system

Abstract

We provide the system. [Solution] A means of obtaining information to acquire event information, A means of obtaining information for acquiring weather information, An analytical means that analyzes acquired event information and weather information to perform demand forecasting, A means for determining the arrangement of transportation means to optimize the arrangement of transportation means based on demand forecasts, A notification means for notifying optimized placement information, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In order to efficiently dispatch transportation means, it is necessary to accurately predict the riding demand during event holding or bad weather. However, with conventional methods, it is difficult to determine the demand in real time and accurately, resulting in inefficient allocation of transportation means and thus the problem of not being able to fully acquire customers. Solving such problems and improving the operation efficiency of transportation means are required. 【Means for Solving the Problems】 【0005】 This invention provides information acquisition means for obtaining event information and information acquisition means for obtaining weather information. Based on the acquired information, an analysis means performs demand forecasting considering the event end time and the probability of precipitation, and based on the results, a placement determination means determines the optimal placement of transportation means. Furthermore, a system is established to notify the drivers and managers of the transportation means of the optimized placement information through a notification means, enabling rapid and efficient operation. 【0006】 "Information acquisition means" refers to a function or device for collecting event information or weather information from external sources. 【0007】 "Analysis means" refers to a function or device that performs calculations or operations to forecast demand based on acquired information. 【0008】 "Arrangement determination means" refers to a function or device for determining the optimal arrangement of transportation means based on analysis results. 【0009】 "Notification means" refers to a function or device for transmitting determined deployment information to the driver or manager of the means of transport. [Brief explanation of the drawing] 【0010】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0011】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0012】 First, let's explain the terminology used in the following explanation. 【0013】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0014】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0015】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0016】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark). 【0017】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0018】 [First Embodiment] 【0019】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0020】 As shown in Figure 1, the 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. 【0021】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0022】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0023】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0024】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0025】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0026】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0027】 As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0028】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0029】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0030】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0031】 This invention is implemented as a system comprising four elements: an information acquisition means, an analysis means, a placement determination means, and a notification means. 【0032】 First, the server collects event and weather information via the internet through websites and APIs. Event information includes data such as the event name, date and time, venue, and expected number of attendees. Weather information includes meteorological data such as temperature, probability of precipitation, and wind speed. By collecting this information, the server can obtain the latest event and weather information in real time. 【0033】 Next, the server analyzes the collected data using machine learning and statistical models. Specifically, the server utilizes passenger demand data related to past events and weather patterns to predict future demand fluctuations. For example, if a concert by a popular artist is scheduled or if a sudden weather change is expected, the server will take these factors into account when analyzing demand. 【0034】 Subsequently, based on the predicted demand, the server utilizes a placement determination mechanism to determine the optimal allocation of transportation methods (such as taxis and vehicles). This proposes an efficient dispatch route to meet the demand at a specific time and location. For example, at the end of a large-scale event, the server might instruct a placement that concentrates a large number of taxis around the venue. 【0035】 Finally, the terminal receives this location information. The terminal notifies the taxi driver, who is the user, of the information in an intuitively understandable format. Specifically, a push notification such as "Demand will increase around XX Hall at 9 PM" is sent via a smartphone or other device. In this way, the driver can efficiently position themselves to pick up passengers. 【0036】 This series of processes allows taxi companies to avoid unnecessary travel while quickly responding to customer demand, ultimately leading to improved profitability. 【0037】 The following describes the processing flow. 【0038】 Step 1: 【0039】 The server sets up scheduled tasks to send requests to event information APIs and weather forecast APIs to retrieve the latest event and weather information. It authenticates using API keys, receives the data in JSON format, and stores it in an internal database. 【0040】 Step 2: 【0041】 The server performs the necessary preprocessing to analyze the acquired event and weather information. Specifically, it filters the event information for the target area and extracts important attributes such as start and end times and the number of participants. For weather information, it identifies data such as the probability of precipitation and temperature. 【0042】 Step 3: 【0043】 The server performs demand forecasting using machine learning models and statistical methods based on pre-processed data. It analyzes predicted demand fluctuations by referencing occupancy rate data from similar past events. This analysis identifies high-demand areas at specific times and locations. 【0044】 Step 4: 【0045】 The server uses the analysis results to run a deployment logic and generate an optimal transportation deployment plan. This includes setting up dispatch routes and waiting points to efficiently meet the predicted demand within a specified time. 【0046】 Step 5: 【0047】 The terminal receives location information transmitted from the server and notifies the taxi driver, who is the user. Push notifications are displayed on a smartphone app or in-vehicle terminal, providing important information through an intuitive interface. The notifications indicate dispatch demand for specific times and locations, enabling drivers to respond quickly. 【0048】 (Example 1) 【0049】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0050】 Efficient transportation deployment needs to be sensitive to demand changes caused by events and weather fluctuations. However, conventional systems have difficulty with real-time demand forecasting and flexible deployment decisions, sometimes leading to excessive waiting times and increased costs. Furthermore, there was a challenge in that specific measures for maximizing the operational efficiency of transportation were limited and lacked flexibility. 【0051】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0052】 In this invention, the server includes an information acquisition device for acquiring event information, an information acquisition device for acquiring weather information, a data analysis device for analyzing the acquired event information and weather information and making demand forecasts, a placement determination device for optimizing the placement of transportation means based on the demand forecast, a notification device for presenting optimized placement information, an analysis device for predicting future demand fluctuations using machine learning models and statistical models, and an optimization device for calculating the optimal placement of transportation means based on past data. This enables real-time demand forecasting and efficient placement determination. 【0053】 An "information acquisition device" refers to hardware or software used to collect data from external sources, and has the function of acquiring specific event information or weather information. 【0054】 A "data analysis device" refers to hardware or software that has the function of analyzing acquired data and applying statistical models or machine learning algorithms to forecast demand. 【0055】 A "distribution determination device" refers to hardware or software used to calculate the optimal distribution of transportation methods based on analyzed demand forecast data. 【0056】 A "notification device" refers to hardware or software that displays or transmits transportation arrangement information calculated by a server to a user. 【0057】 A "machine learning model" is a set of algorithms used to learn data patterns and make predictions about future data, enabling highly accurate analysis. 【0058】 A "statistical model" is an algorithm that incorporates mathematical methods used to analyze data and extract useful information from it. 【0059】 An "optimization device" is hardware or software used to perform calculations to determine the optimal placement of components to obtain the best results under specific conditions. 【0060】 This invention is a system that enables the efficient arrangement of transportation means through the cooperation of a server, terminal, and user. First, the server uses an information acquisition device to collect event information and weather information from publicly available data sources on the internet. In this process, it utilizes scripting languages ​​such as Python and JavaScript (registered trademark) to obtain the necessary data through APIs and web scraping. The collected data is stored in a database in real time. 【0061】 Next, the server uses a data analysis device to analyze the acquired event and weather information. This analysis utilizes machine learning models and statistical models to learn past data patterns and predict future demand fluctuations. Specific technologies used include the Python scikit-learn library and TENSORFLOW®. 【0062】 Based on the analysis results, the server uses a placement determination device to calculate the optimal placement of transportation methods. Here, optimization libraries such as Google® OR-Tools are used to simulate efficient vehicle dispatch routes. 【0063】 Subsequently, the terminal receives optimized placement information from the server via a notification device and displays it to the user. Specifically, Firebase Cloud Messaging (FCM) is used to send the information as a push notification to the smartphone. An example message might be, "Demand will increase around XX Hall at 9 PM." This notification allows users to operate their transportation more efficiently. 【0064】 A concrete example is a scenario where a terminal notifies the server of an expected increase in demand at the end of an event, and the driver, as the user, uses this information to determine the optimal location. In this way, real-time demand forecasting and placement optimization are achieved. An example of a prompt message would be, "Tell me how to determine the optimal taxi placement at the end of a large-scale event." 【0065】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0066】 Step 1: 【0067】 The server uses an information acquisition device to collect event and weather information from the internet. It uses API endpoints and web page URLs as input. The server's specific actions involve using a Python script to send API requests and parsing the returned JSON data. The output includes information such as event name, date and time, venue, expected number of attendees, temperature, probability of precipitation, and wind speed, which are then stored in a database. 【0068】 Step 2: 【0069】 The server uses a data analysis device to analyze collected event and weather information. The input consists of event and weather information stored in a database. The server uses a machine learning model to forecast demand. Specifically, it performs regression analysis using the scikit-learn library to predict demand fluctuations. The output is numerical data indicating increases or decreases in demand, which is used to determine the next deployment. 【0070】 Step 3: 【0071】 The server uses a placement determination device to determine the optimal placement of transportation methods based on predicted demand. The input is the demand forecast data obtained in step 2. The specific operation involves solving an optimization problem using tools such as Google OR-Tools to calculate the appropriate vehicle dispatch route. The output is the placement and route information of transportation methods at a specific time and location. 【0072】 Step 4: 【0073】 The terminal receives optimized placement information from the server via a notification device and notifies the user. The input is the placement information calculated in step 3. The specific action the terminal takes is to send a push notification to a smartphone or device using Firebase Cloud Messaging. The output will display something like, "Demand will be high around XX Hall at 9 PM." 【0074】 Step 5: 【0075】 The user operates the transportation based on notifications. The input is the notification information received from the terminal. The user's specific action is to refer to the notification, position the transportation at a specific location, and efficiently pick up passengers. This allows the user to select the optimal route. 【0076】 (Application Example 1) 【0077】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0078】 Conventional transportation scheduling systems struggle to respond flexibly to real-time demand fluctuations, sometimes resulting in wasted travel and missed opportunities. To solve this problem, more accurate demand forecasting and a mechanism for dynamically adjusting routes are necessary. 【0079】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0080】 In this invention, the server includes information acquisition means for acquiring event information, information acquisition means for acquiring weather information, analysis means for analyzing the acquired event information and weather information and making demand forecasts, and means for self-adjusting travel routes based on demand forecasts. This enables the efficient deployment of travel means in real time according to demand. 【0081】 "Event information" refers to information related to an event, including data on the specific date and time, location, and expected number of participants. 【0082】 "Weather information" refers to data on meteorological conditions such as temperature, probability of precipitation, and wind speed. 【0083】 "Information acquisition means" refers to means that have the function of collecting event information and weather information via the internet. 【0084】 "Analysis means" refers to means that have the function of processing data to predict fluctuations in demand based on collected event information and weather information. 【0085】 A "determination of allocation means" is a means that has the function of determining the optimal allocation of means of transport based on predicted demand. 【0086】 A "notification mechanism" is a means that has the function of notifying relevant users of optimized placement information. 【0087】 "Means for self-adjusting travel routes based on real-time demand forecasts" refers to means that have the function of dynamically adjusting the travel route of a means of transportation in response to fluctuations in demand. 【0088】 This invention embodies a system that forecasts demand based on event information and weather information, and dynamically adjusts the routes of means of transportation. This system mainly consists of a server, terminals, and users who utilize them. 【0089】 The server collects event and weather information via the internet. Event information includes the event name, date and time, location, and expected number of attendees, while weather information includes temperature, probability of precipitation, and wind speed. This information is continuously updated via an API and retrieved as real-time data. 【0090】 The collected information is analyzed on the server. For example, machine learning algorithms are implemented using Python or the scikit-learn library. Based on demand data related to past events and weather patterns, fluctuations in demand are predicted. This analysis forms the basis for efficiently adjusting the supply and demand of transportation in specific locations and time periods. 【0091】 Based on predicted demand, the server determines the optimal travel route. This allows the mode of transport to dynamically adjust its route to areas with high demand, efficiently meeting user needs. At this time, the travel route is dynamically adjusted based on real-time fluctuating demand information, reducing wasted travel and waiting times. 【0092】 Optimized information is sent from the server to the device. The device is a smartphone or tablet, and the user can receive the notification through an easily understandable interface. For example, an intuitive message such as "Demand will increase in the city center at 9 PM" is sent as a push notification. 【0093】 As a concrete example, consider a scenario where a large-scale concert is being held in the city, a sudden rain shower is expected, and transportation demand increases. In this case, the server predicts the demand for transportation based on this information and notifies the user. As a result, the user can arrange for passengers to wait at appropriate locations. 