system

Abstract

The system according to this embodiment aims to predict future congestion based on real-time congestion levels and provide users with appropriate information. [Solution] The system according to the embodiment comprises an acquisition unit, a prediction unit, an integration unit, and a provision unit. The acquisition unit acquires real-time congestion status. The prediction unit makes future congestion predictions based on the information acquired by the acquisition unit. The integration unit incorporates user search information based on the information predicted by the prediction unit. The provision unit provides prediction results based on the information incorporated by the integration unit.

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. [[ID=第十三条]]

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, future congestion prediction based on real-time congestion situations has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform future congestion prediction based on real-time congestion situations and provide appropriate information to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a prediction unit, an integration unit, and a provision unit. The acquisition unit acquires real-time congestion status. The prediction unit makes future congestion predictions based on the information acquired by the acquisition unit. The integration unit incorporates user search information based on the information predicted by the prediction unit. The provision unit provides prediction results based on the information incorporated by the integration unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict future congestion based on real-time congestion levels and provide users with appropriate information. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] 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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The congestion prediction system according to an embodiment of the present invention is a system that uses generation AI to predict congestion levels and provides useful information to users and businesses. This congestion prediction system acquires real-time congestion levels, makes future congestion predictions, and provides prediction results by incorporating user search information. For example, the congestion prediction system acquires real-time congestion levels using Agoop's location information. At this time, it collects data such as the flow of people and the length of stay in a specific area. For example, it can grasp the congestion level of tourist destinations and shopping malls in real time. Next, the congestion prediction system makes future congestion predictions based on past payment information from an electronic payment system. Specifically, it analyzes past payment data to grasp the trends in attracting people at specific times and places. For example, it can predict how many people will gather during a specific event period based on past data. Furthermore, the congestion prediction system generates a more precise prediction map by incorporating user search information. Based on the information that users search for on the app, it grasps the level of interest in specific areas and times and reflects this in the prediction. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the congestion prediction can be corrected based on that information. This prediction map provides users with measures to avoid congestion. For example, it can suggest alternatives to avoid crowded locations and times. It can also assist businesses with demand forecasting and provide information for appropriate inventory management and staffing. For instance, it can predict the expected customer traffic at a specific time and adjust product order quantities accordingly. In this way, the congestion confirmation and forecast map utilizing generative AI is a system that solves user and business challenges and supports efficient action. Thus, the congestion forecasting system can provide users and businesses with useful congestion forecasting information and support efficient action.

[0029] The congestion prediction system according to the embodiment comprises an acquisition unit, a prediction unit, an integration unit, and a provision unit. The acquisition unit acquires real-time congestion information. The acquisition unit collects data such as the flow of people and their length of stay in a specific area, for example, using Agoop's location information. For example, the acquisition unit can grasp the congestion situation in tourist spots or shopping malls in real time. The acquisition unit can also detect the flow of people using sensors and collect data, for example. The prediction unit makes future congestion predictions based on the information acquired by the acquisition unit. For example, the prediction unit makes future congestion predictions based on past payment information from an electronic payment system. The prediction unit analyzes past payment data to understand trends in customer gatherings at specific times and locations. For example, the prediction unit can predict how many people will gather during a specific event period based on past data. The prediction unit can also make future congestion predictions using machine learning algorithms, for example. The integration unit incorporates user search information based on the information predicted by the prediction unit. For example, the integration unit grasps the level of interest in a specific area or time based on information searched by the user on the app and reflects this in the prediction. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the embedded unit can correct the congestion forecast based on that information. The embedded unit can also analyze the user's search information using, for example, natural language processing technology and reflect it in the forecast. The providing unit provides the forecast results based on the information embedded by the embedded unit. The providing unit can, for example, provide the user with measures to avoid congestion. The providing unit can suggest alternatives to avoid places and times where congestion is predicted. The providing unit can, for example, provide companies with information to support demand forecasting and enable appropriate inventory management and staffing. The providing unit can predict how many people are expected to visit at a particular time and adjust the order quantity of goods based on that. In this way, the congestion forecasting system according to the embodiment can provide useful congestion forecasting information to users and companies and support efficient action. Some or all of the above processing in the acquisition unit, forecasting unit, embedded unit, and providing unit may be performed using, for example, generative AI, or without generative AI.For example, the acquisition unit can input Agoop's location information into the generation AI and have the generation AI perform the acquisition of real-time congestion status. The prediction unit can input past payment data into the generation AI and have the generation AI perform the prediction of future congestion. The embedding unit can input user search information into the generation AI and have the generation AI perform the processing to reflect it in the prediction. The provision unit can input the prediction results into the generation AI and have the generation AI perform the processing to provide them to users and companies.

[0030] The data acquisition unit acquires real-time congestion information. For example, the data acquisition unit uses Agoop's location information to collect data such as the flow of people and their dwell time in a specific area. Specifically, Agoop's location information is based on smartphone GPS data and Wi-Fi connection information, which allows for a highly accurate understanding of the density of people and their movement patterns in a specific area. For example, it can grasp the congestion status of tourist spots and shopping malls in real time. The data acquisition unit can also detect the flow of people using sensors and collect data. Sensors such as infrared sensors, cameras, and beacons are installed to detect people's movements and collect data. For example, a camera installed at the entrance of a shopping mall counts the number of visitors and transmits the data to a central database in real time. Furthermore, the data acquisition unit centrally manages this data and can cooperate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the prediction unit and embedded units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data acquisition unit can collect data efficiently and effectively, improving the overall performance of the system. Furthermore, by using a generation AI, the acquisition unit can input Agoop's location information into the generation AI, allowing the generation AI to acquire real-time congestion information. The generation AI can quickly analyze vast amounts of data and grasp real-time congestion information with high accuracy. As a result, the acquisition unit can grasp congestion information more quickly and accurately and provide it to other departments.

[0031] The prediction unit makes future congestion predictions based on information acquired by the acquisition unit. For example, the prediction unit makes future congestion predictions based on past payment information from an electronic payment system. Specifically, it analyzes past payment data to understand crowding trends at specific times and locations. For example, it can predict how many people will gather during a specific event period based on past data. The prediction unit can also make future congestion predictions using machine learning algorithms. Machine learning algorithms learn from past data and predict future congestion with high accuracy. For example, they can learn congestion patterns before and after a specific event or holiday and predict congestion at the next similar event. Furthermore, by using a generative AI, the prediction unit can input past payment data into the generative AI and have the generative AI perform future congestion predictions. The generative AI can quickly analyze vast amounts of data and predict future congestion with high accuracy. For example, the generative AI predicts congestion at specific times and locations based on past payment data and location data and provides the prediction results. This allows the prediction unit to predict future congestion more accurately and quickly and provide the results to other departments. Furthermore, the prediction unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas or time periods based on past congestion data and formulate future countermeasures. The prediction unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the prediction unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The embedded unit incorporates user search information based on the information predicted by the prediction unit. For example, the embedded unit can understand the user's level of interest in specific areas or times based on the information the user searches for on the app and reflect this in the prediction. Specifically, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the embedded unit can adjust the congestion prediction based on that information. The embedded unit can also analyze user search information using natural language processing technology and reflect this in the prediction. Natural language processing technology analyzes the user's search query and understands their intent and interests. For example, if a user searches for "I want to go to the beach during summer vacation," the embedded unit can adjust the congestion prediction for coastal areas during summer vacation based on that information. Furthermore, by using generative AI, the embedded unit can input user search information into the generative AI and have the generative AI perform the process of reflecting it in the prediction. The generative AI can analyze the user's search query, understand their intent and interests with high accuracy, and reflect this in the prediction. For example, if a user searches for "I want to go to a shopping mall at Christmas," the generative AI can adjust the congestion prediction for shopping malls during the Christmas season based on that information. This allows the embedded system to accurately understand user interests and intentions, and to make prediction results more precise. Furthermore, the embedded system can collect user feedback and continuously improve the accuracy and effectiveness of prediction results. For example, it can adjust prediction algorithms and data based on user evaluations and feedback on prediction results. This enables the embedded system to respond flexibly to user needs and improve the overall system performance.

