture points in downtown areas during specific time periods.

[0057] The analysis unit can propose optimal routes taking into account real-time traffic conditions. For example, the analysis unit collects past crime data and proposes passenger capture points that avoid areas with high crime rates. For example, it avoids areas where crime is prevalent at night and prioritizes proposing safe locations. The analysis unit also collects local safety information and identifies areas with good public safety. For example, it considers the location of police stations and security cameras and proposes safe passenger capture points. The analysis unit also analyzes safety information in real time and immediately changes passenger capture points in the event of a sudden incident or accident. For example, it proposes to avoid the area immediately after an incident occurs. This makes it possible to propose optimal routes taking into account real-time traffic conditions.

[0058] The suggestion unit can suggest passenger capture points to new drivers to efficiently capture passengers. For example, the suggestion unit uses an emotion estimation function to analyze people's emotional states at specific locations and times of day, and prioritizes suggesting locations with a high percentage of positive emotions. For example, AI collects emotion data from social media and review sites and analyzes people's emotional states at specific locations and times of day. For example, the suggestion unit suggests locations with a high percentage of positive emotions as passenger capture points. The suggestion unit also uses the emotion estimation function to analyze reviews of events and stores, and identify locations with a high percentage of positive reviews. For example, the suggestion unit suggests locations near popular cafes and restaurants as passenger capture points. The suggestion unit also analyzes emotion data in real time and dynamically suggests locations with a high percentage of positive emotions. For example, if there is a high percentage of positive emotions near an event venue, the suggestion unit prioritizes suggesting those locations. This allows the suggestion unit to suggest locations for new drivers to efficiently capture passengers.

[0059] The analysis unit can predict passenger demand based on past people flow data and event information. For example, AI collects bus and train schedules and predicts when there will be a large number of passengers based on the arrival and departure times of public transportation. For example, it suggests passenger capture points around stations after the last train. The analysis unit also collects public transportation delay information in real time and identifies locations where there will be a large number of passengers when a delay occurs. For example, it suggests locations when an increase in passengers wait at a station due to a delay. The analysis unit also integrates public transportation operation data and people flow data to predict times and locations when there will be a large number of passengers. For example, it suggests passenger capture points around stations during rush hour. This makes it possible to predict passenger demand based on past data.

[0060] The analysis unit can predict fluctuations in pedestrian flow in response to specific events or changes in weather, and update the analysis results in real time. For example, the analysis unit collects information about specific events and predicts fluctuations in pedestrian flow associated with the event. For example, it analyzes pedestrian flow data before and after the start of the event to identify times and locations where many passengers will be present. The analysis unit also collects weather data and predicts fluctuations in pedestrian flow associated with changes in weather. For example, since the number of passengers at stations and bus stops tends to increase during rainy weather, the analysis unit suggests passenger capture points based on this information. The analysis unit also analyzes pedestrian flow data collected in real time and updates the analysis results in response to changes in events or weather. For example, it dynamically adjusts passenger capture points in response to changes in circumstances such as sudden rain or the cancellation of an event. This makes it possible to predict fluctuations in pedestrian flow in response to specific events or changes in weather, and update the analysis results in real time.

[0061] The analysis unit can identify safe passenger capture points by taking into account past crime data and safety information. For example, the analysis unit collects past crime data and suggests passenger capture points that avoid areas with high crime rates. For example, it avoids areas where crime is frequent at night and preferentially suggests safe locations. The analysis unit also collects local safety information and identifies areas with good public safety. For example, it takes into account the location of police stations and security cameras to suggest safe passenger capture points. The analysis unit also analyzes safety information in real time and immediately changes passenger capture points in the event of a sudden incident or accident. For example, it suggests avoiding an area immediately after an incident occurs. In this way, it is possible to identify safe passenger capture points by taking into account past crime data and safety information.

[0062] The analysis unit uses the emotion estimation function to analyze people's emotional state at specific locations and times of day, and can prioritize suggesting locations with a high percentage of positive emotions. For example, the analysis unit uses AI to collect emotional data from social media and review sites and analyze people's emotional state at specific locations and times of day. For example, it can suggest locations with a high percentage of positive emotions as passenger capture points. The analysis unit also uses the emotion estimation function to analyze reviews of events and stores and identify locations with a high number of positive reviews. For example, it can suggest locations around popular cafes and restaurants as passenger capture points. The analysis unit also analyzes emotional data in real time and dynamically suggests locations with a high percentage of positive emotions. For example, if there is a high percentage of positive emotions around an event venue, it will prioritize suggesting those locations. This allows it to prioritize suggesting locations with a high percentage of positive emotions.