【0094】 An example of a prompt message is, "The predicted probability of rain at 7 PM today is 80%. How should transportation be arranged?" This demonstrates a system that efficiently utilizes transportation options using a generative AI model. 【0095】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0096】 Step 1: 【0097】 The server collects event and weather information from APIs via the internet. Specifically, the server sends requests to the event service API and weather service API and retrieves data in JSON format. This input data includes event name, date and time, probability of precipitation, etc., and this information is stored in an internal database as output. 【0098】 Step 2: 【0099】 The server uses the acquired data to perform demand forecasting. Specifically, it employs a machine learning model built on the server based on past events and weather data. The generative AI model takes the acquired event information and weather information as input and outputs forecast data for travel demand at a specific time and location. For example, if a popular concert and a high probability of rain are input, demand is predicted to increase. 【0100】 Step 3: 【0101】 The server determines the optimal allocation of transportation methods based on demand forecasts. Specifically, an algorithm within the server takes the forecasted demand data as input, performs optimization calculations, and outputs information on the optimal allocation of each transportation method. This makes it possible to suggest travel routes to specific locations. 【0102】 Step 4: 【0103】 The server notifies the terminal of optimized placement information. Specifically, the server uses a protocol to send push notifications to the terminal. It uses placement decision information as input and displays a notification message such as "Demand will increase in the city center at 9 PM" on the terminal screen as output. 【0104】 Step 5: 【0105】 Based on notifications from their devices, users take appropriate action to arrange their mode of transportation. Specifically, users check the notification content displayed on their devices and move their mode of transportation to the high-demand location indicated in the notification. This enables efficient use of transportation. 【0106】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0107】 This invention is a system that combines information acquisition means, analysis means, placement determination means, and notification means with an emotion engine that recognizes the user's emotions. 【0108】 First, the server collects data from the event information API and weather forecast API as before, obtaining event and weather information. This data is updated in real time and stored in the server's database. In addition, the sentiment engine obtains user sentiment information from data sources such as social media and user feedback, and analyzes its content. 【0109】 The server then uses the event end time, probability of precipitation, and sentiment data analyzed by the sentiment engine to forecast demand. The sentiment engine identifies emotions such as joy, dissatisfaction, and anxiety from, for example, users' social media posts and reviews, and evaluates their impact on ride demand. This allows the analysis system to perform more accurate demand forecasts. 【0110】 Next, the server determines the optimal transportation arrangement based on the analysis results. In this process, a dispatch strategy tailored to the user's emotional state is considered. For example, if many event participants are deeply moved, a surge in demand is expected immediately after the event ends, so taxis are concentrated during that time. 【0111】 Ultimately, the terminal receives the location information and notifies the taxi driver, who is the user. This notification includes detailed dispatch instructions based on predicted demand, as well as advice to help the driver efficiently meet customer needs. For example, it might include information such as, "Demand is expected to be high near the event venue exit because many users are satisfied with the event." 【0112】 This system enables transportation managers to make quick and appropriate decisions based on customer sentiment and event circumstances, resulting in improved customer satisfaction and optimized operational efficiency. 【0113】 The following describes the processing flow. 【0114】 Step 1: 【0115】 The server connects to an event information API and a weather forecast API to retrieve the latest event and weather information. The retrieved data is converted to JSON format and stored in the database. 【0116】 Step 2: 【0117】 The server uses an emotion engine to retrieve user posts from social media and user review sites. Then, it uses natural language processing technology to analyze the user's emotions from the posts and classify them into categories such as positive, negative, and neutral. 【0118】 Step 3: 【0119】 The server integrates event information, weather information, and sentiment data to perform demand forecasting. It calculates the expected level of ride demand as the event end time approaches or when bad weather is predicted, referencing ride data from similar past situations. Sentiment data has a particularly significant impact; if users are satisfied with the event, the predicted demand is expected to increase further. 【0120】 Step 4: 【0121】 The server determines the optimal allocation of transportation based on predicted demand. This includes planning to concentrate vehicles in high-demand areas during specific demand times. For example, if there is a high level of negative emotion, dispatching vehicles earlier than usual may be considered. 【0122】 Step 5: 【0123】 The terminal receives the dispatch plan provided by the server and sends a push notification to the taxi driver, who is the user. The notification provides specific information such as recommended waiting locations and times when increased demand is expected. For example, it may include a message such as, "We expect an increase in passengers around the event venue between 6 PM and 8 PM." 【0124】 In this way, the entire system works together to aim for efficient dispatching and high customer satisfaction. 【0125】 (Example 2) 【0126】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0127】 Current transportation management systems struggle to accurately reflect demand fluctuations based on user emotions, in addition to demand forecasts based on events and weather conditions. This can lead to delays and inaccurate responses to customer needs. Furthermore, efficiently managing the allocation of resources such as taxis and buses is difficult. Therefore, a new method is needed to maximize operational efficiency while improving customer satisfaction. 【0128】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0129】 In this invention, the server includes data acquisition means for acquiring event data and weather data, sentiment analysis means for collecting and analyzing sentiment information, and estimation means for performing supply and demand forecasting. This makes it possible to forecast demand that takes user sentiment into account and to arrange the optimal means of transportation based on that forecast. 【0130】 "Data acquisition means" refers to a device or configuration that provides the function of automatically collecting event data and weather data from external sources. 【0131】 "Emotional analysis means" refers to a device or configuration that collects user emotional information from social media and feedback data and provides a function for analyzing this information. 【0132】 A "predictive means" is a device or configuration that provides the functionality to predict demand at a specific time and place based on acquired event data, weather data, and sentiment information. 【0133】 A "distribution strategy determination means" is a device or configuration that provides the function of determining the optimal distribution of transportation means based on demand forecasts and formulating that strategy. 【0134】 "Information transmission means" refers to a device or configuration that provides a function for notifying a user or device of optimized resource allocation information and for transmitting necessary instructions. 【0135】 This invention is a system that integrates data acquisition, sentiment analysis, demand forecasting, resource allocation optimization, and information transmission related to these processes. The specific configuration for implementing this system is described below. 【0136】 The server operates on cloud infrastructure. Event information is retrieved via the event management platform's API, and weather information is collected via the weather information service's API. Specific examples include the Eventbrite API and the OpenWeatherMap API. This data is updated in real time and stored in a database on the server. 【0137】 Next, the server uses sentiment analysis tools to obtain sentiment information from social media and user feedback. This involves using SNS APIs and natural language processing services to analyze the content of posts and generate sentiment scores. Specifically, the Twitter API and Google Cloud Natural Language API can be used. 【0138】 The server performs demand forecasting based on acquired event information, weather information, and sentiment information. This demand forecasting is performed using statistical analysis methods and machine learning algorithms. Based on the forecast results, a resource allocation strategy determination mechanism is activated to determine the optimal resource allocation. For example, a strategy is formulated to identify times and locations where demand is high and to concentrate transportation resources on those locations. 【0139】 The terminal is a mobile device for drivers that receives resource allocation information sent from the server and notifies the drivers. This notification includes allocation instructions based on demand forecasts, supporting drivers in responding appropriately. A specific example of a prompt message would be: "To determine the optimal taxi allocation after the event, please perform a demand forecast that takes into account user sentiment information and weather forecasts, and then present an allocation strategy based on the results." 【0140】 In this way, the entire system operates efficiently, enabling rapid and effective optimization of transportation resources in response to demand. 【0141】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0142】 Step 1: 【0143】 The server retrieves event and weather data using APIs. The input data consists of API responses from the event management platform and weather information service. The server stores this data in a database in real time, preparing it for the next stage of processing. Specifically, it sends HTTP requests, parses the retrieved JSON data, and saves it to the database. 【0144】 Step 2: 【0145】 The server collects user sentiment information from social media using sentiment analysis tools. The input is posted data obtained via social media APIs. The server analyzes these posts using natural language processing techniques and calculates sentiment scores. The output is the analyzed sentiment score and category (e.g., happy, unhappy). Specifically, it inputs text data into a sentiment analysis model and calculates a score indicating whether the content is positive or negative. 【0146】 Step 3: 【0147】 The server performs demand forecasting based on acquired event data, weather data, and sentiment information. The input is all data collected to date. The server applies machine learning algorithms to build demand forecasting models for each time and location. The output is a list of predicted demand by time and region. Specifically, the process involves inputting data into the algorithm and generating a forecasting model. 【0148】 Step 4: 【0149】 The server determines the optimal resource allocation based on demand forecasts. The input is newly forecasted demand data. The server identifies areas and time periods where demand is expected to be high and determines an allocation strategy accordingly. The output is resource allocation decision information. Specifically, it calculates the optimal resource allocation based on an allocation algorithm and sets the instructions for that allocation. 【0150】 Step 5: 【0151】 The terminal notifies the taxi driver of resource placement information received from the server. The input is resource placement instructions from the server. The terminal pushes a notification to the driver's device, instructing them to be at the appropriate location and time. The output is a dispatch instruction message to the taxi driver. Specifically, it sends a notification to the driver via a mobile application and displays the instructions on the screen. 【0152】 (Application Example 2) 【0153】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0154】 Modern large-scale events and temporary increases in urban traffic demand are difficult to address effectively using conventional methods. In this context, there is a need to ensure smooth traffic flow by forecasting demand based on people's emotions, in addition to weather and event information, in order to provide optimal transportation allocation and effective advice to users. 【0155】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0156】 In this invention, the server includes information acquisition means for obtaining event information, information acquisition means for obtaining weather information, and sentiment acquisition means for collecting user sentiment data. This enables highly accurate demand forecasting and optimal transportation arrangement based on the collected data. 【0157】 "Event information" refers to detailed information about a specific event or gathering, such as the date, time, location, and expected number of participants. 【0158】 "Weather information" refers to information about current and future weather conditions in a specific region. 【0159】 "User sentiment data" refers to data that indicates the emotional state of individual users, obtained through social media and feedback. 【0160】 "Demand forecasting" is the process of predicting the need for a service or product at a specific time and place, based on collected data. 【0161】 "The arrangement of means of transport" refers to the efficient allocation of transportation resources needed at a specific location and time. 【0162】 "Notification means" refers to a method or device for communicating information acquired by the system or analysis results to the user. 【0163】 In one embodiment of this invention, the server utilizes an information acquisition means to obtain event information and weather information. This means uses an API provided by a cloud service provider to collect real-time data. Furthermore, as an emotion acquisition means to collect user emotion data, it collects data from social media platforms and evaluates the emotional state using an emotion analysis API. 【0164】 The server uses the acquired information to perform data processing for demand forecasting. Here, it processes large amounts of data using AWS (registered trademark) cloud infrastructure and executes scripts written in programming languages ​​such as Python. The demand forecasting algorithm analyzes historical data and current sentiment data to calculate the optimal allocation of transportation methods. 【0165】 The device provides users with notifications based on optimized location information sent from the server. These notifications arrive as push notifications on the user's smartphone and include suggestions for specific modes of transportation and routes home. 【0166】 This system allows users to receive optimized real-time traffic information, helping them to travel smoothly. For example, if the system detects that many visitors are satisfied at the end of a music festival, it can plan the timely placement of shuttle buses based on predicted demand and send users a notification such as, "The bus at the south exit is crowded, so we recommend taking the bus arriving in 5 minutes." 【0167】 An example of a prompt for a generative AI model is: "If participants in this event show signs of excitement, what approaches would be effective in improving the efficiency of their use of public transportation after the event?" 【0168】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0169】 Step 1: 【0170】 The server retrieves event and weather information through various APIs. This includes information about the date, time, location, and scale of events, as well as current and predicted weather conditions. Input is raw data from the APIs, and output is structured data stored in a database. This data is used for subsequent predictive analysis. 【0171】 Step 2: 【0172】 The server collects user sentiment data from social media. This process filters for specific keywords and hashtags and analyzes the emotional state of users from their text posts. The input is text information from social media data, and the output is numerical data from sentiment analysis (e.g., sentiment scores such as excited, satisfied, or dissatisfied). 【0173】 Step 3: 【0174】 The server uses collected event information, weather information, and sentiment data to forecast demand. This forecast utilizes machine learning algorithms to model fluctuations in transportation demand. The input is the data set obtained in the previous step, and the output is the predicted demand as numerical and time-series data. Based on this forecast, the allocation of transportation methods is planned. 【0175】 Step 4: 【0176】 The server calculates the optimal allocation of transportation methods based on the demand forecast results. An algorithm is used to efficiently allocate accessible transportation resources. The input is the demand forecast result, and the output is the optimized transportation allocation plan. 【0177】 Step 5: 【0178】 The terminal receives optimal placement information sent from the server and notifies the user. This notification includes detailed information about the optimal travel route and recommended modes of transport. The input is placement information from the server, and the output is a visual or auditory notification from the user. 【0179】 Step 6: 【0180】 The user receives notifications from their device and takes action to select the most suitable mode of transportation. This enables the user to travel efficiently based on real-time information. The input is the notification from the device, and the output is the user's action choice. 