[0033] The service provider provides prediction results based on information embedded by the embedded system. For example, the service provider can provide users with measures to avoid congestion. Specifically, it can suggest alternatives to avoid places and times when congestion is predicted. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the service provider will suggest alternatives to avoid times when congestion is predicted. The service provider can also provide companies with information to support demand forecasting and enable appropriate inventory management and staffing. Specifically, it can predict how many customers are expected to visit during a particular period and adjust product order quantities accordingly. For example, based on congestion forecasts for shopping malls during the Christmas season, stores can implement appropriate inventory management and staffing. Furthermore, by using a generative AI, the service provider can input prediction results into the generative AI and have the generative AI execute the processing to provide information to users and companies. The generative AI analyzes the prediction results and provides optimal information to users and companies. For example, the generative AI suggests alternatives to avoid times when congestion is predicted and proposes the best course of action for the user. Furthermore, the system provides companies with information that supports inventory management and staffing optimization based on demand forecasts. This allows the service provider to offer useful congestion forecast information to users and companies, supporting efficient actions. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, it can review and improve its offerings based on user evaluations and feedback on the alternatives provided. This allows the service provider to quickly and reliably provide useful information to users and companies, improving the overall performance of the system.

[0034] The system includes an alternative suggestion unit that presents alternative options. The alternative suggestion unit presents alternative options to help the user avoid congestion. For example, the alternative suggestion unit may present alternative routes to avoid places or times where congestion is expected. The alternative suggestion unit may also present alternative times to avoid times when congestion is expected. For example, the alternative suggestion unit may present less crowded places instead of places where congestion is expected. This allows the user to obtain alternative options to avoid congestion. Some or all of the above processing in the alternative suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alternative suggestion unit may input congestion prediction results into a generative AI and have the generative AI perform the task of presenting alternative options.

[0035] The system includes a demand forecasting support unit to assist in demand forecasting. The demand forecasting support unit assists companies in forecasting demand and managing inventory appropriately. For example, the demand forecasting support unit can predict how many customers are expected to come to a particular location at a specific time and adjust the order quantity of goods accordingly. The demand forecasting support unit can also analyze past payment data to understand customer trends at specific times and locations. For example, the demand forecasting support unit can use user search information to understand the level of interest in specific areas and times and reflect this in the demand forecast. This enables companies to forecast demand and manage inventory appropriately. Some or all of the above processes in the demand forecasting support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the demand forecasting support unit can input past payment data into a generative AI and have the generative AI perform the demand forecast.

[0036] The acquisition unit can collect data such as the flow of people and their length of stay in a specific area. For example, the acquisition unit can use Agoop's location information to collect data such as the flow of people and their length of stay in a specific area. For example, the acquisition unit can grasp the congestion status of tourist spots and shopping malls in real time. The acquisition unit can also use sensors to detect the flow of people and collect data. This makes it possible to grasp the detailed congestion status of a specific area. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input Agoop's location information into a generating AI and have the generating AI perform the collection of data such as the flow of people and their length of stay in a specific area.

[0037] The prediction unit can analyze past payment data to understand trends in customer traffic at specific times and locations. For example, the prediction unit can predict future congestion based on past payment information from an electronic payment system. The prediction unit can analyze past payment data to understand trends in customer traffic at specific times and locations. For example, the prediction unit can predict how many people will gather during a specific event period based on past data. The prediction unit can also predict future congestion using machine learning algorithms, for example. This allows for future congestion predictions based on past data. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the prediction unit can input past payment data into a generative AI and have the generative AI perform future congestion predictions.

[0038] The embedded system can understand the user's level of interest in specific areas or times based on information searched on the app, and reflect this in its predictions. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the embedded system can use that information to adjust the crowd prediction. The embedded system can also analyze the user's search information using natural language processing technology, for example, and reflect this in its predictions. This allows for more precise predictions that reflect the user's search information. Some or all of the above-described processes in the embedded system may be performed using, for example, generative AI, or without generative AI. For example, the embedded system can input the user's search information into a generative AI and have the generative AI perform the task of understanding the user's level of interest in specific areas or times and reflecting this in its predictions.

[0039] The data acquisition unit can analyze past congestion data and optimize data acquisition methods according to specific events or seasons. For example, the data acquisition unit can predict congestion levels during a specific event period based on past congestion data and adjust the frequency of data acquisition. The data acquisition unit can also analyze seasonal congestion patterns and optimize data acquisition methods according to specific seasons. For example, the data acquisition unit can prioritize the acquisition of congestion data related to specific events or seasons and provide information in real time. This enables optimal data acquisition according to specific events or seasons. Some or all of the above processing in the data acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data acquisition unit can input past congestion data into a generation AI and have the generation AI optimize data acquisition methods according to specific events or seasons.

[0040] The acquisition unit can correct data when acquiring congestion status by taking into account weather information for a specific area. For example, during rainy weather, the acquisition unit can prioritize acquiring congestion status for indoor facilities and correct the data. For example, during sunny weather, the acquisition unit can also prioritize acquiring congestion status for outdoor facilities and correct the data. The acquisition unit can also correct congestion status for a specific area in real time based on weather information to provide accurate information. This makes it possible to acquire accurate congestion status that takes weather information into account. Some or all of the above processing in the acquisition unit may be performed using a generation AI, for example, or without a generation AI. For example, the acquisition unit can input weather information into a generation AI and have the generation AI perform data correction when acquiring congestion status.

[0041] The acquisition unit can prioritize acquiring data for highly relevant areas by considering the user's travel history when acquiring congestion status. For example, the acquisition unit can prioritize acquiring congestion status for places the user has visited in the past. The acquisition unit can also prioritize acquiring data for highly relevant areas from the user's travel history. For example, the acquisition unit can analyze the user's travel patterns and prioritize acquiring data for the most relevant areas. This allows for the priority acquisition of data for highly relevant areas based on the user's travel history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the user's travel history data into a generating AI and cause the generating AI to prioritize the acquisition of data for highly relevant areas.

[0042] The acquisition unit can analyze the user's social media activity and acquire data for relevant areas when acquiring congestion status. For example, the acquisition unit can prioritize acquiring congestion status for locations where the user has checked in on social media. The acquisition unit can also prioritize acquiring data for highly relevant areas based on the user's social media activity. For example, the acquisition unit can analyze the user's social media posts and prioritize acquiring data for the most relevant areas. This makes it possible to acquire data for highly relevant areas based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI acquire data for relevant areas.

