System

Abstract

An object of a system according to an embodiment is to organize mobility data and realize demand prediction and dynamic pricing.SOLUTION: A system according to an embodiment includes a mobility data collection unit, a data arrangement unit, a demand prediction unit, and a dynamic pricing unit. The mobility data collection unit collects mobility data. The data organization unit organizes the mobility data collected by the mobility data collection unit. The demand prediction unit performs demand prediction based on the mobility data organized by the data organization unit. The dynamic pricing unit sets a price based on the demand forecast data generated by the demand forecasting 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 technologies have not yet fully utilized mobility data effectively to perform demand forecasting and dynamic pricing, and there is room for improvement.

[0005] The system according to the embodiment aims to organize mobility data and realize demand forecasting and dynamic pricing. [Means for solving the problem]

[0006] The system according to the embodiment includes a mobility data collection unit, a data reduction unit, a demand forecasting unit, and a dynamic pricing unit. The mobility data collection unit collects mobility data. The data reduction unit reduces the mobility data collected by the mobility data collection unit. The demand forecasting unit performs demand forecasting based on the mobility data reduced by the data reduction unit. The dynamic pricing unit sets prices based on the demand forecast data generated by the demand forecasting unit. [Effects of the Invention]

[0007] The system according to the embodiment can organize mobility data and realize demand forecasting and dynamic pricing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A mobility data analysis system according to an embodiment of the present invention is a system that organizes and analyzes mobility data to realize demand forecasting and dynamic pricing. As a result, the mobility data analysis system can sell demand forecast data and generate revenue.

[0029] A mobility data analysis system according to an embodiment includes a mobility data collection unit, a data reduction unit, a demand forecasting unit, and a dynamic pricing unit. The mobility data collection unit collects mobility data. For example, it collects taxi boarding and alighting data. The mobility data collection unit can also collect public transportation usage data. The mobility data collection unit can also collect GPS data. For example, taxi boarding and alighting data is obtained from a taxi operation management system. Public transportation usage data is obtained from a transportation card usage history. GPS data is obtained from smartphone location information. The data reduction unit organizes the mobility data collected by the mobility data collection unit. For example, the data reduction unit normalizes the data. The data reduction unit can also filter the data. The data reduction unit can also classify the data. For example, data normalization is a process of standardizing the data format. Data filtering is a process of removing unnecessary data. Data classification is a process of dividing data into specific categories. The demand forecasting unit performs demand forecasting based on the mobility data organized by the data reduction unit. For example, the demand forecasting unit predicts traffic demand in a specific time period or area. The demand forecasting unit can also perform demand forecasts based on the algorithm used. The demand forecasting unit can also evaluate the accuracy of the forecast. For example, traffic demand in a specific time period or area is predicted based on past data. The algorithm used is a machine learning algorithm. The accuracy of the forecast is evaluated by comparing actual data with predicted data. The dynamic pricing unit sets prices based on the demand forecast data generated by the demand forecasting unit. For example, the dynamic pricing unit raises fares during times of high demand. The dynamic pricing unit can also lower fares during times of low demand. The dynamic pricing unit can also fluctuate prices according to the balance between supply and demand. For example, profits are maximized by raising fares during times of high demand. Users are increased by lowering fares during times of low demand. Prices fluctuate according to the balance between supply and demand based on the demand forecast data.As a result, the mobility data analysis system according to the embodiment can organize and analyze mobility data to realize demand forecasting and dynamic pricing. For example, operators of ride-sharing services can create efficient operation plans based on demand forecast data. In addition, new revenue sources can be secured by selling demand forecast data. Furthermore, application of demand forecast data can enable effective measures to be implemented in fields such as public transportation and urban planning.

[0030] The mobility data collection unit can include real-time data from drones and autonomous vehicles. For example, the mobility data collection unit uses drones to monitor traffic conditions throughout a city in real time and collect mobility data. For example, it analyzes video captured by drones to detect traffic congestion and accidents. The mobility data collection unit also collects sensor data from autonomous vehicles to understand traffic conditions in real time. For example, it analyzes vehicle location information, speed, and surrounding obstacle information to predict traffic patterns. The mobility data collection unit also integrates data from drones and autonomous vehicles to build a system that analyzes mobility data throughout a city in real time. For example, it integrates multiple data sources to optimize traffic flow. This enables the collection of a wider range of mobility data by including real-time data from drones and autonomous vehicles.

[0031] The mobility data collection unit can incorporate an anomaly detection algorithm to automatically identify and exclude abnormal movement patterns. For example, the mobility data collection unit uses an anomaly detection algorithm to automatically identify abnormal movement patterns from mobility data. For example, it detects sudden changes in direction or speed that differ from normal movement patterns. The mobility data collection unit also builds a system that detects abnormal movement patterns in real time and excludes them from the data. For example, it issues an alert and filters the data when an abnormal movement pattern is detected. The mobility data collection unit also uses an anomaly detection algorithm to improve the quality of mobility data. For example, excluding abnormal data allows for more accurate demand forecasting. This improves data quality by automatically identifying and excluding abnormal movement patterns.