【0181】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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. 【0182】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0183】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0184】 [Second Embodiment] 【0185】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0186】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0187】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0188】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0189】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0190】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0191】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0192】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0193】 The specific processing program 56 is an example of a "program" relating to the technology of this 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 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. 【0194】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0195】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0196】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0197】 This invention is implemented as a system comprising four elements: an information acquisition means, an analysis means, a placement determination means, and a notification means. 【0198】 First, the server collects event and weather information via the internet through websites and APIs. Event information includes data such as the event name, date and time, venue, and expected number of attendees. Weather information includes meteorological data such as temperature, probability of precipitation, and wind speed. By collecting this information, the server can obtain the latest event and weather information in real time. 【0199】 Next, the server analyzes the collected data using machine learning and statistical models. Specifically, the server utilizes passenger demand data related to past events and weather patterns to predict future demand fluctuations. For example, if a concert by a popular artist is scheduled or if a sudden weather change is expected, the server will take these factors into account when analyzing demand. 【0200】 Subsequently, based on the predicted demand, the server utilizes a placement determination mechanism to determine the optimal allocation of transportation methods (such as taxis and vehicles). This proposes an efficient dispatch route to meet the demand at a specific time and location. For example, at the end of a large-scale event, the server might instruct a placement that concentrates a large number of taxis around the venue. 【0201】 Finally, the terminal receives this location information. The terminal notifies the taxi driver, who is the user, of the information in an intuitively understandable format. Specifically, a push notification such as "Demand will increase around XX Hall at 9 PM" is sent via a smartphone or other device. In this way, the driver can efficiently position themselves to pick up passengers. 【0202】 This series of processes allows taxi companies to avoid unnecessary travel while quickly responding to customer demand, ultimately leading to improved profitability. 【0203】 The following describes the processing flow. 【0204】 Step 1: 【0205】 The server sets up scheduled tasks to send requests to event information APIs and weather forecast APIs to retrieve the latest event and weather information. It authenticates using API keys, receives the data in JSON format, and stores it in an internal database. 【0206】 Step 2: 【0207】 The server performs the necessary preprocessing to analyze the acquired event and weather information. Specifically, it filters the event information for the target area and extracts important attributes such as start and end times and the number of participants. For weather information, it identifies data such as the probability of precipitation and temperature. 【0208】 Step 3: 【0209】 The server performs demand forecasting using machine learning models and statistical methods based on pre-processed data. It analyzes predicted demand fluctuations by referencing occupancy rate data from similar past events. This analysis identifies high-demand areas at specific times and locations. 【0210】 Step 4: 【0211】 The server uses the analysis results to run a deployment logic and generate an optimal transportation deployment plan. This includes setting up dispatch routes and waiting points to efficiently meet the predicted demand within a specified time. 【0212】 Step 5: 【0213】 The terminal receives location information transmitted from the server and notifies the taxi driver, who is the user. Push notifications are displayed on a smartphone app or in-vehicle terminal, providing important information through an intuitive interface. The notifications indicate dispatch demand for specific times and locations, enabling drivers to respond quickly. 【0214】 (Example 1) 【0215】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0216】 Efficient transportation deployment needs to be sensitive to demand changes caused by events and weather fluctuations. However, conventional systems have difficulty with real-time demand forecasting and flexible deployment decisions, sometimes leading to excessive waiting times and increased costs. Furthermore, there was a challenge in that specific measures for maximizing the operational efficiency of transportation were limited and lacked flexibility. 【0217】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0218】 In this invention, the server includes an information acquisition device for acquiring event information, an information acquisition device for acquiring weather information, a data analysis device for analyzing the acquired event information and weather information and making demand forecasts, a placement determination device for optimizing the placement of transportation means based on the demand forecast, a notification device for presenting optimized placement information, an analysis device for predicting future demand fluctuations using machine learning models and statistical models, and an optimization device for calculating the optimal placement of transportation means based on past data. This enables real-time demand forecasting and efficient placement determination. 【0219】 An "information acquisition device" refers to hardware or software used to collect data from external sources, and has the function of acquiring specific event information or weather information. 【0220】 A "data analysis device" refers to hardware or software that has the function of analyzing acquired data and applying statistical models or machine learning algorithms to forecast demand. 【0221】 A "distribution determination device" refers to hardware or software used to calculate the optimal distribution of transportation methods based on analyzed demand forecast data. 【0222】 A "notification device" refers to hardware or software that displays or transmits transportation arrangement information calculated by a server to a user. 【0223】 A "machine learning model" is a set of algorithms used to learn data patterns and make predictions about future data, enabling highly accurate analysis. 【0224】 A "statistical model" is an algorithm that incorporates mathematical methods used to analyze data and extract useful information from it. 【0225】 An "optimization device" is hardware or software used to perform calculations to determine the optimal placement of components to obtain the best results under specific conditions. 【0226】 This invention is a system that enables the efficient arrangement of transportation means through the cooperation of a server, terminal, and user. First, the server uses an information acquisition device to collect event information and weather information from publicly available data sources on the internet. In this process, it utilizes scripting languages ​​such as Python and JavaScript to obtain the necessary data through APIs and web scraping. The collected data is stored in a database in real time. 【0227】 Next, the server uses a data analysis device to analyze the acquired event and weather information. This analysis utilizes machine learning models and statistical models to learn past data patterns and predict future demand fluctuations. Specific technologies used include the Python scikit-learn library and TensorFlow. 【0228】 Based on the analysis results, the server uses a placement decision device to calculate the optimal placement of transportation methods. Here, optimization libraries such as Google OR-Tools are used to simulate efficient vehicle dispatch routes. 【0229】 Subsequently, the terminal receives optimized placement information from the server via a notification device and displays it to the user. Specifically, Firebase Cloud Messaging (FCM) is used to send the information as a push notification to the smartphone. An example message might be, "Demand will increase around XX Hall at 9 PM." This notification allows users to operate their transportation more efficiently. 【0230】 A concrete example is a scenario where a terminal notifies the server of an expected increase in demand at the end of an event, and the driver, as the user, uses this information to determine the optimal location. In this way, real-time demand forecasting and placement optimization are achieved. An example of a prompt message would be, "Tell me how to determine the optimal taxi placement at the end of a large-scale event." 【0231】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0232】 Step 1: 【0233】 The server uses an information acquisition device to collect event and weather information from the internet. It uses API endpoints and web page URLs as input. The server's specific actions involve using a Python script to send API requests and parsing the returned JSON data. The output includes information such as event name, date and time, venue, expected number of attendees, temperature, probability of precipitation, and wind speed, which are then stored in a database. 【0234】 Step 2: 【0235】 The server uses a data analysis device to analyze collected event and weather information. The input consists of event and weather information stored in a database. The server uses a machine learning model to forecast demand. Specifically, it performs regression analysis using the scikit-learn library to predict demand fluctuations. The output is numerical data indicating increases or decreases in demand, which is used to determine the next deployment. 【0236】 Step 3: 【0237】 The server uses a placement determination device to determine the optimal placement of transportation methods based on predicted demand. The input is the demand forecast data obtained in step 2. The specific operation involves solving an optimization problem using tools such as Google OR-Tools to calculate the appropriate vehicle dispatch route. The output is the placement and route information of transportation methods at a specific time and location. 【0238】 Step 4: 【0239】 The terminal receives optimized placement information from the server via a notification device and notifies the user. The input is the placement information calculated in step 3. The specific action the terminal takes is to send a push notification to a smartphone or device using Firebase Cloud Messaging. The output will display something like, "Demand will be high around XX Hall at 9 PM." 【0240】 Step 5: 【0241】 The user operates the transportation based on notifications. The input is the notification information received from the terminal. The user's specific action is to refer to the notification, position the transportation at a specific location, and efficiently pick up passengers. This allows the user to select the optimal route. 【0242】 (Application Example 1) 【0243】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0244】 Conventional transportation scheduling systems struggle to respond flexibly to real-time demand fluctuations, sometimes resulting in wasted travel and missed opportunities. To solve this problem, more accurate demand forecasting and a mechanism for dynamically adjusting routes are necessary. 【0245】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0246】 In this invention, the server includes information acquisition means for acquiring event information, information acquisition means for acquiring weather information, analysis means for analyzing the acquired event information and weather information and making demand forecasts, and means for self-adjusting travel routes based on demand forecasts. This enables the efficient deployment of travel means in real time according to demand. 【0247】 "Event information" refers to information related to an event, including data on the specific date and time, location, and expected number of participants. 【0248】 "Weather information" refers to data on meteorological conditions such as temperature, probability of precipitation, and wind speed. 【0249】 "Information acquisition means" refers to means that have the function of collecting event information and weather information via the internet. 【0250】 "Analysis means" refers to means that have the function of processing data to predict fluctuations in demand based on collected event information and weather information. 【0251】 A "determination of allocation means" is a means that has the function of determining the optimal allocation of means of transport based on predicted demand. 【0252】 A "notification mechanism" is a means that has the function of notifying relevant users of optimized placement information. 【0253】 "Means for self-adjusting travel routes based on real-time demand forecasts" refers to means that have the function of dynamically adjusting the travel route of a means of transportation in response to fluctuations in demand. 【0254】 This invention embodies a system that forecasts demand based on event information and weather information, and dynamically adjusts the routes of means of transportation. This system mainly consists of a server, terminals, and users who utilize them. 【0255】 The server collects event and weather information via the internet. Event information includes the event name, date and time, location, and expected number of attendees, while weather information includes temperature, probability of precipitation, and wind speed. This information is continuously updated via an API and retrieved as real-time data. 【0256】 The collected information is analyzed on the server. For example, machine learning algorithms are implemented using Python or the scikit-learn library. Based on demand data related to past events and weather patterns, fluctuations in demand are predicted. This analysis forms the basis for efficiently adjusting the supply and demand of transportation in specific locations and time periods. 【0257】 Based on predicted demand, the server determines the optimal travel route. This allows the mode of transport to dynamically adjust its route to areas with high demand, efficiently meeting user needs. At this time, the travel route is dynamically adjusted based on real-time fluctuating demand information, reducing wasted travel and waiting times. 【0258】 Optimized information is sent from the server to the device. The device is a smartphone or tablet, and the user can receive the notification through an easily understandable interface. For example, an intuitive message such as "Demand will increase in the city center at 9 PM" is sent as a push notification. 【0259】 As a concrete example, consider a scenario where a large-scale concert is being held in the city, a sudden rain shower is expected, and transportation demand increases. In this case, the server predicts the demand for transportation based on this information and notifies the user. As a result, the user can arrange for passengers to wait at appropriate locations. 【0260】 An example of a prompt message is, "The predicted probability of rain at 7 PM today is 80%. How should transportation be arranged?" This demonstrates a system that efficiently utilizes transportation options using a generative AI model. 【0261】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0262】 Step 1: 【0263】 The server collects event and weather information from APIs via the internet. Specifically, the server sends requests to the event service API and weather service API and retrieves data in JSON format. This input data includes event name, date and time, probability of precipitation, etc., and this information is stored in an internal database as output. 【0264】 Step 2: 【0265】 The server uses the acquired data to perform demand forecasting. Specifically, it employs a machine learning model built on the server based on past events and weather data. The generative AI model takes the acquired event information and weather information as input and outputs forecast data for travel demand at a specific time and location. For example, if a popular concert and a high probability of rain are input, demand is predicted to increase. 【0266】 Step 3: 【0267】 The server determines the optimal allocation of transportation methods based on demand forecasts. Specifically, an algorithm within the server takes the forecasted demand data as input, performs optimization calculations, and outputs information on the optimal allocation of each transportation method. This makes it possible to suggest travel routes to specific locations. 【0268】 Step 4: 【0269】 The server notifies the terminal of optimized placement information. Specifically, the server uses a protocol to send push notifications to the terminal. It uses placement decision information as input and displays a notification message such as "Demand will increase in the city center at 9 PM" on the terminal screen as output. 【0270】 Step 5: 【0271】 Based on notifications from their devices, users take appropriate action to arrange their mode of transportation. Specifically, users check the notification content displayed on their devices and move their mode of transportation to the high-demand location indicated in the notification. This enables efficient use of transportation. 【0272】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0273】 This invention is a system that combines information acquisition means, analysis means, placement determination means, and notification means with an emotion engine that recognizes the user's emotions. 