[0043] The prediction unit can analyze past payment data and optimize prediction models for specific events or seasons. For example, the prediction unit can use past payment data to understand customer traffic trends during a specific event period and optimize the prediction model. The prediction unit can also analyze seasonal customer traffic patterns and optimize prediction models for specific seasons. The prediction unit can also prioritize the analysis of payment data related to specific events or seasons and optimize the prediction model. This allows the prediction unit to provide optimal prediction models for specific events or seasons. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input past payment data into a generative AI and have the generative AI perform the optimization of prediction models for specific events or seasons.

[0044] The prediction unit can correct its prediction results by taking into account weather information for a specific area during the prediction process. For example, during rainy weather, the prediction unit can prioritize predicting the number of visitors to indoor facilities and correct the prediction results. For example, during sunny weather, the prediction unit can also prioritize predicting the number of visitors to outdoor facilities and correct the prediction results. For example, the prediction unit can correct the visitor forecast for a specific area in real time based on weather information to provide accurate prediction results. This makes it possible to provide accurate prediction results that take weather information into account. Some or all of the above processing in the prediction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the prediction unit can input weather information into a generating AI and have the generating AI perform the correction of the prediction results.

[0045] The prediction unit can prioritize displaying prediction results for highly relevant areas, taking into account the user's travel history during prediction. For example, the prediction unit may prioritize displaying prediction results for places the user has visited in the past. The prediction unit can also prioritize displaying prediction results for highly relevant areas based on the user's travel history. For example, the prediction unit may analyze the user's travel patterns and prioritize displaying prediction results for the most relevant areas. This allows the prediction unit to prioritize displaying prediction results for highly relevant areas based on the user's travel history. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input user travel history data into a generative AI and cause the generative AI to prioritize displaying prediction results for highly relevant areas.

[0046] The prediction unit can analyze the user's social media activity during prediction and display prediction results for relevant areas. For example, the prediction unit may prioritize displaying prediction results for locations where the user has checked in on social media. The prediction unit may also prioritize displaying prediction results for areas that are highly relevant based on the user's social media activity. For example, the prediction unit may analyze the user's social media posts and prioritize displaying prediction results for the most relevant areas. This allows the prediction results for highly relevant areas to be displayed based on the user's social media activity. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the user's social media data into a generative AI and have the generative AI perform the display of prediction results for relevant areas.

[0047] The embedded unit can analyze the user's past search history and optimize their level of interest in specific areas or time periods. For example, the embedded unit can prioritize providing information about areas the user has searched for in the past. The embedded unit can also, for example, understand the user's level of interest in specific time periods from their past search history and provide information accordingly. The embedded unit can also, for example, analyze the user's search patterns and provide the most relevant information. This allows the embedded unit to provide optimal information based on the user's past search history. Some or all of the above processing in the embedded unit may be performed using, for example, a generative AI, or without a generative AI. For example, the embedded unit can input the user's past search history data into a generative AI and have the generative AI perform the optimization of the user's level of interest in specific areas or time periods.

[0048] The embedded unit can correct data when embedding search information, taking into account weather information for a specific area. For example, the embedded unit can prioritize providing information about indoor facilities during rainy weather. For example, the embedded unit can also prioritize providing information about outdoor facilities during sunny weather. For example, the embedded unit can correct information for a specific area in real time based on weather information to provide accurate information. This makes it possible to provide accurate search information that takes weather information into account. Some or all of the above processing in the embedded unit may be performed using, for example, a generation AI, or without a generation AI. For example, the embedded unit can input weather information into a generation AI and have the generation AI perform data correction when embedding search information.

[0049] The embedded unit can prioritize the incorporation of highly relevant search information by considering the user's travel history when embedding search information. For example, the embedded unit can prioritize providing information about places the user has visited in the past. The embedded unit can also prioritize providing highly relevant information based on the user's travel history. For example, the embedded unit can analyze the user's travel patterns and prioritize providing the most relevant information. This makes it possible to provide highly relevant search information based on the user's travel history. Some or all of the above processing in the embedded unit may be performed using, for example, a generative AI, or without a generative AI. For example, the embedded unit can input user travel history data into a generative AI and cause the generative AI to prioritize the incorporation of highly relevant search information.

[0050] The embedded unit can analyze the user's social media activity and incorporate relevant search information when embedding search information. For example, the embedded unit can prioritize providing information about places the user has checked into on social media. The embedded unit can also prioritize providing highly relevant information from the user's social media activity. For example, the embedded unit can analyze the user's social media posts and prioritize providing the most relevant information. This allows the embedded unit to provide highly relevant search information based on the user's social media activity. Some or all of the above processing in the embedded unit may be performed using, for example, a generative AI, or without a generative AI. For example, the embedded unit can input the user's social media data into a generative AI and have the generative AI perform the embedding of relevant search information.

[0051] The service provider can analyze a user's past usage history and optimize prediction results for specific areas and time periods. For example, the service provider can prioritize providing prediction results for areas the user has used in the past. The service provider can also provide prediction results for specific time periods based on the user's past usage history. The service provider can also analyze a user's usage patterns and provide the most relevant prediction results. This allows the service provider to provide optimal prediction results based on the user's past usage history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past usage history data into a generative AI and have the generative AI optimize prediction results for specific areas and time periods.

[0052] The service provider can correct the data when providing forecast results, taking into account weather information for a specific area. For example, the service provider may prioritize providing forecast results for indoor facilities during rainy weather. For example, the service provider may also prioritize providing forecast results for outdoor facilities during sunny weather. The service provider can also correct the forecast results for a specific area in real time based on weather information to provide accurate information. This makes it possible to provide accurate forecast results that take weather information into account. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input weather information into a generating AI and have the generating AI perform data correction when providing forecast results.

[0053] The service provider can prioritize providing highly relevant prediction results by considering the user's travel history when providing prediction results. For example, the service provider can prioritize providing prediction results for places the user has visited in the past. The service provider can also prioritize providing highly relevant prediction results based on the user's travel history. The service provider can also analyze the user's travel patterns and prioritize providing the most relevant prediction results. This allows the service provider to provide highly relevant prediction results based on the user's travel history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input user travel history data into a generative AI and have the generative AI prioritize providing highly relevant prediction results.

[0054] The service provider can analyze the user's social media activity and provide relevant prediction results when providing prediction results. For example, the service provider may prioritize providing prediction results for locations where the user has checked in on social media. The service provider may also prioritize providing highly relevant prediction results based on the user's social media activity. The service provider may also analyze the user's social media posts and prioritize providing the most relevant prediction results. This allows the service provider to provide highly relevant prediction results based on the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input the user's social media data into a generative AI and have the generative AI perform the provision of relevant prediction results.

[0055] The alternative suggestion unit can analyze the user's past selection history and optimize alternatives for specific areas or time periods. For example, the alternative suggestion unit may prioritize providing alternatives for areas previously selected by the user. The alternative suggestion unit can also provide alternatives for specific time periods based on the user's past selection history. The alternative suggestion unit can also analyze the user's selection patterns and provide the most relevant alternatives. This allows the system to provide optimal alternatives based on the user's past selection history. Some or all of the above-described processes in the alternative suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alternative suggestion unit can input the user's past selection history data into a generative AI and have the generative AI optimize alternatives for specific areas or time periods.