[0032] The mobility data collection unit can analyze tourist movement patterns limited to tourist destinations and event venues. The mobility data collection unit, for example, collects mobility data at tourist destinations and event venues and analyzes tourist movement patterns. For example, it identifies movement routes and stay times between major tourist spots. The mobility data collection unit also collects tourist movement data during a specific event period and analyzes the impact of the event. For example, it analyzes changes in traffic volume and movement patterns before and after the event. The mobility data collection unit also builds a system that predicts tourist behavior patterns based on mobility data at tourist destinations and event venues. For example, it predicts congestion and suggests optimal movement routes. As a result, by collecting data limited to tourist destinations and event venues and analyzing tourist movement patterns, it becomes possible to predict demand at specific locations.

[0033] The mobility data collection unit integrates smart city infrastructure data to understand the movements of the entire city. For example, the mobility data collection unit integrates smart city infrastructure data with mobility data to build a system that understands the movements of the entire city in real time. For example, it analyzes data on the lighting status of streetlights and the movement of garbage trucks. The mobility data collection unit also uses infrastructure data to develop algorithms that predict the movements of the city. For example, it predicts traffic volume and the flow of people based on streetlight lighting patterns and garbage collection routes. The mobility data collection unit also integrates smart city infrastructure data with mobility data to use in urban planning and traffic management. For example, it predicts traffic congestion and optimizes public transportation operation plans. In this way, by integrating smart city infrastructure data, it is possible to understand the movements of the entire city in real time.

[0034] The demand forecasting unit can make more precise predictions by taking into account external factors such as seasons, weather, and events. The demand forecasting unit, for example, incorporates seasonal and weather data into the demand forecasting algorithm to make more precise predictions that take external factors into account. For example, it predicts traffic demand during rainy weather and seasonal travel patterns. The demand forecasting unit also incorporates event information into the demand forecasting algorithm to make demand forecasts during specific event periods. For example, it predicts traffic demand during concerts and sporting events. The demand forecasting unit also builds a demand forecasting system that takes external factors into account and predicts demand based on seasonal, weather, and event information. For example, it utilizes weather forecast data and event calendars. This makes it possible to make more precise demand forecasts by taking into account external factors such as seasons, weather, and events.

[0035] The demand forecasting unit can make more precise predictions by taking into account external factors such as seasons, weather, and events. The demand forecasting unit, for example, incorporates seasonal and weather data into the demand forecasting algorithm to make more precise predictions that take external factors into account. For example, it predicts traffic demand during rainy weather and seasonal travel patterns. The demand forecasting unit also incorporates event information into the demand forecasting algorithm to make demand forecasts during specific event periods. For example, it predicts traffic demand during concerts and sporting events. The demand forecasting unit also builds a demand forecasting system that takes external factors into account and predicts demand based on seasonal, weather, and event information. For example, it utilizes weather forecast data and event calendars. This makes it possible to make more precise demand forecasts by taking into account external factors such as seasons, weather, and events.

[0036] The demand forecasting unit can incorporate not only past data but also future plans. For example, the demand forecasting unit incorporates future transportation infrastructure plans into the demand forecasting algorithm to predict future demand. For example, it performs demand forecasts that take into account construction plans for new railways and roads. The demand forecasting unit also incorporates future planning data into the demand forecasting algorithm to build a system that performs long-term demand forecasts. For example, it predicts demand based on urban development plans and plans to introduce new public transportation systems. The demand forecasting unit also integrates past data and future planning data to improve the demand forecasting algorithm. For example, it predicts demand by combining past travel patterns with new infrastructure plans. In this way, by incorporating past data and future plans, long-term demand forecasts become possible.

[0037] The demand forecasting unit can perform demand forecasting specialized for a specific industry. For example, the demand forecasting unit develops a demand forecasting algorithm specialized for the logistics industry to predict demand in the logistics industry. For example, it predicts demand for delivery routes and warehouses. The demand forecasting unit also builds a demand forecasting system specialized for the tourism industry to predict tourist movement patterns and demand. For example, it predicts congestion at tourist destinations and demand for accommodation facilities. The demand forecasting unit also develops a demand forecasting algorithm specialized for a specific industry to precisely predict demand for each industry. For example, it predicts demand in the medical industry or the education industry. In this way, by performing demand forecasting specialized for a specific industry, it is possible to precisely predict demand for each industry.

[0038] The demand forecasting unit can update the demand forecast data in real time, enabling it to respond to sudden fluctuations in demand. The demand forecasting unit, for example, updates the demand forecast data in real time and builds a system that can respond to sudden fluctuations in demand. For example, it immediately reflects demand fluctuations due to traffic accidents or natural disasters. The demand forecasting unit also updates the demand forecasting algorithm based on real-time data and responds quickly to demand fluctuations. For example, it utilizes real-time traffic data and weather data. The demand forecasting unit also develops a system that monitors the demand forecast data in real time and corrects the forecast as necessary to respond to sudden fluctuations in demand. For example, it responds to sudden cancellations or changes to events. In this way, by updating the demand forecast data in real time, it is possible to respond quickly to sudden fluctuations in demand.

[0039] The dynamic pricing department can incorporate competitors' price trends in real time and set competitive prices. For example, the dynamic pricing department collects competitors' price trends in real time and incorporates them into the dynamic pricing algorithm. For example, the department adjusts its own prices in response to competitors' price fluctuations. The dynamic pricing department also builds a system for setting competitive prices based on competitors' price data. For example, it monitors competitors' prices in real time and sets optimal prices. The dynamic pricing department also analyzes competitors' price trends in real time and improves the dynamic pricing algorithm. For example, it can respond quickly to competitors' price fluctuations. This makes it possible to set competitive prices by incorporating competitors' price trends in real time.