【0274】 First, the server collects data from the event information API and weather forecast API as before, obtaining event and weather information. This data is updated in real time and stored in the server's database. In addition, the sentiment engine obtains user sentiment information from data sources such as social media and user feedback, and analyzes its content. 【0275】 The server then uses the event end time, probability of precipitation, and sentiment data analyzed by the sentiment engine to forecast demand. The sentiment engine identifies emotions such as joy, dissatisfaction, and anxiety from, for example, users' social media posts and reviews, and evaluates their impact on ride demand. This allows the analysis system to perform more accurate demand forecasts. 【0276】 Next, the server determines the optimal transportation arrangement based on the analysis results. In this process, a dispatch strategy tailored to the user's emotional state is considered. For example, if many event participants are deeply moved, a surge in demand is expected immediately after the event ends, so taxis are concentrated during that time. 【0277】 Ultimately, the terminal receives the location information and notifies the taxi driver, who is the user. This notification includes detailed dispatch instructions based on predicted demand, as well as advice to help the driver efficiently meet customer needs. For example, it might include information such as, "Demand is expected to be high near the event venue exit because many users are satisfied with the event." 【0278】 This system enables transportation managers to make quick and appropriate decisions based on customer sentiment and event circumstances, resulting in improved customer satisfaction and optimized operational efficiency. 【0279】 The following describes the process flow. 【0280】 Step 1: 【0281】 The server connects to the event information API and the weather forecast API to obtain the latest event information and weather information. The acquired data is converted into JSON format and stored in the database. 【0282】 Step 2: 【0283】 The server uses the sentiment engine to obtain user post data from social media and user review sites. Then, it analyzes the user's sentiment from the posts using natural language processing technology and classifies them into categories such as positive, negative, and neutral. 【0284】 Step 3: 【0285】 The server integrates event information, weather information, and sentiment data and performs demand prediction. Here, while referring to the past riding data in similar situations, as the event end time approaches or when bad weather is predicted, it calculates the predicted riding demand. Sentiment data has a particularly important influence, and it is speculated that the predicted demand will further increase when users are satisfied with the event. 【0286】 Step 4: 【0287】 The server determines the optimal allocation of transportation means based on the predicted demand. This includes plans to focus on allocating vehicles to high-demand areas at specific demand times. For example, when there is a lot of negative sentiment, earlier vehicle dispatching than usual is considered. 【0288】 [[ID=3​​​​The terminal receives the dispatch plan provided by the server and sends a push notification to the taxi driver, who is the user. The notification provides specific information such as recommended waiting locations and times when increased demand is expected. For example, it may include a message such as, "We expect an increase in passengers around the event venue between 6 PM and 8 PM." 【0290】 In this way, the entire system works together to aim for efficient dispatching and high customer satisfaction. 【0291】 (Example 2) 【0292】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0293】 Current transportation management systems struggle to accurately reflect demand fluctuations based on user emotions, in addition to demand forecasts based on events and weather conditions. This can lead to delays and inaccurate responses to customer needs. Furthermore, efficiently managing the allocation of resources such as taxis and buses is difficult. Therefore, a new method is needed to maximize operational efficiency while improving customer satisfaction. 【0294】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0295】 In this invention, the server includes data acquisition means for acquiring event data and weather data, sentiment analysis means for collecting and analyzing sentiment information, and estimation means for performing supply and demand forecasting. This makes it possible to forecast demand that takes user sentiment into account and to arrange the optimal means of transportation based on that forecast. 【0296】 "Data acquisition means" refers to a device or configuration that provides the function of automatically collecting event data and weather data from external sources. 【0297】 "Emotional analysis means" refers to a device or configuration that collects user emotional information from social media and feedback data and provides a function for analyzing this information. 【0298】 A "predictive means" is a device or configuration that provides the functionality to predict demand at a specific time and place based on acquired event data, weather data, and sentiment information. 【0299】 A "distribution strategy determination means" is a device or configuration that provides the function of determining the optimal distribution of transportation means based on demand forecasts and formulating that strategy. 【0300】 "Information transmission means" refers to a device or configuration that provides a function for notifying a user or device of optimized resource allocation information and for transmitting necessary instructions. 【0301】 This invention is a system that integrates data acquisition, sentiment analysis, demand forecasting, resource allocation optimization, and information transmission related to these processes. The specific configuration for implementing this system is described below. 【0302】 The server operates on cloud infrastructure. Event information is retrieved via the event management platform's API, and weather information is collected via the weather information service's API. Specific examples include the Eventbrite API and the OpenWeatherMap API. This data is updated in real time and stored in a database on the server. 【0303】 Next, the server uses sentiment analysis tools to obtain sentiment information from social media and user feedback. This involves using SNS APIs and natural language processing services to analyze the content of posts and generate sentiment scores. Specifically, the Twitter API and Google Cloud Natural Language API can be used. 【0304】 The server performs demand prediction based on the acquired event information, weather information, and sentiment information. This demand prediction is carried out using statistical analysis methods and machine learning algorithms. Based on the prediction results, the allocation strategy determination means for determining the optimal resource allocation operates. For example, a strategy is formulated to identify the time periods and locations where demand is increasing and to focus on allocating transportation resources to those locations. 【0305】 The terminal is a mobile device for drivers, receives the resource allocation information sent from the server, and notifies the driver. This notification includes an allocation instruction based on demand prediction and supports the driver to respond appropriately. Specific examples of the prompt text include content such as "In order to determine the optimal taxi allocation after an event, perform demand prediction considering the user's sentiment information and weather forecast, and present an allocation strategy based on the results." 【0306】 In this way, the entire system is operated efficiently, and it becomes possible to optimize transportation resources quickly and effectively according to demand. 【0307】 The flow of the specific process in Example 2 will be described using FIG. 13. 【0308】 Step 1: 【0309】 The server acquires event data and weather data using the API. The input data is an API response from an event management platform and a weather information providing service. The server stores this data in the database in real time to prepare for the next stage of processing. As a specific operation, an HTTP request is sent, and the acquired JSON data is analyzed and saved in the database. 【0310】 Step 2: 【0311】 The server collects user sentiment information from social media using sentiment analysis tools. The input is posted data obtained via social media APIs. The server analyzes these posts using natural language processing techniques and calculates sentiment scores. The output is the analyzed sentiment score and category (e.g., happy, unhappy). Specifically, it inputs text data into a sentiment analysis model and calculates a score indicating whether the content is positive or negative. 【0312】 Step 3: 【0313】 The server performs demand forecasting based on acquired event data, weather data, and sentiment information. The input is all data collected to date. The server applies machine learning algorithms to build demand forecasting models for each time and location. The output is a list of predicted demand by time and region. Specifically, the process involves inputting data into the algorithm and generating a forecasting model. 【0314】 Step 4: 【0315】 The server determines the optimal resource allocation based on demand forecasts. The input is newly forecasted demand data. The server identifies areas and time periods where demand is expected to be high and determines an allocation strategy accordingly. The output is resource allocation decision information. Specifically, it calculates the optimal resource allocation based on an allocation algorithm and sets the instructions for that allocation. 【0316】 Step 5: 【0317】 The terminal notifies the taxi driver of resource placement information received from the server. The input is resource placement instructions from the server. The terminal pushes a notification to the driver's device, instructing them to be at the appropriate location and time. The output is a dispatch instruction message to the taxi driver. Specifically, it sends a notification to the driver via a mobile application and displays the instructions on the screen. 【0318】 (Application Example 2) 【0319】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0320】 Modern large-scale events and temporary increases in urban traffic demand are difficult to address effectively using conventional methods. In this context, there is a need to ensure smooth traffic flow by forecasting demand based on people's emotions, in addition to weather and event information, in order to provide optimal transportation allocation and effective advice to users. 【0321】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0322】 In this invention, the server includes information acquisition means for obtaining event information, information acquisition means for obtaining weather information, and sentiment acquisition means for collecting user sentiment data. This enables highly accurate demand forecasting and optimal transportation arrangement based on the collected data. 【0323】 "Event information" refers to detailed information about a specific event or gathering, such as the date, time, location, and expected number of participants. 【0324】 "Weather information" refers to information about current and future weather conditions in a specific region. 【0325】 "User sentiment data" refers to data that indicates the emotional state of individual users, obtained through social media and feedback. 【0326】 "Demand forecasting" is the process of predicting the need for a service or product at a specific time and place, based on collected data. 【0327】 "The arrangement of means of transport" refers to the efficient allocation of transportation resources needed at a specific location and time. 【0328】 "Notification means" refers to a method or device for communicating information acquired by the system or analysis results to the user. 【0329】 In one embodiment of this invention, the server utilizes an information acquisition means to obtain event information and weather information. This means uses an API provided by a cloud service provider to collect real-time data. Furthermore, as an emotion acquisition means to collect user emotion data, it collects data from social media platforms and evaluates the emotional state using an emotion analysis API. 【0330】 The server uses the acquired information to perform data processing for demand forecasting. Here, it processes large amounts of data using AWS cloud infrastructure and executes scripts written in programming languages ​​such as Python. The demand forecasting algorithm analyzes historical data and current sentiment data to calculate the optimal allocation of transportation methods. 【0331】 The device provides users with notifications based on optimized location information sent from the server. These notifications arrive as push notifications on the user's smartphone and include suggestions for specific modes of transportation and routes home. 【0332】 This system allows users to receive optimized real-time traffic information, helping them to travel smoothly. For example, if the system detects that many visitors are satisfied at the end of a music festival, it can plan the timely placement of shuttle buses based on predicted demand and send users a notification such as, "The bus at the south exit is crowded, so we recommend taking the bus arriving in 5 minutes." 【0333】 An example of a prompt for a generative AI model is: "If participants in this event show signs of excitement, what approaches would be effective in improving the efficiency of their use of public transportation after the event?" 【0334】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0335】 Step 1: 【0336】 The server retrieves event and weather information through various APIs. This includes information about the date, time, location, and scale of events, as well as current and predicted weather conditions. Input is raw data from the APIs, and output is structured data stored in a database. This data is used for subsequent predictive analysis. 【0337】 Step 2: 【0338】 The server collects user sentiment data from social media. This process filters for specific keywords and hashtags and analyzes the emotional state of users from their text posts. The input is text information from social media data, and the output is numerical data from sentiment analysis (e.g., sentiment scores such as excited, satisfied, or dissatisfied). 【0339】 Step 3: 【0340】 The server uses collected event information, weather information, and sentiment data to forecast demand. This forecast utilizes machine learning algorithms to model fluctuations in transportation demand. The input is the data set obtained in the previous step, and the output is the predicted demand as numerical and time-series data. Based on this forecast, the allocation of transportation methods is planned. 【0341】 Step 4: 【0342】 The server calculates the optimal allocation of transportation methods based on the demand forecast results. An algorithm is used to efficiently allocate accessible transportation resources. The input is the demand forecast result, and the output is the optimized transportation allocation plan. 【0343】 Step 5: 【0344】 The terminal receives optimal placement information sent from the server and notifies the user. This notification includes detailed information about the optimal travel route and recommended modes of transport. The input is placement information from the server, and the output is a visual or auditory notification from the user. 【0345】 Step 6: 【0346】 The user receives notifications from their device and takes action to select the most suitable mode of transportation. This enables the user to travel efficiently based on real-time information. The input is the notification from the device, and the output is the user's action choice. 【0347】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0348】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0349】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0350】 [Third Embodiment] 【0351】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0352】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0353】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0354】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0355】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0356】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0357】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0358】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0359】 The specific processing program 56 is an example of a "program" relating to the technology of this 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 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. 【0360】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0361】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0362】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0363】 This invention is implemented as a system comprising four elements: an information acquisition means, an analysis means, a placement determination means, and a notification means. 【0364】 First, the server collects event and weather information via the internet through websites and APIs. Event information includes data such as the event name, date and time, venue, and expected number of attendees. Weather information includes meteorological data such as temperature, probability of precipitation, and wind speed. By collecting this information, the server can obtain the latest event and weather information in real time. 【0365】 Next, the server analyzes the collected data using machine learning and statistical models. Specifically, the server utilizes passenger demand data related to past events and weather patterns to predict future demand fluctuations. For example, if a concert by a popular artist is scheduled or if a sudden weather change is expected, the server will take these factors into account when analyzing demand. 【0366】 Subsequently, based on the predicted demand, the server utilizes a placement determination mechanism to determine the optimal allocation of transportation methods (such as taxis and vehicles). This proposes an efficient dispatch route to meet the demand at a specific time and location. For example, at the end of a large-scale event, the server might instruct a placement that concentrates a large number of taxis around the venue. 