[0056] The alternative suggestion unit can prioritize presenting highly relevant alternatives by considering the user's travel history when presenting alternatives. For example, the alternative suggestion unit may prioritize providing alternatives to places the user has visited in the past. The alternative suggestion unit can also prioritize providing highly relevant alternatives based on the user's travel history. The alternative suggestion unit can also analyze the user's travel patterns and prioritize providing the most relevant alternatives. This allows the system to provide highly relevant alternatives based on the user's travel history. Some or all of the above processing in the alternative suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alternative suggestion unit can input the user's travel history data into a generative AI and cause the generative AI to prioritize presenting highly relevant alternatives.

[0057] The demand forecasting support unit can analyze historical demand data and optimize demand forecasting models for specific areas and periods. For example, the demand forecasting support unit can perform demand forecasting during a specific event period based on historical demand data and optimize the forecasting model. For example, the demand forecasting support unit can analyze seasonal demand patterns and optimize demand forecasting models for specific seasons. For example, the demand forecasting support unit can prioritize the analysis of demand data related to specific events or seasons and optimize the forecasting model. This allows the unit to provide the optimal demand forecasting model based on historical demand data. Some or all of the above-described processes in the demand forecasting support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the demand forecasting support unit can input historical demand data into a generative AI and have the generative AI perform the optimization of demand forecasting models for specific areas and periods.

[0058] The demand forecasting support unit can prioritize providing highly relevant demand forecast results by considering the user's travel history during demand forecasting. For example, the demand forecasting support unit can prioritize providing demand forecast results for places the user has visited in the past. For example, the demand forecasting support unit can also prioritize providing highly relevant demand forecast results based on the user's travel history. For example, the demand forecasting support unit can analyze the user's travel patterns and prioritize providing the most relevant demand forecast results. This allows the unit to provide highly relevant demand forecast results based on the user's travel history. Some or all of the above processing in the demand forecasting support unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the demand forecasting support unit can input the user's travel history data into a generating AI and have the generating AI prioritize providing highly relevant demand forecast results.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The congestion prediction system may further include a search history analysis unit that analyzes the user's past search history and optimizes their level of interest in specific areas and time periods. The search history analysis unit may, for example, prioritize providing information on areas the user has searched for in the past. It can also understand the user's level of interest in specific time periods from their past search history and provide information accordingly. It can also analyze the user's search patterns and provide the most relevant information. This allows the system to provide optimal information based on the user's past search history. Some or all of the above-described processes in the search history analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search history analysis unit can input the user's past search history data into a generative AI and have the generative AI perform the optimization of the user's level of interest in specific areas and time periods.

[0061] The congestion prediction system may further include a movement history analysis unit that prioritizes acquiring data for highly relevant areas, taking into account the user's movement history. The movement history analysis unit may, for example, prioritize acquiring congestion information for places the user has visited in the past. It can also prioritize acquiring data for highly relevant areas from the user's movement history. It can also analyze the user's movement patterns and prioritize acquiring data for the most relevant areas. This allows for the priority acquisition of data for highly relevant areas based on the user's movement history. Some or all of the above-described processes in the movement history analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the movement history analysis unit can input the user's movement history data into a generative AI and cause the generative AI to prioritize acquiring data for highly relevant areas.

[0062] The congestion prediction system may further include a social media analysis unit that analyzes users' social media activity and acquires data for relevant areas. The social media analysis unit may, for example, prioritize acquiring congestion information for locations where users have checked in on social media. It may also prioritize acquiring data for highly relevant areas based on users' social media activity. It may also analyze users' social media posts and prioritize acquiring data for the most relevant areas. This allows for the acquisition of data for highly relevant areas based on users' social media activity. Some or all of the above processing in the social media analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the social media analysis unit may input user social media data into a generative AI and have the generative AI acquire data for relevant areas.

[0063] The congestion prediction system may further include a movement history prediction unit that prioritizes providing highly relevant prediction results by considering the user's movement history. The movement history prediction unit may, for example, prioritize providing prediction results for places the user has visited in the past. It may also prioritize providing highly relevant prediction results based on the user's movement history. It may also analyze the user's movement patterns and prioritize providing the most relevant prediction results. This makes it possible to provide highly relevant prediction results based on the user's movement history. Some or all of the above processing in the movement history prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the movement history prediction unit may input the user's movement history data into a generative AI and cause the generative AI to prioritize providing highly relevant prediction results.

[0064] The congestion prediction system may further include a selection history analysis unit that analyzes the user's past selection history and optimizes alternatives for specific areas and times. The selection history analysis unit may, for example, prioritize providing alternatives for areas previously selected by the user. It may also provide alternatives for specific times based on the user's past selection history. It may also analyze the user's selection patterns and provide the most relevant alternatives. This allows the system to provide optimal alternatives based on the user's past selection history. Some or all of the above processing in the selection history analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection history analysis unit may input the user's past selection history data into a generative AI and have the generative AI perform the optimization of alternatives for specific areas and times.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The acquisition unit acquires real-time congestion information. The acquisition unit collects data such as the flow of people and their length of stay in a specific area, for example, using Agoop's location information. This allows for real-time monitoring of congestion in tourist areas, shopping malls, and other locations. It is also possible to detect the flow of people using sensors and collect data. Step 2: The prediction unit makes future congestion predictions based on the information acquired by the acquisition unit. For example, the prediction unit makes future congestion predictions based on past payment information from an electronic payment system. By analyzing past payment data, it grasps customer attraction trends at specific times and locations. It is also possible to make future congestion predictions using machine learning algorithms. Step 3: The embedded unit incorporates user search information based on the information predicted by the prediction unit. For example, the embedded unit understands the user's level of interest in specific areas or time periods based on the information the user has searched for on the app, and reflects this in the prediction. It is also possible to analyze the user's search information using natural language processing technology and reflect this in the prediction. Step 4: The provider unit provides forecast results based on the information incorporated by the embedded unit. For example, the provider unit can provide users with measures to avoid congestion. It can suggest alternatives to avoid places and times where congestion is predicted. It can also provide companies with information to support demand forecasting and enable appropriate inventory management and staffing.

[0067] (Example of form 2) The congestion prediction system according to an embodiment of the present invention is a system that uses generation AI to predict congestion levels and provides useful information to users and businesses. This congestion prediction system acquires real-time congestion levels, makes future congestion predictions, and provides prediction results by incorporating user search information. For example, the congestion prediction system acquires real-time congestion levels using Agoop's location information. At this time, it collects data such as the flow of people and the length of stay in a specific area. For example, it can grasp the congestion level of tourist destinations and shopping malls in real time. Next, the congestion prediction system makes future congestion predictions based on past payment information from an electronic payment system. Specifically, it analyzes past payment data to grasp the trends in attracting people at specific times and places. For example, it can predict how many people will gather during a specific event period based on past data. Furthermore, the congestion prediction system generates a more precise prediction map by incorporating user search information. Based on the information that users search for on the app, it grasps the level of interest in specific areas and times and reflects this in the prediction. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the congestion prediction can be corrected based on that information. This prediction map provides users with measures to avoid congestion. For example, it can suggest alternatives to avoid crowded locations and times. It can also assist businesses with demand forecasting and provide information for appropriate inventory management and staffing. For instance, it can predict the expected customer traffic at a specific time and adjust product order quantities accordingly. In this way, the congestion confirmation and forecast map utilizing generative AI is a system that solves user and business challenges and supports efficient action. Thus, the congestion forecasting system can provide users and businesses with useful congestion forecasting information and support efficient action.