[0063] The analysis unit integrates public transportation operation data and can predict when there will be a large number of passengers. For example, AI collects bus and train schedules and predicts when there will be a large number of passengers based on the arrival and departure times of public transportation. For example, it suggests passenger collection points around stations after the last train. The analysis unit also collects public transportation delay information in real time and identifies locations where there will be a large number of passengers when a delay occurs. For example, if an increase in passengers causes a delay to wait at a station, it suggests such locations. The analysis unit also integrates public transportation operation data and people flow data to predict times and locations where there will be a large number of passengers. For example, it suggests passenger collection points around stations during rush hour. This makes it possible to integrate public transportation operation data and predict when there will be a large number of passengers.

[0064] The analysis unit incorporates business information from tourist destinations and commercial facilities, and can predict the trends of tourists and shoppers. The analysis unit, for example, collects business hours and event information from tourist destinations, and predicts the times and locations when many tourists will be present. For example, it suggests passenger capture points during tourist destination opening hours and when events are held. The analysis unit also collects business information from commercial facilities, and predicts the times and locations when many shoppers will be present. For example, it suggests passenger capture points around shopping malls during sales periods. The analysis unit also uses AI to integrate business information from tourist destinations and commercial facilities with people flow data, and predict the trends of tourists and shoppers. For example, it suggests passenger capture points during tourist seasons and sales periods. This makes it possible to incorporate business information from tourist destinations and commercial facilities, and predict the trends of tourists and shoppers.

[0065] The analysis unit uses the emotion estimation function to analyze people's emotional reactions to specific events and locations, and can prioritize suggesting locations with a high level of positive emotions. For example, the analysis unit uses AI to collect emotional data from social media and review sites and analyze people's emotional reactions to specific events and locations. For example, it suggests areas around event venues with a high level of positive emotions as passenger capture points. The analysis unit also uses the emotion estimation function to analyze reviews of events and stores and identify locations with a high level of positive reviews. For example, it suggests areas around popular cafes and restaurants as passenger capture points. The analysis unit also analyzes emotional data in real time and dynamically suggests locations with a high level of positive emotions. For example, if there is a high level of positive emotions around an event venue, it will prioritize suggesting those locations. This makes it possible to analyze people's emotional reactions to specific events and locations, and prioritize suggesting locations with a high level of positive emotions.

[0066] The suggestion unit can learn from past successes and failures and improve the accuracy of its suggestions. For example, the suggestion unit uses AI to learn from past successes and failures of passenger capture points and improve the accuracy of its suggestions based on that data. For example, it prioritizes suggestions of points with a high success rate. The suggestion unit also analyzes past data and learns from successes at specific time periods and locations. For example, it suggests points that were successful when a specific event was held. The suggestion unit also learns from failures and makes suggestions to avoid similar failures. For example, it suggests avoiding places that have had few passengers in the past. In this way, it can learn from past successes and failures and improve the accuracy of its suggestions.

[0067] The suggestion unit can customize the optimal passenger capture point by taking into account the individual driving style and past performance of each driver. The suggestion unit, for example, collects the driving style and past performance of each driver and customizes the optimal passenger capture point based on that data. For example, it prioritizes suggesting areas in which a particular driver excels. The suggestion unit also analyzes the driver's past performance and suggests points with a high success rate. For example, it suggests locations where a particular driver has caught many passengers in the past. The suggestion unit also customizes the optimal point according to the driver's driving style. For example, it suggests areas with heavy traffic for a driver who is good at driving. In this way, the optimal point can be customized by taking into account the individual driving style and past performance of each driver.

[0068] The suggestion unit can use the emotion estimation function to preferentially suggest places where passengers feel positive emotions. For example, the suggestion unit uses the emotion estimation function to identify places where passengers feel positive emotions and preferentially suggest those places. For example, it can suggest places around popular tourist spots and restaurants. The suggestion unit also uses AI to collect emotion data from social media and review sites and identify places with many positive reviews. For example, it can suggest places with many positive emotions as passenger capture points. The suggestion unit also analyzes emotion data in real time and dynamically suggests places with many positive emotions. For example, if there is a lot of positive emotion around a venue where an event is being held, it will preferentially suggest those places. This makes it possible to preferentially suggest places where passengers feel positive emotions.

[0069] The suggestion unit incorporates information about nearby restaurants and entertainment facilities and can identify locations where passengers are likely to gather. The suggestion unit, for example, collects information about nearby restaurants and entertainment facilities and identifies locations where passengers are likely to gather. For example, it suggests areas around popular restaurants and cafes as passenger capture points. The suggestion unit also uses AI to analyze business information about restaurants and entertainment facilities and predict times and locations where passengers will be in high numbers. For example, it suggests passenger capture points during dinner time or after movies. The suggestion unit also analyzes reviews of restaurants and entertainment facilities and identifies locations with many positive reviews. For example, it suggests areas around popular facilities as passenger capture points. This makes it possible to incorporate information about nearby restaurants and entertainment facilities and identify locations where passengers are likely to gather.