【0367】 Finally, the terminal receives this location information. The terminal notifies the taxi driver, who is the user, of the information in an intuitively understandable format. Specifically, a push notification such as "Demand will increase around XX Hall at 9 PM" is sent via a smartphone or other device. In this way, the driver can efficiently position themselves to pick up passengers. 【0368】 This series of processes allows taxi companies to avoid unnecessary travel while quickly responding to customer demand, ultimately leading to improved profitability. 【0369】 The following describes the processing flow. 【0370】 Step 1: 【0371】 The server sets up scheduled tasks to send requests to event information APIs and weather forecast APIs to retrieve the latest event and weather information. It authenticates using API keys, receives the data in JSON format, and stores it in an internal database. 【0372】 Step 2: 【0373】 The server performs the necessary preprocessing to analyze the acquired event and weather information. Specifically, it filters the event information for the target area and extracts important attributes such as start and end times and the number of participants. For weather information, it identifies data such as the probability of precipitation and temperature. 【0374】 Step 3: 【0375】 The server performs demand forecasting using machine learning models and statistical methods based on pre-processed data. It analyzes predicted demand fluctuations by referencing occupancy rate data from similar past events. This analysis identifies high-demand areas at specific times and locations. 【0376】 Step 4: 【0377】 The server uses the analysis results to run a deployment logic and generate an optimal transportation deployment plan. This includes setting up dispatch routes and waiting points to efficiently meet the predicted demand within a specified time. 【0378】 Step 5: 【0379】 The terminal receives location information transmitted from the server and notifies the taxi driver, who is the user. Push notifications are displayed on a smartphone app or in-vehicle terminal, providing important information through an intuitive interface. The notifications indicate dispatch demand for specific times and locations, enabling drivers to respond quickly. 【0380】 (Example 1) 【0381】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0382】 Efficient transportation deployment needs to be sensitive to demand changes caused by events and weather fluctuations. However, conventional systems have difficulty with real-time demand forecasting and flexible deployment decisions, sometimes leading to excessive waiting times and increased costs. Furthermore, there was a challenge in that specific measures for maximizing the operational efficiency of transportation were limited and lacked flexibility. 【0383】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0384】 In this invention, the server includes an information acquisition device for acquiring event information, an information acquisition device for acquiring weather information, a data analysis device for analyzing the acquired event information and weather information and making demand forecasts, a placement determination device for optimizing the placement of transportation means based on the demand forecast, a notification device for presenting optimized placement information, an analysis device for predicting future demand fluctuations using machine learning models and statistical models, and an optimization device for calculating the optimal placement of transportation means based on past data. This enables real-time demand forecasting and efficient placement determination. 【0385】 An "information acquisition device" refers to hardware or software used to collect data from external sources, and has the function of acquiring specific event information or weather information. 【0386】 A "data analysis device" refers to hardware or software that has the function of analyzing acquired data and applying statistical models or machine learning algorithms to forecast demand. 【0387】 A "distribution determination device" refers to hardware or software used to calculate the optimal distribution of transportation methods based on analyzed demand forecast data. 【0388】 A "notification device" refers to hardware or software that displays or transmits transportation arrangement information calculated by a server to a user. 【0389】 A "machine learning model" is a set of algorithms used to learn data patterns and make predictions about future data, enabling highly accurate analysis. 【0390】 A "statistical model" is an algorithm that incorporates mathematical methods used to analyze data and extract useful information from it. 【0391】 An "optimization device" is hardware or software used to perform calculations to determine the optimal placement of components to obtain the best results under specific conditions. 【0392】 This invention is a system that enables the efficient arrangement of transportation means through the cooperation of a server, terminal, and user. First, the server uses an information acquisition device to collect event information and weather information from publicly available data sources on the internet. In this process, it utilizes scripting languages ​​such as Python and JavaScript to obtain the necessary data through APIs and web scraping. The collected data is stored in a database in real time. 【0393】 Next, the server uses a data analysis device to analyze the acquired event and weather information. This analysis utilizes machine learning models and statistical models to learn past data patterns and predict future demand fluctuations. Specific technologies used include the Python scikit-learn library and TensorFlow. 【0394】 Based on the analysis results, the server uses a placement decision device to calculate the optimal placement of transportation methods. Here, optimization libraries such as Google OR-Tools are used to simulate efficient vehicle dispatch routes. 【0395】 Subsequently, the terminal receives optimized placement information from the server via a notification device and displays it to the user. Specifically, Firebase Cloud Messaging (FCM) is used to send the information as a push notification to the smartphone. An example message might be, "Demand will increase around XX Hall at 9 PM." This notification allows users to operate their transportation more efficiently. 【0396】 A concrete example is a scenario where a terminal notifies the server of an expected increase in demand at the end of an event, and the driver, as the user, uses this information to determine the optimal location. In this way, real-time demand forecasting and placement optimization are achieved. An example of a prompt message would be, "Tell me how to determine the optimal taxi placement at the end of a large-scale event." 【0397】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0398】 Step 1: 【0399】 The server uses an information acquisition device to collect event and weather information from the internet. It uses API endpoints and web page URLs as input. The server's specific actions involve using a Python script to send API requests and parsing the returned JSON data. The output includes information such as event name, date and time, venue, expected number of attendees, temperature, probability of precipitation, and wind speed, which are then stored in a database. 【0400】 Step 2: 【0401】 The server uses a data analysis device to analyze collected event and weather information. The input consists of event and weather information stored in a database. The server uses a machine learning model to forecast demand. Specifically, it performs regression analysis using the scikit-learn library to predict demand fluctuations. The output is numerical data indicating increases or decreases in demand, which is used to determine the next deployment. 【0402】 Step 3: 【0403】 The server uses a placement determination device to determine the optimal placement of transportation methods based on predicted demand. The input is the demand forecast data obtained in step 2. The specific operation involves solving an optimization problem using tools such as Google OR-Tools to calculate the appropriate vehicle dispatch route. The output is the placement and route information of transportation methods at a specific time and location. 【0404】 Step 4: 【0405】 The terminal receives optimized placement information from the server via a notification device and notifies the user. The input is the placement information calculated in step 3. The specific action the terminal takes is to send a push notification to a smartphone or device using Firebase Cloud Messaging. The output will display something like, "Demand will be high around XX Hall at 9 PM." 【0406】 Step 5: 【0407】 The user operates the transportation based on notifications. The input is the notification information received from the terminal. The user's specific action is to refer to the notification, position the transportation at a specific location, and efficiently pick up passengers. This allows the user to select the optimal route. 【0408】 (Application Example 1) 【0409】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0410】 Conventional transportation scheduling systems struggle to respond flexibly to real-time demand fluctuations, sometimes resulting in wasted travel and missed opportunities. To solve this problem, more accurate demand forecasting and a mechanism for dynamically adjusting routes are necessary. 【0411】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0412】 In this invention, the server includes information acquisition means for acquiring event information, information acquisition means for acquiring weather information, analysis means for analyzing the acquired event information and weather information and making demand forecasts, and means for self-adjusting travel routes based on demand forecasts. This enables the efficient deployment of travel means in real time according to demand. 【0413】 "Event information" refers to information related to an event, including data on the specific date and time, location, and expected number of participants. 【0414】 "Weather information" refers to data on meteorological conditions such as temperature, probability of precipitation, and wind speed. 【0415】 "Information acquisition means" refers to means that have the function of collecting event information and weather information via the internet. 【0416】 "Analysis means" refers to means that have the function of processing data to predict fluctuations in demand based on collected event information and weather information. 【0417】 A "determination of allocation means" is a means that has the function of determining the optimal allocation of means of transport based on predicted demand. 【0418】 A "notification mechanism" is a means that has the function of notifying relevant users of optimized placement information. 【0419】 "Means for self-adjusting travel routes based on real-time demand forecasts" refers to means that have the function of dynamically adjusting the travel route of a means of transportation in response to fluctuations in demand. 【0420】 This invention embodies a system that forecasts demand based on event information and weather information, and dynamically adjusts the routes of means of transportation. This system mainly consists of a server, terminals, and users who utilize them. 【0421】 The server collects event and weather information via the internet. Event information includes the event name, date and time, location, and expected number of attendees, while weather information includes temperature, probability of precipitation, and wind speed. This information is continuously updated via an API and retrieved as real-time data. 【0422】 The collected information is analyzed on the server. For example, machine learning algorithms are implemented using Python or the scikit-learn library. Based on demand data related to past events and weather patterns, fluctuations in demand are predicted. This analysis forms the basis for efficiently adjusting the supply and demand of transportation in specific locations and time periods. 【0423】 Based on predicted demand, the server determines the optimal travel route. This allows the mode of transport to dynamically adjust its route to areas with high demand, efficiently meeting user needs. At this time, the travel route is dynamically adjusted based on real-time fluctuating demand information, reducing wasted travel and waiting times. 【0424】 Optimized information is sent from the server to the device. The device is a smartphone or tablet, and the user can receive the notification through an easily understandable interface. For example, an intuitive message such as "Demand will increase in the city center at 9 PM" is sent as a push notification. 【0425】 As a concrete example, consider a scenario where a large-scale concert is being held in the city, a sudden rain shower is expected, and transportation demand increases. In this case, the server predicts the demand for transportation based on this information and notifies the user. As a result, the user can arrange for passengers to wait at appropriate locations. 【0426】 An example of a prompt message is, "The predicted probability of rain at 7 PM today is 80%. How should transportation be arranged?" This demonstrates a system that efficiently utilizes transportation options using a generative AI model. 【0427】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0428】 Step 1: 【0429】 The server collects event and weather information from APIs via the internet. Specifically, the server sends requests to the event service API and weather service API and retrieves data in JSON format. This input data includes event name, date and time, probability of precipitation, etc., and this information is stored in an internal database as output. 【0430】 Step 2: 【0431】 The server uses the acquired data to perform demand forecasting. Specifically, it employs a machine learning model built on the server based on past events and weather data. The generative AI model takes the acquired event information and weather information as input and outputs forecast data for travel demand at a specific time and location. For example, if a popular concert and a high probability of rain are input, demand is predicted to increase. 【0432】 Step 3: 【0433】 The server determines the optimal allocation of transportation methods based on demand forecasts. Specifically, an algorithm within the server takes the forecasted demand data as input, performs optimization calculations, and outputs information on the optimal allocation of each transportation method. This makes it possible to suggest travel routes to specific locations. 【0434】 Step 4: 【0435】 The server notifies the terminal of optimized placement information. Specifically, the server uses a protocol to send push notifications to the terminal. It uses placement decision information as input and displays a notification message such as "Demand will increase in the city center at 9 PM" on the terminal screen as output. 【0436】 Step 5: 【0437】 Based on notifications from their devices, users take appropriate action to arrange their mode of transportation. Specifically, users check the notification content displayed on their devices and move their mode of transportation to the high-demand location indicated in the notification. This enables efficient use of transportation. 【0438】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0439】 This invention is a system that combines information acquisition means, analysis means, placement determination means, and notification means with an emotion engine that recognizes the user's emotions. 【0440】 First, the server collects data from the event information API and weather forecast API as before, obtaining event and weather information. This data is updated in real time and stored in the server's database. In addition, the sentiment engine obtains user sentiment information from data sources such as social media and user feedback, and analyzes its content. 【0441】 The server then uses the event end time, probability of precipitation, and sentiment data analyzed by the sentiment engine to forecast demand. The sentiment engine identifies emotions such as joy, dissatisfaction, and anxiety from, for example, users' social media posts and reviews, and evaluates their impact on ride demand. This allows the analysis system to perform more accurate demand forecasts. 【0442】 Next, the server determines the optimal transportation arrangement based on the analysis results. In this process, a dispatch strategy tailored to the user's emotional state is considered. For example, if many event participants are deeply moved, a surge in demand is expected immediately after the event ends, so taxis are concentrated during that time. 【0443】 Ultimately, the terminal receives the location information and notifies the taxi driver, who is the user. This notification includes detailed dispatch instructions based on predicted demand, as well as advice to help the driver efficiently meet customer needs. For example, it might include information such as, "Demand is expected to be high near the event venue exit because many users are satisfied with the event." 【0444】 This system enables transportation managers to make quick and appropriate decisions based on customer sentiment and event circumstances, resulting in improved customer satisfaction and optimized operational efficiency. 【0445】 The following describes the processing flow. 【0446】 Step 1: 【0447】 The server connects to an event information API and a weather forecast API to retrieve the latest event and weather information. The retrieved data is converted to JSON format and stored in the database. 【0448】 Step 2: 【0449】 The server uses an emotion engine to retrieve user posts from social media and user review sites. Then, it uses natural language processing technology to analyze the user's emotions from the posts and classify them into categories such as positive, negative, and neutral. 【0450】 Step 3: 【0451】 The server integrates event information, weather information, and sentiment data to perform demand forecasting. It calculates the expected level of ride demand as the event end time approaches or when bad weather is predicted, referencing ride data from similar past situations. Sentiment data has a particularly significant impact; if users are satisfied with the event, the predicted demand is expected to increase further. 