[0040] The dynamic pricing unit can set individually optimized prices by taking into account the user's past usage history and loyalty programs. The dynamic pricing unit, for example, builds a system that provides individually optimized prices based on the user's past usage history. For example, it provides discounted prices to users who use the service frequently. The dynamic pricing unit also sets different prices for each user by taking into account the loyalty program. For example, it provides special prices to users with high loyalty points. The dynamic pricing unit also integrates the user's past usage history and loyalty programs to develop an algorithm that provides individually optimized prices. For example, it adjusts prices according to frequency of use and loyalty points. This makes it possible to set individually optimized prices by taking into account the user's past usage history and loyalty programs.

[0041] The dynamic pricing department can also apply this to other services to build a comprehensive pricing strategy. For example, the dynamic pricing department applies dynamic pricing to hotel and restaurant reservations to develop a system that builds a comprehensive pricing strategy. For example, it adjusts accommodation prices and meal prices according to demand. The dynamic pricing department also develops dynamic pricing algorithms that work in conjunction with other services to realize a comprehensive pricing strategy. For example, it optimizes the price of the entire travel package. The dynamic pricing department also applies dynamic pricing based on hotel and restaurant reservation data to build a comprehensive pricing strategy. For example, it raises prices during periods of high demand and lowers prices during periods of low demand. In this way, a comprehensive pricing strategy can be built by applying dynamic pricing to other services.

[0042] The dynamic pricing unit can improve trust by transparently disclosing the results of dynamic pricing to users and explaining the reasons for price setting. The dynamic pricing unit, for example, builds a system that transparently discloses the results of dynamic pricing to users and explains the reasons for price setting. For example, it shows price fluctuations based on the balance between supply and demand. The dynamic pricing unit also develops an interface for explaining the reasons for price setting to users. For example, it visually displays the factors behind price fluctuations and calculation methods. The dynamic pricing unit also discloses the pricing process to users to improve the transparency of dynamic pricing. For example, it shows price setting based on demand forecast data and the price trends of competitors. In this way, it is possible to transparently disclose the results of dynamic pricing and explain the reasons for price setting, thereby improving user trust.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The mobility data analysis system can also collect energy consumption data and optimize energy efficiency. For example, it can collect charging data for electric vehicles and propose optimal charging schedules. It can also evaluate the energy efficiency of transportation methods based on energy consumption data and propose routes with low energy consumption. Furthermore, it can use energy consumption data to analyze energy consumption patterns across the city and improve energy efficiency. Thus, by collecting and analyzing energy consumption data, it is possible to optimize energy efficiency.

[0045] The mobility data collection unit can include real-time data from drones and autonomous vehicles. For example, drones are used to monitor traffic conditions throughout a city in real time and collect mobility data. For example, images captured by drones are analyzed to detect traffic congestion and accidents. Sensor data from autonomous vehicles is also collected to understand traffic conditions in real time. For example, vehicle location information, speed, and information on surrounding obstacles are analyzed to predict traffic patterns. Data from drones and autonomous vehicles is also integrated to build a system that analyzes mobility data throughout a city in real time. For example, multiple data sources are integrated to optimize traffic flow. This makes it possible to collect a wider range of mobility data by including real-time data from drones and autonomous vehicles.

[0046] The mobility data collection unit can incorporate an anomaly detection algorithm to automatically identify and exclude abnormal movement patterns. For example, an anomaly detection algorithm can be used to automatically identify abnormal movement patterns from mobility data. For example, it can detect sudden changes in direction or speed that differ from normal movement patterns. In addition, a system can be built to detect abnormal movement patterns in real time and exclude them from the data. For example, an alert can be issued and the data can be filtered when an abnormal movement pattern is detected. In addition, an anomaly detection algorithm can be used to improve the quality of mobility data. For example, excluding abnormal data can enable more accurate demand forecasting. This automatically identifying and excluding abnormal movement patterns improves data quality.

[0047] The mobility data collection unit can analyze tourist movement patterns limited to tourist destinations and event venues. For example, it collects mobility data at tourist destinations and event venues to analyze tourist movement patterns. For example, it identifies travel routes and length of stay between major tourist spots. It also collects tourist movement data during specific events to analyze the impact of the event. For example, it analyzes changes in traffic volume and movement patterns before and after the event. It also builds a system that predicts tourist behavior patterns based on mobility data at tourist destinations and event venues. For example, it predicts congestion and suggests optimal travel routes. This makes it possible to forecast demand at specific locations by collecting data limited to tourist destinations and event venues and analyzing tourist movement patterns.

[0048] The mobility data collection unit can integrate smart city infrastructure data to understand the movements of the entire city. For example, by integrating smart city infrastructure data with mobility data, a system can be built to understand the movements of the entire city in real time. For example, data on the lighting status of streetlights and the movement of garbage trucks can be analyzed. The infrastructure data can also be used to develop algorithms that predict movements in the city. For example, traffic volume and the flow of people can be predicted based on streetlight lighting patterns and garbage collection routes. The smart city infrastructure data can also be integrated with mobility data to be used in urban planning and traffic management. For example, traffic congestion can be predicted and public transportation operation plans can be optimized. In this way, by integrating smart city infrastructure data, it is possible to understand the movements of the entire city in real time.