[0068] The congestion prediction system according to the embodiment comprises an acquisition unit, a prediction unit, an integration unit, and a provision unit. The acquisition unit acquires real-time congestion information. The acquisition unit collects data such as the flow of people and their length of stay in a specific area, for example, using Agoop's location information. For example, the acquisition unit can grasp the congestion situation in tourist spots or shopping malls in real time. The acquisition unit can also detect the flow of people using sensors and collect data, for example. The prediction unit makes future congestion predictions based on the information acquired by the acquisition unit. For example, the prediction unit makes future congestion predictions based on past payment information from an electronic payment system. The prediction unit analyzes past payment data to understand trends in customer gatherings at specific times and locations. For example, the prediction unit can predict how many people will gather during a specific event period based on past data. The prediction unit can also make future congestion predictions using machine learning algorithms, for example. The integration unit incorporates user search information based on the information predicted by the prediction unit. For example, the integration unit grasps the level of interest in a specific area or time based on information searched by the user on the app and reflects this in the prediction. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the embedded unit can correct the congestion forecast based on that information. The embedded unit can also analyze the user's search information using, for example, natural language processing technology and reflect it in the forecast. The providing unit provides the forecast results based on the information embedded by the embedded unit. The providing unit can, for example, provide the user with measures to avoid congestion. The providing unit can suggest alternatives to avoid places and times where congestion is predicted. The providing unit can, for example, provide companies with information to support demand forecasting and enable appropriate inventory management and staffing. The providing unit can predict how many people are expected to visit at a particular time and adjust the order quantity of goods based on that. In this way, the congestion forecasting system according to the embodiment can provide useful congestion forecasting information to users and companies and support efficient action. Some or all of the above processing in the acquisition unit, forecasting unit, embedded unit, and providing unit may be performed using, for example, generative AI, or without generative AI.For example, the acquisition unit can input Agoop's location information into the generation AI and have the generation AI perform the acquisition of real-time congestion status. The prediction unit can input past payment data into the generation AI and have the generation AI perform the prediction of future congestion. The embedding unit can input user search information into the generation AI and have the generation AI perform the processing to reflect it in the prediction. The provision unit can input the prediction results into the generation AI and have the generation AI perform the processing to provide them to users and companies.

[0069] The data acquisition unit acquires real-time congestion information. For example, the data acquisition unit uses Agoop's location information to collect data such as the flow of people and their dwell time in a specific area. Specifically, Agoop's location information is based on smartphone GPS data and Wi-Fi connection information, which allows for a highly accurate understanding of the density of people and their movement patterns in a specific area. For example, it can grasp the congestion status of tourist spots and shopping malls in real time. The data acquisition unit can also detect the flow of people using sensors and collect data. Sensors such as infrared sensors, cameras, and beacons are installed to detect people's movements and collect data. For example, a camera installed at the entrance of a shopping mall counts the number of visitors and transmits the data to a central database in real time. Furthermore, the data acquisition unit centrally manages this data and can cooperate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the prediction unit and embedded units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data acquisition unit can collect data efficiently and effectively, improving the overall performance of the system. Furthermore, by using a generation AI, the acquisition unit can input Agoop's location information into the generation AI, allowing the generation AI to acquire real-time congestion information. The generation AI can quickly analyze vast amounts of data and grasp real-time congestion information with high accuracy. As a result, the acquisition unit can grasp congestion information more quickly and accurately and provide it to other departments.

[0070] The prediction unit makes future congestion predictions based on information acquired by the acquisition unit. For example, the prediction unit makes future congestion predictions based on past payment information from an electronic payment system. Specifically, it analyzes past payment data to understand crowding trends at specific times and locations. For example, it can predict how many people will gather during a specific event period based on past data. The prediction unit can also make future congestion predictions using machine learning algorithms. Machine learning algorithms learn from past data and predict future congestion with high accuracy. For example, they can learn congestion patterns before and after a specific event or holiday and predict congestion at the next similar event. Furthermore, by using a generative AI, the prediction unit can input past payment data into the generative AI and have the generative AI perform future congestion predictions. The generative AI can quickly analyze vast amounts of data and predict future congestion with high accuracy. For example, the generative AI predicts congestion at specific times and locations based on past payment data and location data and provides the prediction results. This allows the prediction unit to predict future congestion more accurately and quickly and provide the results to other departments. Furthermore, the prediction unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas or time periods based on past congestion data and formulate future countermeasures. The prediction unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the prediction unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0071] The embedded unit incorporates user search information based on the information predicted by the prediction unit. For example, the embedded unit can understand the user's level of interest in specific areas or times based on the information the user searches for on the app and reflect this in the prediction. Specifically, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the embedded unit can adjust the congestion prediction based on that information. The embedded unit can also analyze user search information using natural language processing technology and reflect this in the prediction. Natural language processing technology analyzes the user's search query and understands their intent and interests. For example, if a user searches for "I want to go to the beach during summer vacation," the embedded unit can adjust the congestion prediction for coastal areas during summer vacation based on that information. Furthermore, by using generative AI, the embedded unit can input user search information into the generative AI and have the generative AI perform the process of reflecting it in the prediction. The generative AI can analyze the user's search query, understand their intent and interests with high accuracy, and reflect this in the prediction. For example, if a user searches for "I want to go to a shopping mall at Christmas," the generative AI can adjust the congestion prediction for shopping malls during the Christmas season based on that information. This allows the embedded system to accurately understand user interests and intentions, and to make prediction results more precise. Furthermore, the embedded system can collect user feedback and continuously improve the accuracy and effectiveness of prediction results. For example, it can adjust prediction algorithms and data based on user evaluations and feedback on prediction results. This enables the embedded system to respond flexibly to user needs and improve the overall system performance.

[0072] The service provider provides prediction results based on information embedded by the embedded system. For example, the service provider can provide users with measures to avoid congestion. Specifically, it can suggest alternatives to avoid places and times when congestion is predicted. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the service provider will suggest alternatives to avoid times when congestion is predicted. The service provider can also provide companies with information to support demand forecasting and enable appropriate inventory management and staffing. Specifically, it can predict how many customers are expected to visit during a particular period and adjust product order quantities accordingly. For example, based on congestion forecasts for shopping malls during the Christmas season, stores can implement appropriate inventory management and staffing. Furthermore, by using a generative AI, the service provider can input prediction results into the generative AI and have the generative AI execute the processing to provide information to users and companies. The generative AI analyzes the prediction results and provides optimal information to users and companies. For example, the generative AI suggests alternatives to avoid times when congestion is predicted and proposes the best course of action for the user. Furthermore, the system provides companies with information that supports inventory management and staffing optimization based on demand forecasts. This allows the service provider to offer useful congestion forecast information to users and companies, supporting efficient actions. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, it can review and improve its offerings based on user evaluations and feedback on the alternatives provided. This allows the service provider to quickly and reliably provide useful information to users and companies, improving the overall performance of the system.