【0452】 Step 4: 【0453】 The server determines the optimal allocation of transportation based on predicted demand. This includes planning to concentrate vehicles in high-demand areas during specific demand times. For example, if there is a high level of negative emotion, dispatching vehicles earlier than usual may be considered. 【0454】 Step 5: 【0455】 The terminal receives the dispatch plan provided by the server and sends a push notification to the taxi driver, who is the user. The notification provides specific information such as recommended waiting locations and times when increased demand is expected. For example, it may include a message such as, "We expect an increase in passengers around the event venue between 6 PM and 8 PM." 【0456】 In this way, the entire system works together to aim for efficient dispatching and high customer satisfaction. 【0457】 (Example 2) 【0458】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0459】 Current transportation management systems struggle to accurately reflect demand fluctuations based on user emotions, in addition to demand forecasts based on events and weather conditions. This can lead to delays and inaccurate responses to customer needs. Furthermore, efficiently managing the allocation of resources such as taxis and buses is difficult. Therefore, a new method is needed to maximize operational efficiency while improving customer satisfaction. 【0460】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0461】 In this invention, the server includes data acquisition means for acquiring event data and weather data, sentiment analysis means for collecting and analyzing sentiment information, and estimation means for performing supply and demand forecasting. This makes it possible to forecast demand that takes user sentiment into account and to arrange the optimal means of transportation based on that forecast. 【0462】 "Data acquisition means" refers to a device or configuration that provides the function of automatically collecting event data and weather data from external sources. 【0463】 "Emotional analysis means" refers to a device or configuration that collects user emotional information from social media and feedback data and provides a function for analyzing this information. 【0464】 A "predictive means" is a device or configuration that provides the functionality to predict demand at a specific time and place based on acquired event data, weather data, and sentiment information. 【0465】 A "distribution strategy determination means" is a device or configuration that provides the function of determining the optimal distribution of transportation means based on demand forecasts and formulating that strategy. 【0466】 "Information transmission means" refers to a device or configuration that provides a function for notifying a user or device of optimized resource allocation information and for transmitting necessary instructions. 【0467】 This invention is a system that integrates data acquisition, sentiment analysis, demand forecasting, resource allocation optimization, and information transmission related to these processes. The specific configuration for implementing this system is described below. 【0468】 The server operates on cloud infrastructure. Event information is retrieved via the event management platform's API, and weather information is collected via the weather information service's API. Specific examples include the Eventbrite API and the OpenWeatherMap API. This data is updated in real time and stored in a database on the server. 【0469】 Next, the server uses sentiment analysis tools to obtain sentiment information from social media and user feedback. This involves using SNS APIs and natural language processing services to analyze the content of posts and generate sentiment scores. Specifically, the Twitter API and Google Cloud Natural Language API can be used. 【0470】 The server performs demand forecasting based on acquired event information, weather information, and sentiment information. This demand forecasting is performed using statistical analysis methods and machine learning algorithms. Based on the forecast results, a resource allocation strategy determination mechanism is activated to determine the optimal resource allocation. For example, a strategy is formulated to identify times and locations where demand is high and to concentrate transportation resources on those locations. 【0471】 The terminal is a mobile device for drivers that receives resource allocation information sent from the server and notifies the drivers. This notification includes allocation instructions based on demand forecasts, supporting drivers in responding appropriately. A specific example of a prompt message would be: "To determine the optimal taxi allocation after the event, please perform a demand forecast that takes into account user sentiment information and weather forecasts, and then present an allocation strategy based on the results." 【0472】 In this way, the entire system operates efficiently, enabling rapid and effective optimization of transportation resources in response to demand. 【0473】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0474】 Step 1: 【0475】 The server retrieves event and weather data using APIs. The input data consists of API responses from the event management platform and weather information service. The server stores this data in a database in real time, preparing it for the next stage of processing. Specifically, it sends HTTP requests, parses the retrieved JSON data, and saves it to the database. 【0476】 Step 2: 【0477】 The server collects user sentiment information from social media using sentiment analysis tools. The input is posted data obtained via social media APIs. The server analyzes these posts using natural language processing techniques and calculates sentiment scores. The output is the analyzed sentiment score and category (e.g., happy, unhappy). Specifically, it inputs text data into a sentiment analysis model and calculates a score indicating whether the content is positive or negative. 【0478】 Step 3: 【0479】 The server performs demand forecasting based on acquired event data, weather data, and sentiment information. The input is all data collected to date. The server applies machine learning algorithms to build demand forecasting models for each time and location. The output is a list of predicted demand by time and region. Specifically, the process involves inputting data into the algorithm and generating a forecasting model. 【0480】 Step 4: 【0481】 The server determines the optimal resource allocation based on demand forecasts. The input is newly forecasted demand data. The server identifies areas and time periods where demand is expected to be high and determines an allocation strategy accordingly. The output is resource allocation decision information. Specifically, it calculates the optimal resource allocation based on an allocation algorithm and sets the instructions for that allocation. 【0482】 Step 5: 【0483】 The terminal notifies the taxi driver of resource placement information received from the server. The input is resource placement instructions from the server. The terminal pushes a notification to the driver's device, instructing them to be at the appropriate location and time. The output is a dispatch instruction message to the taxi driver. Specifically, it sends a notification to the driver via a mobile application and displays the instructions on the screen. 【0484】 (Application Example 2) 【0485】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0486】 Modern large-scale events and temporary increases in urban traffic demand are difficult to address effectively using conventional methods. In this context, there is a need to ensure smooth traffic flow by forecasting demand based on people's emotions, in addition to weather and event information, in order to provide optimal transportation allocation and effective advice to users. 【0487】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0488】 In this invention, the server includes information acquisition means for obtaining event information, information acquisition means for obtaining weather information, and sentiment acquisition means for collecting user sentiment data. This enables highly accurate demand forecasting and optimal transportation arrangement based on the collected data. 【0489】 "Event information" refers to detailed information about a specific event or gathering, such as the date, time, location, and expected number of participants. 【0490】 "Weather information" refers to information about current and future weather conditions in a specific region. 【0491】 "User sentiment data" refers to data that indicates the emotional state of individual users, obtained through social media and feedback. 【0492】 "Demand forecasting" is the process of predicting the need for a service or product at a specific time and place, based on collected data. 【0493】 "The arrangement of means of transport" refers to the efficient allocation of transportation resources needed at a specific location and time. 【0494】 "Notification means" refers to a method or device for communicating information acquired by the system or analysis results to the user. 【0495】 In one embodiment of this invention, the server utilizes an information acquisition means to obtain event information and weather information. This means uses an API provided by a cloud service provider to collect real-time data. Furthermore, as an emotion acquisition means to collect user emotion data, it collects data from social media platforms and evaluates the emotional state using an emotion analysis API. 【0496】 The server uses the acquired information to perform data processing for demand forecasting. Here, it processes large amounts of data using AWS cloud infrastructure and executes scripts written in programming languages ​​such as Python. The demand forecasting algorithm analyzes historical data and current sentiment data to calculate the optimal allocation of transportation methods. 【0497】 The device provides users with notifications based on optimized location information sent from the server. These notifications arrive as push notifications on the user's smartphone and include suggestions for specific modes of transportation and routes home. 【0498】 This system allows users to receive optimized real-time traffic information, helping them to travel smoothly. For example, if the system detects that many visitors are satisfied at the end of a music festival, it can plan the timely placement of shuttle buses based on predicted demand and send users a notification such as, "The bus at the south exit is crowded, so we recommend taking the bus arriving in 5 minutes." 【0499】 An example of a prompt for a generative AI model is: "If participants in this event show signs of excitement, what approaches would be effective in improving the efficiency of their use of public transportation after the event?" 【0500】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0501】 Step 1: 【0502】 The server retrieves event and weather information through various APIs. This includes information about the date, time, location, and scale of events, as well as current and predicted weather conditions. Input is raw data from the APIs, and output is structured data stored in a database. This data is used for subsequent predictive analysis. 【0503】 Step 2: 【0504】 The server collects user sentiment data from social media. This process filters for specific keywords and hashtags and analyzes the emotional state of users from their text posts. The input is text information from social media data, and the output is numerical data from sentiment analysis (e.g., sentiment scores such as excited, satisfied, or dissatisfied). 【0505】 Step 3: 【0506】 The server uses collected event information, weather information, and sentiment data to forecast demand. This forecast utilizes machine learning algorithms to model fluctuations in transportation demand. The input is the data set obtained in the previous step, and the output is the predicted demand as numerical and time-series data. Based on this forecast, the allocation of transportation methods is planned. 【0507】 Step 4: 【0508】 The server calculates the optimal allocation of transportation methods based on the demand forecast results. An algorithm is used to efficiently allocate accessible transportation resources. The input is the demand forecast result, and the output is the optimized transportation allocation plan. 【0509】 Step 5: 【0510】 The terminal receives optimal placement information sent from the server and notifies the user. This notification includes detailed information about the optimal travel route and recommended modes of transport. The input is placement information from the server, and the output is a visual or auditory notification from the user. 【0511】 Step 6: 【0512】 The user receives notifications from their device and takes action to select the most suitable mode of transportation. This enables the user to travel efficiently based on real-time information. The input is the notification from the device, and the output is the user's action choice. 【0513】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0514】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0515】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0516】 [Fourth Embodiment] 【0517】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0518】 As shown in Figure 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. 【0519】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0520】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0521】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0522】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0523】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0524】 The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0525】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0526】 The specific processing program 56 is an example of a "program" relating to the technology of this 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 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. 【0527】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0528】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0529】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0530】 This invention is implemented as a system comprising four elements: an information acquisition means, an analysis means, a placement determination means, and a notification means. 【0531】 First, the server collects event and weather information via the internet through websites and APIs. Event information includes data such as the event name, date and time, venue, and expected number of attendees. Weather information includes meteorological data such as temperature, probability of precipitation, and wind speed. By collecting this information, the server can obtain the latest event and weather information in real time. 【0532】 Next, the server analyzes the collected data using machine learning and statistical models. Specifically, the server utilizes passenger demand data related to past events and weather patterns to predict future demand fluctuations. For example, if a concert by a popular artist is scheduled or if a sudden weather change is expected, the server will take these factors into account when analyzing demand. 【0533】 Subsequently, based on the predicted demand, the server utilizes a placement determination mechanism to determine the optimal allocation of transportation methods (such as taxis and vehicles). This proposes an efficient dispatch route to meet the demand at a specific time and location. For example, at the end of a large-scale event, the server might instruct a placement that concentrates a large number of taxis around the venue. 【0534】 Finally, the terminal receives this location information. The terminal notifies the taxi driver, who is the user, of the information in an intuitively understandable format. Specifically, a push notification such as "Demand will increase around XX Hall at 9 PM" is sent via a smartphone or other device. In this way, the driver can efficiently position themselves to pick up passengers. 【0535】 This series of processes allows taxi companies to avoid unnecessary travel while quickly responding to customer demand, ultimately leading to improved profitability. 【0536】 The following describes the processing flow. 【0537】 Step 1: 【0538】 The server sets up scheduled tasks to send requests to event information APIs and weather forecast APIs to retrieve the latest event and weather information. It authenticates using API keys, receives the data in JSON format, and stores it in an internal database. 【0539】 Step 2: 【0540】 The server performs the necessary preprocessing to analyze the acquired event and weather information. Specifically, it filters the event information for the target area and extracts important attributes such as start and end times and the number of participants. For weather information, it identifies data such as the probability of precipitation and temperature. 【0541】 Step 3: 【0542】 The server performs demand forecasting using machine learning models and statistical methods based on pre-processed data. It analyzes predicted demand fluctuations by referencing occupancy rate data from similar past events. This analysis identifies high-demand areas at specific times and locations. 【0543】 Step 4: 【0544】 The server uses the analysis results to run a deployment logic and generate an optimal transportation deployment plan. This includes setting up dispatch routes and waiting points to efficiently meet the predicted demand within a specified time. 【0545】 Step 5: 【0546】 The terminal receives location information transmitted from the server and notifies the taxi driver, who is the user. Push notifications are displayed on a smartphone app or in-vehicle terminal, providing important information through an intuitive interface. The notifications indicate dispatch demand for specific times and locations, enabling drivers to respond quickly. 