[0049] The demand forecasting unit can make more precise predictions by taking into account external factors such as seasons, weather, and events. For example, seasonal and weather data can be incorporated into the demand forecasting algorithm to make more precise predictions that take external factors into account. For example, traffic demand during rainy weather and seasonal travel patterns can be predicted. In addition, event information can be incorporated into the demand forecasting algorithm to make demand forecasts during specific event periods. For example, traffic demand can be predicted during concerts and sporting events. In addition, a demand forecasting system that takes external factors into account can be built, and demand can be predicted based on seasonal, weather, and event information. For example, weather forecast data and event calendars can be used. This makes it possible to make more precise demand forecasts by taking into account external factors such as seasons, weather, and events.

[0050] The demand forecasting unit can incorporate not only past data but also future plans. For example, future transportation infrastructure plans can be incorporated into the demand forecasting algorithm to predict future demand. For example, demand forecasts can be made taking into account construction plans for new railways and roads. Future planning data can also be incorporated into the demand forecasting algorithm to build a system that performs long-term demand forecasts. For example, demand can be predicted based on urban development plans or plans to introduce new public transportation systems. Past data can also be integrated with future planning data to improve the demand forecasting algorithm. For example, demand can be predicted by combining past travel patterns with new infrastructure plans. In this way, by incorporating past data and future plans, long-term demand forecasts become possible.

[0051] The demand forecasting unit can perform demand forecasts specialized for a specific industry. For example, a demand forecasting algorithm specialized for the logistics industry can be developed to predict demand in the logistics industry. For example, demand for delivery routes and warehouses can be predicted. A demand forecasting system specialized for the tourism industry can also be built to predict tourist movement patterns and demand. For example, congestion at tourist destinations can be predicted and demand for accommodation can be predicted. A demand forecasting algorithm specialized for a specific industry can also be developed to precisely predict demand for each industry. For example, demand in the medical or education industry can be predicted. In this way, by performing demand forecasts specialized for a specific industry, demand for each industry can be precisely predicted.

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

[0053] Step 1: The mobility data collection unit collects mobility data. For example, it collects taxi boarding and alighting data, public transportation usage data, and GPS data. Taxi boarding and alighting data is obtained from the taxi operation management system, public transportation usage data from transportation card usage history, and GPS data from smartphone location information. Step 2: The data reduction unit reduces the mobility data collected by the mobility data collection unit. For example, it normalizes, filters, and classifies the data. Data normalization is the process of standardizing the data format, filtering is the process of removing unnecessary data, and classification is the process of dividing data into specific categories. Step 3: The demand forecasting unit performs demand forecasting based on the mobility data organized by the data organization unit. For example, it predicts traffic demand in a specific time period or area and performs demand forecasting based on the algorithm used. The accuracy of the forecast is evaluated by comparing the actual data with the predicted data. Step 4: The dynamic pricing unit sets prices based on the demand forecast data generated by the demand forecasting unit. For example, it can raise prices during times of high demand and lower prices during times of low demand. It can also fluctuate prices according to the balance between supply and demand.

[0054] (Example 2) A mobility data analysis system according to an embodiment of the present invention is a system that organizes and analyzes mobility data to realize demand forecasting and dynamic pricing. As a result, the mobility data analysis system can sell demand forecast data and generate revenue.

[0055] A mobility data analysis system according to an embodiment includes a mobility data collection unit, a data reduction unit, a demand forecasting unit, and a dynamic pricing unit. The mobility data collection unit collects mobility data. For example, it collects taxi boarding and alighting data. The mobility data collection unit can also collect public transportation usage data. The mobility data collection unit can also collect GPS data. For example, taxi boarding and alighting data is obtained from a taxi operation management system. Public transportation usage data is obtained from a transportation card usage history. GPS data is obtained from smartphone location information. The data reduction unit organizes the mobility data collected by the mobility data collection unit. For example, the data reduction unit normalizes the data. The data reduction unit can also filter the data. The data reduction unit can also classify the data. For example, data normalization is a process of standardizing the data format. Data filtering is a process of removing unnecessary data. Data classification is a process of dividing data into specific categories. The demand forecasting unit performs demand forecasting based on the mobility data organized by the data reduction unit. For example, the demand forecasting unit predicts traffic demand in a specific time period or area. The demand forecasting unit can also perform demand forecasts based on the algorithm used. The demand forecasting unit can also evaluate the accuracy of the forecast. For example, traffic demand in a specific time period or area is predicted based on past data. The algorithm used is a machine learning algorithm. The accuracy of the forecast is evaluated by comparing actual data with predicted data. The dynamic pricing unit sets prices based on the demand forecast data generated by the demand forecasting unit. For example, the dynamic pricing unit raises fares during times of high demand. The dynamic pricing unit can also lower fares during times of low demand. The dynamic pricing unit can also fluctuate prices according to the balance between supply and demand. For example, profits are maximized by raising fares during times of high demand. Users are increased by lowering fares during times of low demand. Prices fluctuate according to the balance between supply and demand based on the demand forecast data.As a result, the mobility data analysis system according to the embodiment can organize and analyze mobility data to realize demand forecasting and dynamic pricing. For example, operators of ride-sharing services can create efficient operation plans based on demand forecast data. In addition, new revenue sources can be secured by selling demand forecast data. Furthermore, application of demand forecast data can enable effective measures to be implemented in fields such as public transportation and urban planning.