[0073] The system includes an alternative suggestion unit that presents alternative options. The alternative suggestion unit presents alternative options to help the user avoid congestion. For example, the alternative suggestion unit may present alternative routes to avoid places or times where congestion is expected. The alternative suggestion unit may also present alternative times to avoid times when congestion is expected. For example, the alternative suggestion unit may present less crowded places instead of places where congestion is expected. This allows the user to obtain alternative options to avoid congestion. Some or all of the above processing in the alternative suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alternative suggestion unit may input congestion prediction results into a generative AI and have the generative AI perform the task of presenting alternative options.

[0074] The system includes a demand forecasting support unit to assist in demand forecasting. The demand forecasting support unit assists companies in forecasting demand and managing inventory appropriately. For example, the demand forecasting support unit can predict how many customers are expected to come to a particular location at a specific time and adjust the order quantity of goods accordingly. The demand forecasting support unit can also analyze past payment data to understand customer trends at specific times and locations. For example, the demand forecasting support unit can use user search information to understand the level of interest in specific areas and times and reflect this in the demand forecast. This enables companies to forecast demand and manage inventory appropriately. Some or all of the above processes in the demand forecasting support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the demand forecasting support unit can input past payment data into a generative AI and have the generative AI perform the demand forecast.

[0075] The acquisition unit can collect data such as the flow of people and their length of stay in a specific area. For example, the acquisition unit can use Agoop's location information to collect data such as the flow of people and their length of stay in a specific area. For example, the acquisition unit can grasp the congestion status of tourist spots and shopping malls in real time. The acquisition unit can also use sensors to detect the flow of people and collect data. This makes it possible to grasp the detailed congestion status of a specific area. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input Agoop's location information into a generating AI and have the generating AI perform the collection of data such as the flow of people and their length of stay in a specific area.

[0076] The prediction unit can analyze past payment data to understand trends in customer traffic at specific times and locations. For example, the prediction unit can predict future congestion based on past payment information from an electronic payment system. The prediction unit can analyze past payment data to understand trends in customer traffic at specific times and locations. For example, the prediction unit can predict how many people will gather during a specific event period based on past data. The prediction unit can also predict future congestion using machine learning algorithms, for example. This allows for future congestion predictions based on past data. Some or all of the above-described processes in the prediction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the prediction unit can input past payment data into a generative AI and have the generative AI perform future congestion predictions.

[0077] The embedded system can understand the user's level of interest in specific areas or times based on information searched on the app, and reflect this in its predictions. For example, if a user searches for "I want to go to Tokyo Disneyland on the weekend," the embedded system can use that information to adjust the crowd prediction. The embedded system can also analyze the user's search information using natural language processing technology, for example, and reflect this in its predictions. This allows for more precise predictions that reflect the user's search information. Some or all of the above-described processes in the embedded system may be performed using, for example, generative AI, or without generative AI. For example, the embedded system can input the user's search information into a generative AI and have the generative AI perform the task of understanding the user's level of interest in specific areas or times and reflecting this in its predictions.

[0078] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring congestion information based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can increase the frequency of acquiring congestion information to enhance real-time information provision. For example, if the user is relaxed, the acquisition unit can also decrease the frequency of acquiring congestion information and provide information only when necessary. For example, if the user is in a hurry, the acquisition unit can prioritize acquiring information on the most crowded areas and provide it quickly. This allows for acquiring congestion information at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or not. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the timing of acquiring congestion information.

[0079] The data acquisition unit can analyze past congestion data and optimize data acquisition methods according to specific events or seasons. For example, the data acquisition unit can predict congestion levels during a specific event period based on past congestion data and adjust the frequency of data acquisition. The data acquisition unit can also analyze seasonal congestion patterns and optimize data acquisition methods according to specific seasons. For example, the data acquisition unit can prioritize the acquisition of congestion data related to specific events or seasons and provide information in real time. This enables optimal data acquisition according to specific events or seasons. Some or all of the above processing in the data acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data acquisition unit can input past congestion data into a generation AI and have the generation AI optimize data acquisition methods according to specific events or seasons.

[0080] The acquisition unit can correct data when acquiring congestion status by taking into account weather information for a specific area. For example, during rainy weather, the acquisition unit can prioritize acquiring congestion status for indoor facilities and correct the data. For example, during sunny weather, the acquisition unit can also prioritize acquiring congestion status for outdoor facilities and correct the data. The acquisition unit can also correct congestion status for a specific area in real time based on weather information to provide accurate information. This makes it possible to acquire accurate congestion status that takes weather information into account. Some or all of the above processing in the acquisition unit may be performed using a generation AI, for example, or without a generation AI. For example, the acquisition unit can input weather information into a generation AI and have the generation AI perform data correction when acquiring congestion status.

[0081] The acquisition unit can estimate the user's emotions and determine the priority of areas to acquire based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit may prioritize acquiring information on less crowded areas. For example, if the user is relaxed, the acquisition unit may prioritize acquiring information on tourist attractions and leisure facilities. For example, if the user is in a hurry, the acquisition unit may prioritize acquiring information on the most crowded areas and provide it quickly. This allows for the priority acquisition of information on appropriate areas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or not using a generative AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of areas to acquire.

[0082] The acquisition unit can prioritize acquiring data for highly relevant areas by considering the user's travel history when acquiring congestion status. For example, the acquisition unit can prioritize acquiring congestion status for places the user has visited in the past. The acquisition unit can also prioritize acquiring data for highly relevant areas from the user's travel history. For example, the acquisition unit can analyze the user's travel patterns and prioritize acquiring data for the most relevant areas. This allows for the priority acquisition of data for highly relevant areas based on the user's travel history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the user's travel history data into a generating AI and cause the generating AI to prioritize the acquisition of data for highly relevant areas.

[0083] The acquisition unit can analyze the user's social media activity and acquire data for relevant areas when acquiring congestion status. For example, the acquisition unit can prioritize acquiring congestion status for locations where the user has checked in on social media. The acquisition unit can also prioritize acquiring data for highly relevant areas based on the user's social media activity. For example, the acquisition unit can analyze the user's social media posts and prioritize acquiring data for the most relevant areas. This makes it possible to acquire data for highly relevant areas based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI acquire data for relevant areas.

[0084] The prediction unit can estimate the user's emotions and adjust the prediction algorithm based on the estimated emotions. For example, if the user is stressed, the prediction unit can enhance the prediction algorithm to avoid crowded places. For example, if the user is relaxed, the prediction unit can also enhance the prediction algorithm for tourist destinations and leisure facilities. For example, if the user is in a hurry, the prediction unit can also enhance the prediction algorithm for the most crowded areas. This allows the system to provide an appropriate prediction algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using a generative AI, or not. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the prediction algorithm.

[0085] The prediction unit can analyze past payment data and optimize prediction models for specific events or seasons. For example, the prediction unit can use past payment data to understand customer traffic trends during a specific event period and optimize the prediction model. The prediction unit can also analyze seasonal customer traffic patterns and optimize prediction models for specific seasons. The prediction unit can also prioritize the analysis of payment data related to specific events or seasons and optimize the prediction model. This allows the prediction unit to provide optimal prediction models for specific events or seasons. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input past payment data into a generative AI and have the generative AI perform the optimization of prediction models for specific events or seasons.