【0547】 (Example 1) 【0548】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0549】 Efficient transportation deployment needs to be sensitive to demand changes caused by events and weather fluctuations. However, conventional systems have difficulty with real-time demand forecasting and flexible deployment decisions, sometimes leading to excessive waiting times and increased costs. Furthermore, there was a challenge in that specific measures for maximizing the operational efficiency of transportation were limited and lacked flexibility. 【0550】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0551】 In this invention, the server includes an information acquisition device for acquiring event information, an information acquisition device for acquiring weather information, a data analysis device for analyzing the acquired event information and weather information and making demand forecasts, a placement determination device for optimizing the placement of transportation means based on the demand forecast, a notification device for presenting optimized placement information, an analysis device for predicting future demand fluctuations using machine learning models and statistical models, and an optimization device for calculating the optimal placement of transportation means based on past data. This enables real-time demand forecasting and efficient placement determination. 【0552】 An "information acquisition device" refers to hardware or software used to collect data from external sources, and has the function of acquiring specific event information or weather information. 【0553】 A "data analysis device" refers to hardware or software that has the function of analyzing acquired data and applying statistical models or machine learning algorithms to forecast demand. 【0554】 A "distribution determination device" refers to hardware or software used to calculate the optimal distribution of transportation methods based on analyzed demand forecast data. 【0555】 A "notification device" refers to hardware or software that displays or transmits transportation arrangement information calculated by a server to a user. 【0556】 A "machine learning model" is a set of algorithms used to learn data patterns and make predictions about future data, enabling highly accurate analysis. 【0557】 A "statistical model" is an algorithm that incorporates mathematical methods used to analyze data and extract useful information from it. 【0558】 An "optimization device" is hardware or software used to perform calculations to determine the optimal placement of components to obtain the best results under specific conditions. 【0559】 This invention is a system that enables the efficient arrangement of transportation means through the cooperation of a server, terminal, and user. First, the server uses an information acquisition device to collect event information and weather information from publicly available data sources on the internet. In this process, it utilizes scripting languages ​​such as Python and JavaScript to obtain the necessary data through APIs and web scraping. The collected data is stored in a database in real time. 【0560】 Next, the server uses a data analysis device to analyze the acquired event and weather information. This analysis utilizes machine learning models and statistical models to learn past data patterns and predict future demand fluctuations. Specific technologies used include the Python scikit-learn library and TensorFlow. 【0561】 Based on the analysis results, the server uses a placement decision device to calculate the optimal placement of transportation methods. Here, optimization libraries such as Google OR-Tools are used to simulate efficient vehicle dispatch routes. 【0562】 Subsequently, the terminal receives optimized placement information from the server via a notification device and displays it to the user. Specifically, Firebase Cloud Messaging (FCM) is used to send the information as a push notification to the smartphone. An example message might be, "Demand will increase around XX Hall at 9 PM." This notification allows users to operate their transportation more efficiently. 【0563】 A concrete example is a scenario where a terminal notifies the server of an expected increase in demand at the end of an event, and the driver, as the user, uses this information to determine the optimal location. In this way, real-time demand forecasting and placement optimization are achieved. An example of a prompt message would be, "Tell me how to determine the optimal taxi placement at the end of a large-scale event." 【0564】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0565】 Step 1: 【0566】 The server uses an information acquisition device to collect event and weather information from the internet. It uses API endpoints and web page URLs as input. The server's specific actions involve using a Python script to send API requests and parsing the returned JSON data. The output includes information such as event name, date and time, venue, expected number of attendees, temperature, probability of precipitation, and wind speed, which are then stored in a database. 【0567】 Step 2: 【0568】 The server uses a data analysis device to analyze collected event and weather information. The input consists of event and weather information stored in a database. The server uses a machine learning model to forecast demand. Specifically, it performs regression analysis using the scikit-learn library to predict demand fluctuations. The output is numerical data indicating increases or decreases in demand, which is used to determine the next deployment. 【0569】 Step 3: 【0570】 The server uses a placement determination device to determine the optimal placement of transportation methods based on predicted demand. The input is the demand forecast data obtained in step 2. The specific operation involves solving an optimization problem using tools such as Google OR-Tools to calculate the appropriate vehicle dispatch route. The output is the placement and route information of transportation methods at a specific time and location. 【0571】 Step 4: 【0572】 The terminal receives optimized placement information from the server via a notification device and notifies the user. The input is the placement information calculated in step 3. The specific action the terminal takes is to send a push notification to a smartphone or device using Firebase Cloud Messaging. The output will display something like, "Demand will be high around XX Hall at 9 PM." 【0573】 Step 5: 【0574】 The user operates the transportation based on notifications. The input is the notification information received from the terminal. The user's specific action is to refer to the notification, position the transportation at a specific location, and efficiently pick up passengers. This allows the user to select the optimal route. 【0575】 (Application Example 1) 【0576】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0577】 Conventional transportation scheduling systems struggle to respond flexibly to real-time demand fluctuations, sometimes resulting in wasted travel and missed opportunities. To solve this problem, more accurate demand forecasting and a mechanism for dynamically adjusting routes are necessary. 【0578】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0579】 In this invention, the server includes information acquisition means for acquiring event information, information acquisition means for acquiring weather information, analysis means for analyzing the acquired event information and weather information and making demand forecasts, and means for self-adjusting travel routes based on demand forecasts. This enables the efficient deployment of travel means in real time according to demand. 【0580】 "Event information" refers to information related to an event, including data on the specific date and time, location, and expected number of participants. 【0581】 "Weather information" refers to data on meteorological conditions such as temperature, probability of precipitation, and wind speed. 【0582】 "Information acquisition means" refers to means that have the function of collecting event information and weather information via the internet. 【0583】 "Analysis means" refers to means that have the function of processing data to predict fluctuations in demand based on collected event information and weather information. 【0584】 A "determination of allocation means" is a means that has the function of determining the optimal allocation of means of transport based on predicted demand. 【0585】 A "notification mechanism" is a means that has the function of notifying relevant users of optimized placement information. 【0586】 "Means for self-adjusting travel routes based on real-time demand forecasts" refers to means that have the function of dynamically adjusting the travel route of a means of transportation in response to fluctuations in demand. 【0587】 This invention embodies a system that forecasts demand based on event information and weather information, and dynamically adjusts the routes of means of transportation. This system mainly consists of a server, terminals, and users who utilize them. 【0588】 The server collects event and weather information via the internet. Event information includes the event name, date and time, location, and expected number of attendees, while weather information includes temperature, probability of precipitation, and wind speed. This information is continuously updated via an API and retrieved as real-time data. 【0589】 The collected information is analyzed on the server. For example, machine learning algorithms are implemented using Python or the scikit-learn library. Based on demand data related to past events and weather patterns, fluctuations in demand are predicted. This analysis forms the basis for efficiently adjusting the supply and demand of transportation in specific locations and time periods. 【0590】 Based on predicted demand, the server determines the optimal travel route. This allows the mode of transport to dynamically adjust its route to areas with high demand, efficiently meeting user needs. At this time, the travel route is dynamically adjusted based on real-time fluctuating demand information, reducing wasted travel and waiting times. 【0591】 Optimized information is sent from the server to the device. The device is a smartphone or tablet, and the user can receive the notification through an easily understandable interface. For example, an intuitive message such as "Demand will increase in the city center at 9 PM" is sent as a push notification. 【0592】 As a concrete example, consider a scenario where a large-scale concert is being held in the city, a sudden rain shower is expected, and transportation demand increases. In this case, the server predicts the demand for transportation based on this information and notifies the user. As a result, the user can arrange for passengers to wait at appropriate locations. 【0593】 An example of a prompt message is, "The predicted probability of rain at 7 PM today is 80%. How should transportation be arranged?" This demonstrates a system that efficiently utilizes transportation options using a generative AI model. 【0594】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0595】 Step 1: 【0596】 The server collects event and weather information from APIs via the internet. Specifically, the server sends requests to the event service API and weather service API and retrieves data in JSON format. This input data includes event name, date and time, probability of precipitation, etc., and this information is stored in an internal database as output. 【0597】 Step 2: 【0598】 The server uses the acquired data to perform demand forecasting. Specifically, it employs a machine learning model built on the server based on past events and weather data. The generative AI model takes the acquired event information and weather information as input and outputs forecast data for travel demand at a specific time and location. For example, if a popular concert and a high probability of rain are input, demand is predicted to increase. 【0599】 Step 3: 【0600】 The server determines the optimal allocation of transportation methods based on demand forecasts. Specifically, an algorithm within the server takes the forecasted demand data as input, performs optimization calculations, and outputs information on the optimal allocation of each transportation method. This makes it possible to suggest travel routes to specific locations. 【0601】 Step 4: 【0602】 The server notifies the terminal of optimized placement information. Specifically, the server uses a protocol to send push notifications to the terminal. It uses placement decision information as input and displays a notification message such as "Demand will increase in the city center at 9 PM" on the terminal screen as output. 【0603】 Step 5: 【0604】 Based on notifications from their devices, users take appropriate action to arrange their mode of transportation. Specifically, users check the notification content displayed on their devices and move their mode of transportation to the high-demand location indicated in the notification. This enables efficient use of transportation. 【0605】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0606】 This invention is a system that combines information acquisition means, analysis means, placement determination means, and notification means with an emotion engine that recognizes the user's emotions. 【0607】 First, the server collects data from the event information API and weather forecast API as before, obtaining event and weather information. This data is updated in real time and stored in the server's database. In addition, the sentiment engine obtains user sentiment information from data sources such as social media and user feedback, and analyzes its content. 【0608】 The server then uses the event end time, probability of precipitation, and sentiment data analyzed by the sentiment engine to forecast demand. The sentiment engine identifies emotions such as joy, dissatisfaction, and anxiety from, for example, users' social media posts and reviews, and evaluates their impact on ride demand. This allows the analysis system to perform more accurate demand forecasts. 【0609】 Next, the server determines the optimal transportation arrangement based on the analysis results. In this process, a dispatch strategy tailored to the user's emotional state is considered. For example, if many event participants are deeply moved, a surge in demand is expected immediately after the event ends, so taxis are concentrated during that time. 【0610】 Ultimately, the terminal receives the location information and notifies the taxi driver, who is the user. This notification includes detailed dispatch instructions based on predicted demand, as well as advice to help the driver efficiently meet customer needs. For example, it might include information such as, "Demand is expected to be high near the event venue exit because many users are satisfied with the event." 【0611】 This system enables transportation managers to make quick and appropriate decisions based on customer sentiment and event circumstances, resulting in improved customer satisfaction and optimized operational efficiency. 【0612】 The following describes the processing flow. 【0613】 Step 1: 【0614】 The server connects to an event information API and a weather forecast API to retrieve the latest event and weather information. The retrieved data is converted to JSON format and stored in the database. 【0615】 Step 2: 【0616】 The server uses an emotion engine to retrieve user posts from social media and user review sites. Then, it uses natural language processing technology to analyze the user's emotions from the posts and classify them into categories such as positive, negative, and neutral. 【0617】 Step 3: 【0618】 The server integrates event information, weather information, and sentiment data to perform demand forecasting. It calculates the expected level of ride demand as the event end time approaches or when bad weather is predicted, referencing ride data from similar past situations. Sentiment data has a particularly significant impact; if users are satisfied with the event, the predicted demand is expected to increase further. 【0619】 Step 4: 【0620】 The server determines the optimal allocation of transportation based on predicted demand. This includes planning to concentrate vehicles in high-demand areas during specific demand times. For example, if there is a high level of negative emotion, dispatching vehicles earlier than usual may be considered. 【0621】 Step 5: 【0622】 The terminal receives the dispatch plan provided by the server and sends a push notification to the taxi driver, who is the user. The notification provides specific information such as recommended waiting locations and times when increased demand is expected. For example, it may include a message such as, "We expect an increase in passengers around the event venue between 6 PM and 8 PM." 【0623】 In this way, the entire system works together to aim for efficient dispatching and high customer satisfaction. 【0624】 (Example 2) 【0625】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0626】 Current transportation management systems struggle to accurately reflect demand fluctuations based on user emotions, in addition to demand forecasts based on events and weather conditions. This can lead to delays and inaccurate responses to customer needs. Furthermore, efficiently managing the allocation of resources such as taxis and buses is difficult. Therefore, a new method is needed to maximize operational efficiency while improving customer satisfaction. 【0627】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0628】 In this invention, the server includes data acquisition means for acquiring event data and weather data, sentiment analysis means for collecting and analyzing sentiment information, and estimation means for performing supply and demand forecasting. This makes it possible to forecast demand that takes user sentiment into account and to arrange the optimal means of transportation based on that forecast. 【0629】 "Data acquisition means" refers to a device or configuration that provides the function of automatically collecting event data and weather data from external sources. 【0630】 "Emotional analysis means" refers to a device or configuration that collects user emotional information from social media and feedback data and provides a function for analyzing this information. 