[0056] The mobility data collection unit can simultaneously collect user emotion data and analyze the user's emotions regarding travel using an emotion estimation function. For example, when collecting mobility data, the mobility data collection unit acquires emotion data from the user's smartphone and analyzes the user's emotions during travel in real time. For example, the smartphone's sensors are used to detect the user's heart rate and facial expressions and estimate emotions. The mobility data collection unit also collects emotion data and analyzes the association between travel patterns and emotions to evaluate the stress and satisfaction the user feels during travel. For example, it analyzes voice and text messages during travel and estimates emotions. The mobility data collection unit also uses the emotion data to evaluate the emotional impact of specific travel patterns on the user. For example, it makes suggestions to avoid crowded times or routes. By collecting user emotion data and analyzing emotions regarding travel, more precise demand forecasting becomes possible.

[0057] The mobility data collection unit can include real-time data from drones and autonomous vehicles. For example, the mobility data collection unit uses drones to monitor traffic conditions throughout a city in real time and collect mobility data. For example, it analyzes video captured by drones to detect traffic congestion and accidents. The mobility data collection unit also collects sensor data from autonomous vehicles to understand traffic conditions in real time. For example, it analyzes vehicle location information, speed, and surrounding obstacle information to predict traffic patterns. The mobility data collection unit also integrates data from drones and autonomous vehicles to build a system that analyzes mobility data throughout a city in real time. For example, it integrates multiple data sources to optimize traffic flow. This enables the collection of a wider range of mobility data by including real-time data from drones and autonomous vehicles.

[0058] The mobility data collection unit can incorporate an anomaly detection algorithm to automatically identify and exclude abnormal movement patterns. For example, the mobility data collection unit uses an anomaly detection algorithm to automatically identify abnormal movement patterns from mobility data. For example, it detects sudden changes in direction or speed that differ from normal movement patterns. The mobility data collection unit also builds a system that detects abnormal movement patterns in real time and excludes them from the data. For example, it issues an alert and filters the data when an abnormal movement pattern is detected. The mobility data collection unit also uses an anomaly detection algorithm to improve the quality of mobility data. For example, excluding abnormal data allows for more accurate demand forecasting. This improves data quality by automatically identifying and excluding abnormal movement patterns.

[0059] The mobility data collection unit can analyze tourist movement patterns limited to tourist destinations and event venues. The mobility data collection unit, for example, collects mobility data at tourist destinations and event venues and analyzes tourist movement patterns. For example, it identifies movement routes and stay times between major tourist spots. The mobility data collection unit also collects tourist movement data during a specific event period and analyzes the impact of the event. For example, it analyzes changes in traffic volume and movement patterns before and after the event. The mobility data collection unit also builds a system that predicts tourist behavior patterns based on mobility data at tourist destinations and event venues. For example, it predicts congestion and suggests optimal movement routes. As a result, by collecting data limited to tourist destinations and event venues and analyzing tourist movement patterns, it becomes possible to predict demand at specific locations.

[0060] The mobility data collection unit integrates smart city infrastructure data to understand the movements of the entire city. For example, the mobility data collection unit integrates smart city infrastructure data with mobility data to build a system that understands the movements of the entire city in real time. For example, it analyzes data on the lighting status of streetlights and the movement of garbage trucks. The mobility data collection unit also uses infrastructure data to develop algorithms that predict the movements of the city. For example, it predicts traffic volume and the flow of people based on streetlight lighting patterns and garbage collection routes. The mobility data collection unit also integrates smart city infrastructure data with mobility data to use in urban planning and traffic management. For example, it predicts traffic congestion and optimizes public transportation operation plans. In this way, by integrating smart city infrastructure data, it is possible to understand the movements of the entire city in real time.

[0061] The demand forecasting unit can take into account the user's emotional data and prioritize predicting emotionally positive travel patterns. The demand forecasting unit, for example, incorporates the user's emotional data into a demand forecasting algorithm and prioritizes predicting travel patterns associated with positive emotions. For example, it predicts travel routes that will give the user high satisfaction. The demand forecasting unit also identifies travel patterns that will give the user positive emotions based on the emotional data and reflects these patterns in the demand forecast. For example, it prioritizes predicting comfortable means of transportation and routes. The demand forecasting unit also analyzes the user's emotional data and builds a system that incorporates travel patterns associated with positive emotions into the demand forecast. For example, it prioritizes predicting travel patterns with a high emotional score. In this way, by taking the user's emotional data into consideration, it is possible to prioritize predicting positive travel patterns.

[0062] The demand forecasting unit can make more precise predictions by taking into account external factors such as seasons, weather, and events. The demand forecasting unit, for example, incorporates seasonal and weather data into the demand forecasting algorithm to make more precise predictions that take external factors into account. For example, it predicts traffic demand during rainy weather and seasonal travel patterns. The demand forecasting unit also incorporates event information into the demand forecasting algorithm to make demand forecasts during specific event periods. For example, it predicts traffic demand during concerts and sporting events. The demand forecasting unit also builds a demand forecasting system that takes external factors into account and predicts demand based on seasonal, weather, and event information. For example, it utilizes weather forecast data and event calendars. This makes it possible to make more precise demand forecasts by taking into account external factors such as seasons, weather, and events.