[0086] The prediction unit can correct its prediction results by taking into account weather information for a specific area during the prediction process. For example, during rainy weather, the prediction unit can prioritize predicting the number of visitors to indoor facilities and correct the prediction results. For example, during sunny weather, the prediction unit can also prioritize predicting the number of visitors to outdoor facilities and correct the prediction results. For example, the prediction unit can correct the visitor forecast for a specific area in real time based on weather information to provide accurate prediction results. This makes it possible to provide accurate prediction results that take weather information into account. Some or all of the above processing in the prediction unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the prediction unit can input weather information into a generating AI and have the generating AI perform the correction of the prediction results.

[0087] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is stressed, the prediction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the prediction unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit can also provide a display method that gets straight to the point. This allows the prediction results to be provided in an appropriate display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the prediction results.

[0088] The prediction unit can prioritize displaying prediction results for highly relevant areas, taking into account the user's travel history during prediction. For example, the prediction unit may prioritize displaying prediction results for places the user has visited in the past. The prediction unit can also prioritize displaying prediction results for highly relevant areas based on the user's travel history. For example, the prediction unit may analyze the user's travel patterns and prioritize displaying prediction results for the most relevant areas. This allows the prediction unit to prioritize displaying prediction results for highly relevant areas based on the user's travel history. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input user travel history data into a generative AI and cause the generative AI to prioritize displaying prediction results for highly relevant areas.

[0089] The prediction unit can analyze the user's social media activity during prediction and display prediction results for relevant areas. For example, the prediction unit may prioritize displaying prediction results for locations where the user has checked in on social media. The prediction unit may also prioritize displaying prediction results for areas that are highly relevant based on the user's social media activity. For example, the prediction unit may analyze the user's social media posts and prioritize displaying prediction results for the most relevant areas. This allows the prediction results for highly relevant areas to be displayed based on the user's social media activity. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the user's social media data into a generative AI and have the generative AI perform the display of prediction results for relevant areas.

[0090] The embedded system can estimate the user's emotions and adjust how search information is embedded based on the estimated emotions. For example, if the user is stressed, the embedded system can provide simple search results and avoid providing too much information. If the user is relaxed, the embedded system can provide detailed search results and enrich the information. If the user is in a hurry, the embedded system can prioritize providing the most relevant search results. This allows search information to be embedded in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the embedded system may be performed using a generative AI, or not. For example, the embedded system can input user emotion data into a generative AI and have the generative AI adjust how search information is embedded.

[0091] The embedded unit can analyze the user's past search history and optimize their level of interest in specific areas or time periods. For example, the embedded unit can prioritize providing information about areas the user has searched for in the past. The embedded unit can also, for example, understand the user's level of interest in specific time periods from their past search history and provide information accordingly. The embedded unit can also, for example, analyze the user's search patterns and provide the most relevant information. This allows the embedded unit to provide optimal information based on the user's past search history. Some or all of the above processing in the embedded unit may be performed using, for example, a generative AI, or without a generative AI. For example, the embedded unit can input the user's past search history data into a generative AI and have the generative AI perform the optimization of the user's level of interest in specific areas or time periods.

[0092] The embedded unit can correct data when embedding search information, taking into account weather information for a specific area. For example, the embedded unit can prioritize providing information about indoor facilities during rainy weather. For example, the embedded unit can also prioritize providing information about outdoor facilities during sunny weather. For example, the embedded unit can correct information for a specific area in real time based on weather information to provide accurate information. This makes it possible to provide accurate search information that takes weather information into account. Some or all of the above processing in the embedded unit may be performed using, for example, a generation AI, or without a generation AI. For example, the embedded unit can input weather information into a generation AI and have the generation AI perform data correction when embedding search information.

[0093] The embedded system can estimate the user's emotions and determine the priority of the search information to be embedded based on the estimated emotions. For example, if the user is stressed, the embedded system may prioritize providing simple and easily visible information. For example, if the user is relaxed, the embedded system may prioritize providing detailed information. For example, if the user is in a hurry, the embedded system may prioritize providing the most relevant information. This allows the system to provide search information with appropriate priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the embedded system may be performed using, for example, a generative AI, or not using a generative AI. For example, the embedded system can input user emotion data into a generative AI and have the generative AI determine the priority of the search information.

[0094] The embedded unit can prioritize the incorporation of highly relevant search information by considering the user's travel history when embedding search information. For example, the embedded unit can prioritize providing information about places the user has visited in the past. The embedded unit can also prioritize providing highly relevant information based on the user's travel history. For example, the embedded unit can analyze the user's travel patterns and prioritize providing the most relevant information. This makes it possible to provide highly relevant search information based on the user's travel history. Some or all of the above processing in the embedded unit may be performed using, for example, a generative AI, or without a generative AI. For example, the embedded unit can input user travel history data into a generative AI and cause the generative AI to prioritize the incorporation of highly relevant search information.

[0095] The embedded unit can analyze the user's social media activity and incorporate relevant search information when embedding search information. For example, the embedded unit can prioritize providing information about places the user has checked into on social media. The embedded unit can also prioritize providing highly relevant information from the user's social media activity. For example, the embedded unit can analyze the user's social media posts and prioritize providing the most relevant information. This allows the embedded unit to provide highly relevant search information based on the user's social media activity. Some or all of the above processing in the embedded unit may be performed using, for example, a generative AI, or without a generative AI. For example, the embedded unit can input the user's social media data into a generative AI and have the generative AI perform the embedding of relevant search information.

[0096] The service provider can estimate the user's emotions and adjust the method of providing prediction results based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand prediction results. For example, if the user is relaxed, the service provider can also provide detailed prediction results. For example, if the user is in a hurry, the service provider can also provide concise prediction results. This allows the service provider to provide prediction results in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the method of providing prediction results.

[0097] The service provider can analyze a user's past usage history and optimize prediction results for specific areas and time periods. For example, the service provider can prioritize providing prediction results for areas the user has used in the past. The service provider can also provide prediction results for specific time periods based on the user's past usage history. The service provider can also analyze a user's usage patterns and provide the most relevant prediction results. This allows the service provider to provide optimal prediction results based on the user's past usage history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past usage history data into a generative AI and have the generative AI optimize prediction results for specific areas and time periods.

[0098] The service provider can correct the data when providing forecast results, taking into account weather information for a specific area. For example, the service provider may prioritize providing forecast results for indoor facilities during rainy weather. For example, the service provider may also prioritize providing forecast results for outdoor facilities during sunny weather. The service provider can also correct the forecast results for a specific area in real time based on weather information to provide accurate information. This makes it possible to provide accurate forecast results that take weather information into account. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input weather information into a generating AI and have the generating AI perform data correction when providing forecast results.

[0099] The service provider can estimate the user's emotions and determine the priority of the prediction results to be provided based on the estimated user emotions. For example, if the user is stressed, the service provider may prioritize providing simple and highly visual prediction results. For example, if the user is relaxed, the service provider may prioritize providing detailed prediction results. For example, if the user is in a hurry, the service provider may prioritize providing the most relevant prediction results. This allows the service provider to provide prediction results with appropriate priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of the prediction results.