[0070] The suggestion unit can integrate local event calendars and identify locations where many passengers will gather when an event is held. The suggestion unit, for example, collects local event calendars and identifies locations where many passengers will gather when an event is held. For example, it suggests areas around concert or festival venues as passenger capture points. The suggestion unit also uses AI to integrate event calendars with people flow data and predict passenger trends when an event is held. For example, it suggests passenger capture points for the time periods before and after the start of an event. The suggestion unit also identifies locations where many passengers will gather depending on the type and scale of the event. For example, it suggests areas around venues for large-scale or popular events. In this way, it is possible to integrate local event calendars and identify locations where many passengers will gather when an event is held.

[0071] The suggestion unit can use the emotion estimation function to analyze passengers' emotional responses at specific locations and prioritize suggesting locations with a high percentage of positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze passengers' emotional responses at specific locations and identify locations with a high percentage of positive emotions. For example, it can suggest locations near popular tourist spots and restaurants. The suggestion unit also uses AI to collect emotional data from social media and review sites and identify locations with a high percentage of positive reviews. For example, it can suggest locations with a high percentage of positive emotions as passenger capture points. The suggestion unit also analyzes emotional data in real time and dynamically suggests locations with a high percentage of positive emotions. For example, if there is a high percentage of positive emotions around an event venue, it will prioritize suggesting that location. This makes it possible to analyze passengers' emotional responses at specific locations and prioritize suggesting locations with a high percentage of positive emotions.

[0072] The suggestion unit can take past traffic accident data into consideration and prioritize suggest safer routes. For example, the suggestion unit uses AI to collect past traffic accident data and prioritize suggesting routes with a low accident rate. For example, it sets a route to avoid intersections and roads where accidents frequently occur. The suggestion unit also analyzes traffic accident data and proposes routes that take into consideration the risk of accidents occurring at specific times of the day or in specific weather conditions. For example, it proposes safer routes at night or in rainy weather. The suggestion unit also collects traffic accident information in real time and immediately changes the route if an accident occurs. For example, it reconfigures the route to avoid the accident site. This makes it possible to take past traffic accident data into consideration and prioritize suggesting safer routes.

[0073] The suggestion unit can suggest optimal rest points by taking into account the driver's driving history and fatigue level. For example, the suggestion unit collects the driver's driving history and suggests appropriate rest points when driving for long periods of time. For example, it suggests service areas and parking areas as rest points. The suggestion unit also monitors the driver's fatigue level in real time and encourages the driver to take a break if fatigue is accumulating. For example, it evaluates fatigue level based on driving time and heart rate and suggests rest points. The suggestion unit also uses AI to integrate the driver's driving history and fatigue level data and dynamically suggest optimal rest points. For example, it suggests taking a break at an appropriate time when driving long distances. This makes it possible to suggest optimal rest points by taking into account the driver's driving history and fatigue level.

[0074] The suggestion unit can use the emotion estimation function to preferentially suggest routes that evoke positive emotions in passengers. The suggestion unit, for example, uses the emotion estimation function to identify routes that evoke positive emotions in passengers and preferentially suggest those routes. For example, it can suggest scenic routes or routes that pass through tourist spots. The suggestion unit also uses AI to collect emotion data from social media and review sites and identify routes with many positive reviews. For example, it can suggest routes that will satisfy passengers. The suggestion unit also analyzes emotion data in real time and dynamically suggests routes with many positive emotions. For example, if a route where an event is being held has many positive emotions, it will preferentially suggest that route. This makes it possible to preferentially suggest routes that evoke positive emotions in passengers.

[0075] The suggestion unit can incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists. For example, the suggestion unit collects information on tourist attractions and famous places and proposes optimal routes for tourists. For example, it proposes a route that passes through major tourist spots. The suggestion unit also uses AI to analyze the opening hours and event information of tourist attractions and famous places and propose routes that tourists can enjoy. For example, it proposes a route that coincides with the opening hours of a tourist attraction. The suggestion unit also analyzes reviews and ratings from tourists and proposes routes that pass through tourist attractions with many positive ratings. For example, it proposes a route that goes around popular tourist spots. This makes it possible to incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists.