[0063] The demand forecasting unit can make more precise predictions by taking into account external factors such as seasons, weather, and events. The demand forecasting unit, for example, incorporates seasonal and weather data into the demand forecasting algorithm to make more precise predictions that take external factors into account. For example, it predicts traffic demand during rainy weather and seasonal travel patterns. The demand forecasting unit also incorporates event information into the demand forecasting algorithm to make demand forecasts during specific event periods. For example, it predicts traffic demand during concerts and sporting events. The demand forecasting unit also builds a demand forecasting system that takes external factors into account and predicts demand based on seasonal, weather, and event information. For example, it utilizes weather forecast data and event calendars. This makes it possible to make more precise demand forecasts by taking into account external factors such as seasons, weather, and events.

[0064] The demand forecasting unit can incorporate not only past data but also future plans. For example, the demand forecasting unit incorporates future transportation infrastructure plans into the demand forecasting algorithm to predict future demand. For example, it performs demand forecasts that take into account construction plans for new railways and roads. The demand forecasting unit also incorporates future planning data into the demand forecasting algorithm to build a system that performs long-term demand forecasts. For example, it predicts demand based on urban development plans and plans to introduce new public transportation systems. The demand forecasting unit also integrates past data and future planning data to improve the demand forecasting algorithm. For example, it predicts demand by combining past travel patterns with new infrastructure plans. In this way, by incorporating past data and future plans, long-term demand forecasts become possible.

[0065] The demand forecasting unit can perform demand forecasting specialized for a specific industry. For example, the demand forecasting unit develops a demand forecasting algorithm specialized for the logistics industry to predict demand in the logistics industry. For example, it predicts demand for delivery routes and warehouses. The demand forecasting unit also builds a demand forecasting system specialized for the tourism industry to predict tourist movement patterns and demand. For example, it predicts congestion at tourist destinations and demand for accommodation facilities. The demand forecasting unit also develops a demand forecasting algorithm specialized for a specific industry to precisely predict demand for each industry. For example, it predicts demand in the medical industry or the education industry. In this way, by performing demand forecasting specialized for a specific industry, it is possible to precisely predict demand for each industry.

[0066] The demand forecasting unit can update the demand forecast data in real time, enabling it to respond to sudden fluctuations in demand. The demand forecasting unit, for example, updates the demand forecast data in real time and builds a system that can respond to sudden fluctuations in demand. For example, it immediately reflects demand fluctuations due to traffic accidents or natural disasters. The demand forecasting unit also updates the demand forecasting algorithm based on real-time data and responds quickly to demand fluctuations. For example, it utilizes real-time traffic data and weather data. The demand forecasting unit also develops a system that monitors the demand forecast data in real time and corrects the forecast as necessary to respond to sudden fluctuations in demand. For example, it responds to sudden cancellations or changes to events. In this way, by updating the demand forecast data in real time, it is possible to respond quickly to sudden fluctuations in demand.

[0067] The demand forecasting unit can use the emotion estimation function to analyze users' emotional responses based on demand forecast data and improve the accuracy of predictions. The demand forecasting unit, for example, uses the emotion estimation function to analyze users' emotional responses based on demand forecast data and build a system that improves prediction accuracy. For example, the demand forecasting unit adjusts the prediction algorithm based on users' emotional data. The demand forecasting unit also collects users' emotional responses to demand forecast data in real time and improves prediction accuracy based on that data. For example, it prioritizes the adoption of predictions with a high number of positive emotional responses. The demand forecasting unit also analyzes users' emotional response data and improves the demand forecasting algorithm. For example, it reevaluates predictions with low emotion scores and improves accuracy. In this way, by using the emotion estimation function, it is possible to analyze users' emotional responses based on demand forecast data and improve prediction accuracy.

[0068] The dynamic pricing unit can set emotionally positive prices by taking into account the user's emotional data. The dynamic pricing unit, for example, incorporates the user's emotional data into a dynamic pricing algorithm to set prices that elicit positive emotions. For example, it prioritizes price ranges that result in high user satisfaction. The dynamic pricing unit also identifies price ranges that evoke positive emotions in users based on the emotional data and reflects these prices in dynamic pricing. For example, it prioritizes price ranges with high emotional scores. The dynamic pricing unit also analyzes the user's emotional data and builds a system for setting prices that elicit positive emotions. For example, it prioritizes price ranges with high emotional scores. In this way, emotionally positive pricing is possible by taking the user's emotional data into consideration.

[0069] The dynamic pricing department can incorporate competitors' price trends in real time and set competitive prices. For example, the dynamic pricing department collects competitors' price trends in real time and incorporates them into the dynamic pricing algorithm. For example, the department adjusts its own prices in response to competitors' price fluctuations. The dynamic pricing department also builds a system for setting competitive prices based on competitors' price data. For example, it monitors competitors' prices in real time and sets optimal prices. The dynamic pricing department also analyzes competitors' price trends in real time and improves the dynamic pricing algorithm. For example, it can respond quickly to competitors' price fluctuations. This makes it possible to set competitive prices by incorporating competitors' price trends in real time.

[0070] The dynamic pricing unit can set individually optimized prices by taking into account the user's past usage history and loyalty programs. The dynamic pricing unit, for example, builds a system that provides individually optimized prices based on the user's past usage history. For example, it provides discounted prices to users who use the service frequently. The dynamic pricing unit also sets different prices for each user by taking into account the loyalty program. For example, it provides special prices to users with high loyalty points. The dynamic pricing unit also integrates the user's past usage history and loyalty programs to develop an algorithm that provides individually optimized prices. For example, it adjusts prices according to frequency of use and loyalty points. This makes it possible to set individually optimized prices by taking into account the user's past usage history and loyalty programs.