【0631】 A "predictive means" is a device or configuration that provides the functionality to predict demand at a specific time and place based on acquired event data, weather data, and sentiment information. 【0632】 A "distribution strategy determination means" is a device or configuration that provides the function of determining the optimal distribution of transportation means based on demand forecasts and formulating that strategy. 【0633】 "Information transmission means" refers to a device or configuration that provides a function for notifying a user or device of optimized resource allocation information and for transmitting necessary instructions. 【0634】 This invention is a system that integrates data acquisition, sentiment analysis, demand forecasting, resource allocation optimization, and information transmission related to these processes. The specific configuration for implementing this system is described below. 【0635】 The server operates on cloud infrastructure. Event information is retrieved via the event management platform's API, and weather information is collected via the weather information service's API. Specific examples include the Eventbrite API and the OpenWeatherMap API. This data is updated in real time and stored in a database on the server. 【0636】 Next, the server uses sentiment analysis tools to obtain sentiment information from social media and user feedback. This involves using SNS APIs and natural language processing services to analyze the content of posts and generate sentiment scores. Specifically, the Twitter API and Google Cloud Natural Language API can be used. 【0637】 The server performs demand forecasting based on acquired event information, weather information, and sentiment information. This demand forecasting is performed using statistical analysis methods and machine learning algorithms. Based on the forecast results, a resource allocation strategy determination mechanism is activated to determine the optimal resource allocation. For example, a strategy is formulated to identify times and locations where demand is high and to concentrate transportation resources on those locations. 【0638】 The terminal is a mobile device for drivers that receives resource allocation information sent from the server and notifies the drivers. This notification includes allocation instructions based on demand forecasts, supporting drivers in responding appropriately. A specific example of a prompt message would be: "To determine the optimal taxi allocation after the event, please perform a demand forecast that takes into account user sentiment information and weather forecasts, and then present an allocation strategy based on the results." 【0639】 In this way, the entire system operates efficiently, enabling rapid and effective optimization of transportation resources in response to demand. 【0640】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0641】 Step 1: 【0642】 The server retrieves event and weather data using APIs. The input data consists of API responses from the event management platform and weather information service. The server stores this data in a database in real time, preparing it for the next stage of processing. Specifically, it sends HTTP requests, parses the retrieved JSON data, and saves it to the database. 【0643】 Step 2: 【0644】 The server collects user sentiment information from social media using sentiment analysis tools. The input is posted data obtained via social media APIs. The server analyzes these posts using natural language processing techniques and calculates sentiment scores. The output is the analyzed sentiment score and category (e.g., happy, unhappy). Specifically, it inputs text data into a sentiment analysis model and calculates a score indicating whether the content is positive or negative. 【0645】 Step 3: 【0646】 The server performs demand forecasting based on acquired event data, weather data, and sentiment information. The input is all data collected to date. The server applies machine learning algorithms to build demand forecasting models for each time and location. The output is a list of predicted demand by time and region. Specifically, the process involves inputting data into the algorithm and generating a forecasting model. 【0647】 Step 4: 【0648】 The server determines the optimal resource allocation based on demand forecasts. The input is newly forecasted demand data. The server identifies areas and time periods where demand is expected to be high and determines an allocation strategy accordingly. The output is resource allocation decision information. Specifically, it calculates the optimal resource allocation based on an allocation algorithm and sets the instructions for that allocation. 【0649】 Step 5: 【0650】 The terminal notifies the taxi driver of resource placement information received from the server. The input is resource placement instructions from the server. The terminal pushes a notification to the driver's device, instructing them to be at the appropriate location and time. The output is a dispatch instruction message to the taxi driver. Specifically, it sends a notification to the driver via a mobile application and displays the instructions on the screen. 【0651】 (Application Example 2) 【0652】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0653】 Modern large-scale events and temporary increases in urban traffic demand are difficult to address effectively using conventional methods. In this context, there is a need to ensure smooth traffic flow by forecasting demand based on people's emotions, in addition to weather and event information, in order to provide optimal transportation allocation and effective advice to users. 【0654】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0655】 In this invention, the server includes information acquisition means for obtaining event information, information acquisition means for obtaining weather information, and sentiment acquisition means for collecting user sentiment data. This enables highly accurate demand forecasting and optimal transportation arrangement based on the collected data. 【0656】 "Event information" refers to detailed information about a specific event or gathering, such as the date, time, location, and expected number of participants. 【0657】 "Weather information" refers to information about current and future weather conditions in a specific region. 【0658】 "User sentiment data" refers to data that indicates the emotional state of individual users, obtained through social media and feedback. 【0659】 "Demand forecasting" is the process of predicting the need for a service or product at a specific time and place, based on collected data. 【0660】 "The arrangement of means of transport" refers to the efficient allocation of transportation resources needed at a specific location and time. 【0661】 "Notification means" refers to a method or device for communicating information acquired by the system or analysis results to the user. 【0662】 In one embodiment of this invention, the server utilizes an information acquisition means to obtain event information and weather information. This means uses an API provided by a cloud service provider to collect real-time data. Furthermore, as an emotion acquisition means to collect user emotion data, it collects data from social media platforms and evaluates the emotional state using an emotion analysis API. 【0663】 The server uses the acquired information to perform data processing for demand forecasting. Here, it processes large amounts of data using AWS cloud infrastructure and executes scripts written in programming languages ​​such as Python. The demand forecasting algorithm analyzes historical data and current sentiment data to calculate the optimal allocation of transportation methods. 【0664】 The device provides users with notifications based on optimized location information sent from the server. These notifications arrive as push notifications on the user's smartphone and include suggestions for specific modes of transportation and routes home. 【0665】 This system allows users to receive optimized real-time traffic information, helping them to travel smoothly. For example, if the system detects that many visitors are satisfied at the end of a music festival, it can plan the timely placement of shuttle buses based on predicted demand and send users a notification such as, "The bus at the south exit is crowded, so we recommend taking the bus arriving in 5 minutes." 【0666】 An example of a prompt for a generative AI model is: "If participants in this event show signs of excitement, what approaches would be effective in improving the efficiency of their use of public transportation after the event?" 【0667】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0668】 Step 1: 【0669】 The server retrieves event and weather information through various APIs. This includes information about the date, time, location, and scale of events, as well as current and predicted weather conditions. Input is raw data from the APIs, and output is structured data stored in a database. This data is used for subsequent predictive analysis. 【0670】 Step 2: 【0671】 The server collects user sentiment data from social media. This process filters for specific keywords and hashtags and analyzes the emotional state of users from their text posts. The input is text information from social media data, and the output is numerical data from sentiment analysis (e.g., sentiment scores such as excited, satisfied, or dissatisfied). 【0672】 Step 3: 【0673】 The server uses collected event information, weather information, and sentiment data to forecast demand. This forecast utilizes machine learning algorithms to model fluctuations in transportation demand. The input is the data set obtained in the previous step, and the output is the predicted demand as numerical and time-series data. Based on this forecast, the allocation of transportation methods is planned. 【0674】 Step 4: 【0675】 The server calculates the optimal allocation of transportation methods based on the demand forecast results. An algorithm is used to efficiently allocate accessible transportation resources. The input is the demand forecast result, and the output is the optimized transportation allocation plan. 【0676】 Step 5: 【0677】 The terminal receives optimal placement information sent from the server and notifies the user. This notification includes detailed information about the optimal travel route and recommended modes of transport. The input is placement information from the server, and the output is a visual or auditory notification from the user. 【0678】 Step 6: 【0679】 The user receives notifications from their device and takes action to select the most suitable mode of transportation. This enables the user to travel efficiently based on real-time information. The input is the notification from the device, and the output is the user's action choice. 【0680】 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0681】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0682】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0683】 Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0684】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0685】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0686】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0687】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0688】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0689】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0690】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0691】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0692】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0693】 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. 【0694】 Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0695】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0696】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0697】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0698】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0699】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0700】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0701】 The following is further disclosed regarding the embodiments described above. 【0702】 (Claim 1) 【0703】 A means of obtaining information to acquire event information, 【0704】 A means of obtaining information for acquiring weather information, 【0705】 An analytical means that analyzes acquired event information and weather information to perform demand forecasting, 【0706】 A means for determining the arrangement of transportation means to optimize the arrangement of transportation means based on demand forecasts, 【0707】 A notification means for notifying optimized placement information, 【0708】 A system that includes this. 【0709】 (Claim 2) 【0710】 The system according to claim 1, further comprising analytical means for identifying the end time of an event and performing demand forecasting based thereon. 【0711】 (Claim 3) 【0712】 The system according to claim 1, further comprising analytical means for analyzing the probability of precipitation and predicting the demand for transportation means during adverse weather conditions. 【0713】 "Example 1" 【0714】 (Claim 1) 【0715】 An information acquisition device for obtaining event information, 【0716】 An information acquisition device for obtaining weather information, 【0717】 A data analysis device for analyzing acquired event information and weather information to perform demand forecasting, 【0718】 A device for determining the arrangement of transportation means based on demand forecasts, 【0719】 A notification device for presenting optimized placement information, 【0720】 An analytical device for predicting future demand fluctuations using machine learning models and statistical models, 【0721】 An optimization device for calculating the optimal arrangement of transportation methods based on past data, 【0722】 A system that includes this. 【0723】 (Claim 2) 【0724】 The system according to claim 1, further comprising a data analysis device that identifies the end time of an event and performs demand forecasting based on that. 【0725】 (Claim 3) 【0726】 The system according to claim 1, further comprising a data analysis device for analyzing the probability of precipitation and predicting the demand for transportation during adverse weather conditions. 【0727】 "Application Example 1" 【0728】 (Claim 1) 【0729】 A means of obtaining information to acquire event information, 【0730】 A means of obtaining information for acquiring weather information, 【0731】 An analytical means that analyzes acquired event information and weather information to perform demand forecasting, 【0732】 A means for determining the arrangement of means of transport that optimizes the arrangement of means of transport based on demand forecasts, 【0733】 A notification means for notifying optimized placement information, 【0734】 A means of self-adjusting travel routes based on real-time demand forecasts, 【0735】 A system that includes this. 【0736】 (Claim 2) 【0737】 The system according to claim 1, further comprising analytical means for identifying the end time of an event and performing demand forecasting based thereon. 【0738】 (Claim 3) 【0739】 The system according to claim 1, further comprising analytical means for analyzing the probability of precipitation and predicting the demand for transportation during inclement weather. 【0740】 "Example 2 of combining an emotion engine" 【0741】 (Claim 1) 【0742】 A data acquisition means for obtaining event data and weather data, 【0743】 A means of emotional analysis for collecting and analyzing emotional information, 【0744】 An estimation method for predicting demand based on acquired event data, weather data, and sentiment information, 【0745】 A means for determining a resource allocation strategy to optimize resource allocation based on the prediction results, 【0746】 Information transmission means for transmitting optimized resource allocation information, 【0747】 A system that includes this. 【0748】 (Claim 2) 【0749】 The system according to claim 1, further comprising means for adjusting a staffing strategy based on emotional information obtained through an emotional analysis means. 【0750】 (Claim 3) 【0751】 The system according to claim 1, further comprising means for analyzing demand trends based on time of day and location information using acquired sentiment information. 【0752】 "Application example 2 when combining with an emotional engine" 【0753】 (Claim 1) 【0754】 A means of obtaining information to acquire event information, 【0755】 A means of obtaining information for acquiring weather information, 【0756】 A means of acquiring emotions to collect user emotion data, 【0757】 An analytical means that analyzes acquired event information, weather information, and sentiment data to perform demand forecasting, 【0758】 A means for determining the arrangement of transportation means to optimize the arrangement of transportation means based on demand forecasts, 【0759】 A notification means for notifying optimized placement information, 【0760】 A system that includes this. 【0761】 (Claim 2) 【0762】 The system according to claim 1, further comprising a notification means for identifying the event end time and suggesting an efficient route home to the user based on the analysis results. 【0763】 (Claim 3) 【0764】 The system according to claim 1, further comprising analytical means for predicting fluctuations in transportation demand based on user sentiment data and suggesting appropriate means of transport. [Explanation of Symbols] 【0765】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of obtaining information to acquire event information, A means of obtaining information for acquiring weather information, An analytical means that analyzes acquired event information and weather information to perform demand forecasting, A means for determining the arrangement of transportation means to optimize the arrangement of transportation means based on demand forecasts, A notification means for notifying optimized placement information, A system that includes this. [Claim 2] The system according to claim 1, further comprising analytical means for identifying the end time of an event and performing demand forecasting based on that. [Claim 3] The system according to claim 1, further comprising analytical means for analyzing the probability of precipitation and predicting the demand for transportation means during adverse weather conditions.

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