[0076] The suggestion unit can integrate real-time weather information and propose the optimal route depending on the weather. For example, the suggestion unit collects real-time weather information and proposes the optimal route depending on the weather. For example, it proposes a route that avoids slippery roads when it is raining. The suggestion unit also analyzes weather data and proposes a route that takes safety into consideration under specific weather conditions. For example, it proposes a safe route on snowy roads or in strong winds. The suggestion unit also uses AI to integrate weather information and traffic data and dynamically propose the optimal route depending on the weather. For example, it reconfigures the route in response to sudden changes in weather. This makes it possible to integrate real-time weather information and propose the optimal route depending on the weather.

[0077] The suggestion unit can use the emotion estimation function to analyze passengers' emotional responses on a specific route and prioritize suggesting routes with a high percentage of positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze passengers' emotional responses on a specific route and identify routes with a high percentage of positive emotions. For example, it can suggest routes with beautiful scenery or routes that pass through tourist spots. The suggestion unit also uses AI to collect emotion data from social media and review sites and identify routes with a high number of positive reviews. For example, it can suggest routes that will satisfy passengers. The suggestion unit also analyzes emotion data in real time and dynamically suggests routes with a high percentage of positive emotions. For example, if a route where an event is being held has a high percentage of positive emotions, it can prioritize suggesting that route. This makes it possible to analyze passengers' emotional responses on a specific route and prioritize suggesting routes with a high percentage of positive emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The suggestion unit can monitor the driver's health condition and suggest passenger capture points according to the health condition. For example, it can monitor the driver's heart rate and blood pressure in real time, and if an abnormality is detected, suggest nearby medical facilities or rest areas. The suggestion unit can also suggest routes to avoid long driving times based on the driver's health condition. For example, it can prioritize suggesting points where passengers can be captured in a short time. The suggestion unit can also suggest routes that put less strain on driving according to the driver's health condition. This makes it possible to suggest passenger capture points that take the driver's health condition into consideration.

[0080] The analysis unit can learn the driving style of each driver and suggest optimal passenger pickup points for each individual driver. For example, it can analyze past driving data to identify areas where a particular driver excels. The analysis unit can also suggest times and locations where there are many passengers based on the driver's driving style. For example, if a particular driver tends to pick up many passengers at night, it will suggest points that are suitable for those times. The analysis unit can also suggest optimal routes depending on the driver's driving style. This makes it possible to suggest passenger pickup points that take the driver's driving style into consideration.

[0081] The suggestion unit can suggest passenger capture points with a high success rate based on the driver's past performance. For example, it can prioritize suggestions of locations that have captured many passengers in the past. The suggestion unit can also analyze past data and learn successful cases at specific times and locations. For example, it can suggest points that were successful when a specific event was held. The suggestion unit can also learn from unsuccessful cases and make suggestions to avoid similar failures. For example, it can suggest avoiding locations that have had few passengers in the past. In this way, it can suggest passenger capture points based on past performance.

[0082] The suggestion unit can suggest optimal rest points by taking into account the driver's driving history and fatigue level. For example, it collects the driver's driving history and suggests appropriate rest points when driving for long periods of time. For example, it can suggest service areas or parking areas as rest points. The suggestion unit can also monitor the driver's fatigue level in real time and encourage them to take a break if fatigue is accumulating. For example, it can evaluate fatigue level based on driving time and heart rate and suggest rest points. The suggestion unit can also use AI to integrate the driver's driving history and fatigue level data and dynamically suggest optimal rest points. For example, it can suggest breaks at appropriate times when driving long distances. This makes it possible to suggest optimal rest points by taking into account the driver's driving history and fatigue level.

[0083] The suggestion unit can incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists. For example, it can collect information on tourist attractions and famous places and propose optimal routes for tourists. For example, it can propose routes that pass through major tourist spots. The suggestion unit also uses AI to analyze the opening hours and event information of tourist attractions and famous places and propose routes that tourists can enjoy. For example, it can propose routes that coincide with the opening hours of tourist attractions. The suggestion unit also analyzes reviews and ratings by tourists and proposes routes that pass through tourist attractions with many positive ratings. For example, it can propose routes that go around popular tourist spots. This makes it possible to incorporate routes that pass through tourist attractions and famous places and propose optimal routes for tourists.

[0084] The suggestion unit can use the emotion estimation function to prioritize suggesting locations where passengers feel positive emotions. For example, the emotion estimation function can be used to identify locations where passengers feel positive emotions and prioritize suggesting those locations. For example, it can suggest locations near popular tourist spots and restaurants. The suggestion unit also uses AI to collect emotion data from social media and review sites to identify locations with many positive reviews. For example, it can suggest locations with many positive emotions as passenger capture points. The suggestion unit also analyzes emotion data in real time and dynamically suggests locations with many positive emotions. For example, if there is a lot of positive emotion near an event venue, it will prioritize suggesting those locations. This allows the system to prioritize suggesting locations where passengers feel positive emotions.