[0071] The dynamic pricing department can also apply this to other services to build a comprehensive pricing strategy. For example, the dynamic pricing department applies dynamic pricing to hotel and restaurant reservations to develop a system that builds a comprehensive pricing strategy. For example, it adjusts accommodation prices and meal prices according to demand. The dynamic pricing department also develops dynamic pricing algorithms that work in conjunction with other services to realize a comprehensive pricing strategy. For example, it optimizes the price of the entire travel package. The dynamic pricing department also applies dynamic pricing based on hotel and restaurant reservation data to build a comprehensive pricing strategy. For example, it raises prices during periods of high demand and lowers prices during periods of low demand. In this way, a comprehensive pricing strategy can be built by applying dynamic pricing to other services.

[0072] The dynamic pricing unit can improve trust by transparently disclosing the results of dynamic pricing to users and explaining the reasons for price setting. The dynamic pricing unit, for example, builds a system that transparently discloses the results of dynamic pricing to users and explains the reasons for price setting. For example, it shows price fluctuations based on the balance between supply and demand. The dynamic pricing unit also develops an interface for explaining the reasons for price setting to users. For example, it visually displays the factors behind price fluctuations and calculation methods. The dynamic pricing unit also discloses the pricing process to users to improve the transparency of dynamic pricing. For example, it shows price setting based on demand forecast data and the price trends of competitors. In this way, it is possible to transparently disclose the results of dynamic pricing and explain the reasons for price setting, thereby improving user trust.

[0073] The dynamic pricing unit uses the emotion estimation function to analyze users' emotional responses to the dynamic pricing results and continuously optimize pricing. The dynamic pricing unit, for example, uses the emotion estimation function to analyze users' emotional responses to the dynamic pricing results and builds a system that optimizes pricing. For example, the dynamic pricing unit adjusts prices based on user emotional data. The dynamic pricing unit also collects users' emotional responses to the dynamic pricing results in real time and optimizes pricing based on that data. For example, it prioritizes setting price ranges with a high number of positive emotional responses. The dynamic pricing unit also analyzes users' emotional response data and improves the dynamic pricing algorithm. For example, it reevaluates pricing with low emotional scores and sets optimal prices. In this way, by using the emotion estimation function, it is possible to analyze users' emotional responses to the dynamic pricing results and continuously optimize pricing.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The mobility data analysis system can also collect energy consumption data and optimize energy efficiency. For example, it can collect charging data for electric vehicles and propose optimal charging schedules. It can also evaluate the energy efficiency of transportation methods based on energy consumption data and propose routes with low energy consumption. Furthermore, it can use energy consumption data to analyze energy consumption patterns across the city and improve energy efficiency. Thus, by collecting and analyzing energy consumption data, it is possible to optimize energy efficiency.

[0076] The mobility data collection unit can simultaneously collect user emotion data and use the emotion estimation function to analyze the user's emotions regarding travel. For example, when collecting mobility data, emotion data is acquired from the user's smartphone and emotions during travel are analyzed in real time. For example, smartphone sensors are used to detect the user's heart rate and facial expressions and estimate emotions. Furthermore, emotion data is collected and the relationship between travel patterns and emotions is analyzed to evaluate the stress and satisfaction the user feels during travel. For example, voice and text messages during travel are analyzed to estimate emotions. Furthermore, the emotion data is used to evaluate the emotional impact of specific travel patterns on the user. For example, suggestions are made to avoid crowded times or routes. In this way, collecting user emotion data and analyzing emotions regarding travel enables more precise demand forecasting.

[0077] The mobility data collection unit can include real-time data from drones and autonomous vehicles. For example, drones are used to monitor traffic conditions throughout a city in real time and collect mobility data. For example, images captured by drones are analyzed to detect traffic congestion and accidents. Sensor data from autonomous vehicles is also collected to understand traffic conditions in real time. For example, vehicle location information, speed, and information on surrounding obstacles are analyzed to predict traffic patterns. Data from drones and autonomous vehicles is also integrated to build a system that analyzes mobility data throughout a city in real time. For example, multiple data sources are integrated to optimize traffic flow. This makes it possible to collect a wider range of mobility data by including real-time data from drones and autonomous vehicles.

[0078] The mobility data collection unit can incorporate an anomaly detection algorithm to automatically identify and exclude abnormal movement patterns. For example, an anomaly detection algorithm can be used to automatically identify abnormal movement patterns from mobility data. For example, it can detect sudden changes in direction or speed that differ from normal movement patterns. In addition, a system can be built to detect abnormal movement patterns in real time and exclude them from the data. For example, an alert can be issued and the data can be filtered when an abnormal movement pattern is detected. In addition, an anomaly detection algorithm can be used to improve the quality of mobility data. For example, excluding abnormal data can enable more accurate demand forecasting. This automatically identifying and excluding abnormal movement patterns improves data quality.

[0079] The mobility data collection unit can analyze tourist movement patterns limited to tourist destinations and event venues. For example, it collects mobility data at tourist destinations and event venues to analyze tourist movement patterns. For example, it identifies travel routes and length of stay between major tourist spots. It also collects tourist movement data during specific events to analyze the impact of the event. For example, it analyzes changes in traffic volume and movement patterns before and after the event. It also builds a system that predicts tourist behavior patterns based on mobility data at tourist destinations and event venues. For example, it predicts congestion and suggests optimal travel routes. This makes it possible to forecast demand at specific locations by collecting data limited to tourist destinations and event venues and analyzing tourist movement patterns.

[0080] The mobility data collection unit can integrate smart city infrastructure data to understand the movements of the entire city. For example, by integrating smart city infrastructure data with mobility data, a system can be built to understand the movements of the entire city in real time. For example, data on the lighting status of streetlights and the movement of garbage trucks can be analyzed. The infrastructure data can also be used to develop algorithms that predict movements in the city. For example, traffic volume and the flow of people can be predicted based on streetlight lighting patterns and garbage collection routes. The smart city infrastructure data can also be integrated with mobility data to be used in urban planning and traffic management. For example, traffic congestion can be predicted and public transportation operation plans can be optimized. In this way, by integrating smart city infrastructure data, it is possible to understand the movements of the entire city in real time.

[0081] The demand forecasting unit can take into account the user's emotional data and prioritize predicting emotionally positive travel patterns. For example, the demand forecasting algorithm incorporates the user's emotional data and prioritizes predicting travel patterns associated with positive emotions. For example, it predicts travel routes that give the user high satisfaction. Furthermore, based on the emotional data, it identifies travel patterns that give the user positive emotions and reflects those patterns in the demand forecast. For example, it prioritizes predicting comfortable means of transportation and routes. Furthermore, it builds a system that analyzes the user's emotional data and incorporates travel patterns associated with positive emotions into the demand forecast. For example, it prioritizes predicting travel patterns with a high emotional score. In this way, by taking the user's emotional data into consideration, it is possible to prioritize predicting positive travel patterns.

[0082] The demand forecasting unit can make more precise predictions by taking into account external factors such as seasons, weather, and events. For example, seasonal and weather data can be incorporated into the demand forecasting algorithm to make more precise predictions that take external factors into account. For example, traffic demand during rainy weather and seasonal travel patterns can be predicted. In addition, event information can be incorporated into the demand forecasting algorithm to make demand forecasts during specific event periods. For example, traffic demand can be predicted during concerts and sporting events. In addition, a demand forecasting system that takes external factors into account can be built, and demand can be predicted based on seasonal, weather, and event information. For example, weather forecast data and event calendars can be used. This makes it possible to make more precise demand forecasts by taking into account external factors such as seasons, weather, and events.

[0083] The demand forecasting unit can incorporate not only past data but also future plans. For example, future transportation infrastructure plans can be incorporated into the demand forecasting algorithm to predict future demand. For example, demand forecasts can be made taking into account construction plans for new railways and roads. Future planning data can also be incorporated into the demand forecasting algorithm to build a system that performs long-term demand forecasts. For example, demand can be predicted based on urban development plans or plans to introduce new public transportation systems. Past data can also be integrated with future planning data to improve the demand forecasting algorithm. For example, demand can be predicted by combining past travel patterns with new infrastructure plans. In this way, by incorporating past data and future plans, long-term demand forecasts become possible.

[0084] The demand forecasting unit can perform demand forecasts specialized for a specific industry. For example, a demand forecasting algorithm specialized for the logistics industry can be developed to predict demand in the logistics industry. For example, demand for delivery routes and warehouses can be predicted. A demand forecasting system specialized for the tourism industry can also be built to predict tourist movement patterns and demand. For example, congestion at tourist destinations can be predicted and demand for accommodation can be predicted. A demand forecasting algorithm specialized for a specific industry can also be developed to precisely predict demand for each industry. For example, demand in the medical or education industry can be predicted. In this way, by performing demand forecasts specialized for a specific industry, demand for each industry can be precisely predicted.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The mobility data collection unit collects mobility data. For example, it collects taxi boarding and alighting data, public transportation usage data, and GPS data. Taxi boarding and alighting data is obtained from the taxi operation management system, public transportation usage data from transportation card usage history, and GPS data from smartphone location information. Step 2: The data reduction unit reduces the mobility data collected by the mobility data collection unit. For example, it normalizes, filters, and classifies the data. Data normalization is the process of standardizing the data format, filtering is the process of removing unnecessary data, and classification is the process of dividing data into specific categories. Step 3: The demand forecasting unit performs demand forecasting based on the mobility data organized by the data organization unit. For example, it predicts traffic demand in a specific time period or area and performs demand forecasting based on the algorithm used. The accuracy of the forecast is evaluated by comparing the actual data with the predicted data. Step 4: The dynamic pricing unit sets prices based on the demand forecast data generated by the demand forecasting unit. For example, it can raise prices during times of high demand and lower prices during times of low demand. It can also fluctuate prices according to the balance between supply and demand.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 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 mobility data collection unit that collects mobility data; a data organizing unit that organizes the mobility data collected by the mobility data collecting unit; a demand prediction unit that performs demand prediction based on the mobility data organized by the data organization unit; a dynamic pricing unit that sets prices based on the demand forecast data generated by the demand forecast unit. A system characterized by:

2. The mobility data collection unit Collect user emotion data at the same time and analyze the user's emotion regarding the movement.

2. The system of claim 1.

3. The mobility data collection unit Include real-time data from drones and autonomous vehicles 2. The system of claim 1.

4. The mobility data collection unit Implement anomaly detection algorithms to automatically identify and filter out abnormal movement patterns 2. The system of claim 1.

5. The mobility data collection unit Analyze tourist movement patterns limited to tourist spots and event venues 2. The system of claim 1.

Images