[0085] The suggestion unit can use the emotion estimation function to analyze passengers' emotional responses on a specific route and prioritize suggesting routes with a high percentage of positive emotions. For example, the emotion estimation function can be used to analyze passengers' emotional responses on a specific route and identify routes with a high percentage of positive emotions. For example, it can suggest routes with beautiful scenery or routes that pass through tourist spots. The suggestion unit also uses AI to collect emotional data from social media and review sites and identify routes with a high number of positive reviews. For example, it can suggest routes that will satisfy passengers. The suggestion unit also analyzes emotional data in real time and dynamically suggests routes with a high percentage of positive emotions. For example, if a route where an event is being held has a high percentage of positive emotions, it will prioritize suggesting that route. This makes it possible to analyze passengers' emotional responses on a specific route and prioritize suggesting routes with a high percentage of positive emotions.

[0086] The suggestion unit uses the emotion estimation function to analyze people's emotional reactions to specific events and locations, and can prioritize suggesting locations with a high level of positive emotions. For example, AI collects emotional data from social media and review sites and analyzes people's emotional reactions to specific events and locations. For example, it can suggest areas around event venues with a high level of positive emotions as passenger capture points. The suggestion unit also uses the emotion estimation function to analyze reviews of events and stores and identify locations with a high level of positive reviews. For example, it can suggest areas around popular cafes and restaurants as passenger capture points. The suggestion unit also analyzes emotional data in real time and dynamically suggests locations with a high level of positive emotions. For example, if there is a high level of positive emotions around an event venue, it will prioritize suggesting those locations. This makes it possible to analyze people's emotional reactions to specific events and locations, and prioritize suggesting locations with a high level of positive emotions.

[0087] The suggestion unit can use the emotion estimation function to preferentially suggest routes that evoke positive emotions among passengers. For example, the emotion estimation function can be used to identify routes that evoke positive emotions among passengers and preferentially suggest those routes. For example, it can suggest routes with beautiful scenery or routes that pass through tourist spots. The suggestion unit also uses AI to collect emotion data from social media and review sites to identify routes with many positive reviews. For example, it can suggest routes that will satisfy passengers. The suggestion unit also analyzes emotion data in real time and dynamically suggests routes with many positive emotions. For example, if a route where an event is being held evokes many positive emotions, it will preferentially suggest that route. This makes it possible to preferentially suggest routes that evoke positive emotions among passengers.

[0088] The suggestion unit uses the emotion estimation function to analyze people's emotional state at specific locations and times of day, and can prioritize suggesting locations with a high percentage of positive emotions. For example, AI collects emotional data from social media and review sites and analyzes people's emotional state at specific locations and times of day. For example, it can suggest locations with a high percentage of positive emotions as passenger capture points. The suggestion unit also uses the emotion estimation function to analyze reviews of events and stores and identify locations with a high percentage of positive reviews. For example, it can suggest locations near popular cafes and restaurants as passenger capture points. The suggestion unit also analyzes emotional data in real time and dynamically suggests locations with a high percentage of positive emotions. For example, if there is a high percentage of positive emotions around an event venue, it will prioritize suggesting those locations. This makes it possible to analyze people's emotional state at specific locations and times of day, and prioritize suggesting locations with a high percentage of positive emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The people flow data collection unit collects people flow data, such as GPS data, Wi-Fi connection data, and camera video data. Step 2: The analysis unit analyzes the people flow data collected by the people flow data collection unit. For example, the analysis can be performed using data mining techniques, statistical analysis techniques, or machine learning algorithms. Step 3: The proposal unit proposes optimal passenger capture points and routes based on the results of the analysis by the analysis unit. For example, it can propose routes that take into account locations with many passengers, locations with good access, and traffic conditions.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0127] The 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.

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

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0131] 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.

[0132] 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a people flow data collection unit that collects people flow data; an analysis unit that analyzes the people flow data collected by the people flow data collection unit; a proposal unit that proposes optimal passenger capture points and routes based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Analyzing the pedestrian flow data in the downtown area during a specific time period and identifying locations where passengers are most likely to be present 2. The system of claim 1.

3. The analysis unit Propose the best route considering real-time traffic conditions 2. The system of claim 1.

4. The proposal unit Propose passenger capture points to new drivers to efficiently capture passengers.

2. The system of claim 1.

5. The analysis unit Predict passenger demand based on past pedestrian flow data and event information 2. The system of claim 1.

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