[0100] The service provider can prioritize providing highly relevant prediction results by considering the user's travel history when providing prediction results. For example, the service provider can prioritize providing prediction results for places the user has visited in the past. The service provider can also prioritize providing highly relevant prediction results based on the user's travel history. The service provider can also analyze the user's travel patterns and prioritize providing the most relevant prediction results. This allows the service provider to provide highly relevant prediction results based on the user's travel history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input user travel history data into a generative AI and have the generative AI prioritize providing highly relevant prediction results.

[0101] The service provider can analyze the user's social media activity and provide relevant prediction results when providing prediction results. For example, the service provider may prioritize providing prediction results for locations where the user has checked in on social media. The service provider may also prioritize providing highly relevant prediction results based on the user's social media activity. The service provider may also analyze the user's social media posts and prioritize providing the most relevant prediction results. This allows the service provider to provide highly relevant prediction results based on the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input the user's social media data into a generative AI and have the generative AI perform the provision of relevant prediction results.

[0102] The alternative suggestion unit can estimate the user's emotions and adjust the method of presenting alternatives based on the estimated emotions. For example, if the user is stressed, the alternative suggestion unit can provide simple and easily understandable alternatives. For example, if the user is relaxed, the alternative suggestion unit can also provide detailed alternatives. For example, if the user is in a hurry, the alternative suggestion unit can also provide the most relevant alternatives. This allows for the provision of alternatives in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the alternative suggestion unit may be performed using a generative AI or not. For example, the alternative suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the method of presenting alternatives.

[0103] The alternative suggestion unit can analyze the user's past selection history and optimize alternatives for specific areas or time periods. For example, the alternative suggestion unit may prioritize providing alternatives for areas previously selected by the user. The alternative suggestion unit can also provide alternatives for specific time periods based on the user's past selection history. The alternative suggestion unit can also analyze the user's selection patterns and provide the most relevant alternatives. This allows the system to provide optimal alternatives based on the user's past selection history. Some or all of the above-described processes in the alternative suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alternative suggestion unit can input the user's past selection history data into a generative AI and have the generative AI optimize alternatives for specific areas or time periods.

[0104] The alternative suggestion unit can estimate the user's emotions and determine the priority of alternatives to present based on the estimated emotions. For example, if the user is stressed, the alternative suggestion unit may prioritize simple and easily recognizable alternatives. If the user is relaxed, the alternative suggestion unit may also prioritize detailed alternatives. If the user is in a hurry, the alternative suggestion unit may also prioritize the most relevant alternatives. This allows for the provision of alternatives with appropriate priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the alternative suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the alternative suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of alternatives.

[0105] The alternative suggestion unit can prioritize presenting highly relevant alternatives by considering the user's travel history when presenting alternatives. For example, the alternative suggestion unit may prioritize providing alternatives to places the user has visited in the past. The alternative suggestion unit can also prioritize providing highly relevant alternatives based on the user's travel history. The alternative suggestion unit can also analyze the user's travel patterns and prioritize providing the most relevant alternatives. This allows the system to provide highly relevant alternatives based on the user's travel history. Some or all of the above processing in the alternative suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alternative suggestion unit can input the user's travel history data into a generative AI and cause the generative AI to prioritize presenting highly relevant alternatives.

[0106] The demand forecasting support unit can estimate the user's emotions and adjust the demand forecasting method based on the estimated user emotions. For example, if the user is stressed, the demand forecasting support unit can provide a simple and easy-to-understand demand forecast result. For example, if the user is relaxed, the demand forecasting support unit can also provide a detailed demand forecast result. For example, if the user is in a hurry, the demand forecasting support unit can also provide the most relevant demand forecast result. This allows for the provision of demand forecasts in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the demand forecasting support unit may be performed using a generative AI, or not using a generative AI. For example, the demand forecasting support unit can input user emotion data into a generative AI and have the generative AI adjust the demand forecasting method.

[0107] The demand forecasting support unit can analyze historical demand data and optimize demand forecasting models for specific areas and periods. For example, the demand forecasting support unit can perform demand forecasting during a specific event period based on historical demand data and optimize the forecasting model. For example, the demand forecasting support unit can analyze seasonal demand patterns and optimize demand forecasting models for specific seasons. For example, the demand forecasting support unit can prioritize the analysis of demand data related to specific events or seasons and optimize the forecasting model. This allows the unit to provide the optimal demand forecasting model based on historical demand data. Some or all of the above-described processes in the demand forecasting support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the demand forecasting support unit can input historical demand data into a generative AI and have the generative AI perform the optimization of demand forecasting models for specific areas and periods.

[0108] The demand forecasting support unit can estimate the user's emotions and determine the priority of demand forecast results based on the estimated user emotions. For example, if the user is stressed, the demand forecasting support unit can prioritize providing simple and highly visual demand forecast results. For example, if the user is relaxed, the demand forecasting support unit can also prioritize providing detailed demand forecast results. For example, if the user is in a hurry, the demand forecasting support unit can also prioritize providing the most relevant demand forecast results. This allows for the provision of demand forecast results with appropriate priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the demand forecasting support unit may be performed using a generative AI, or not. For example, the demand forecasting support unit can input user emotion data into a generative AI and have the generative AI determine the priority of demand forecast results.

[0109] The demand forecasting support unit can prioritize providing highly relevant demand forecast results by considering the user's travel history during demand forecasting. For example, the demand forecasting support unit can prioritize providing demand forecast results for places the user has visited in the past. For example, the demand forecasting support unit can also prioritize providing highly relevant demand forecast results based on the user's travel history. For example, the demand forecasting support unit can analyze the user's travel patterns and prioritize providing the most relevant demand forecast results. This allows the unit to provide highly relevant demand forecast results based on the user's travel history. Some or all of the above processing in the demand forecasting support unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the demand forecasting support unit can input the user's travel history data into a generating AI and have the generating AI prioritize providing highly relevant demand forecast results.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The congestion prediction system may further include a display adjustment unit that estimates the user's emotions and adjusts the display method of the prediction results based on the estimated emotions. For example, if the user is stressed, the display adjustment unit may provide a simple and highly visible display method. If the user is relaxed, it may also provide a display method that includes detailed information. If the user is in a hurry, it may also provide a display method that gets straight to the point. This allows the system to provide prediction results in an appropriate display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display adjustment unit may be performed using a generative AI, or not using a generative AI. For example, the display adjustment unit may input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0112] The congestion prediction system may further include a search history analysis unit that analyzes the user's past search history and optimizes their level of interest in specific areas and time periods. The search history analysis unit may, for example, prioritize providing information on areas the user has searched for in the past. It can also understand the user's level of interest in specific time periods from their past search history and provide information accordingly. It can also analyze the user's search patterns and provide the most relevant information. This allows the system to provide optimal information based on the user's past search history. Some or all of the above-described processes in the search history analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search history analysis unit can input the user's past search history data into a generative AI and have the generative AI perform the optimization of the user's level of interest in specific areas and time periods.

[0113] The congestion prediction system may further include a priority determination unit that estimates the user's emotions and determines the priority of the prediction results to be provided based on the estimated emotions. For example, if the user is feeling stressed, the priority determination unit may prioritize providing simple and highly visual prediction results. If the user is relaxed, it may also prioritize providing detailed prediction results. If the user is in a hurry, it may also prioritize providing the most relevant prediction results. This allows prediction results to be provided with appropriate priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the priority determination unit may be performed using a generative AI, or not using a generative AI. For example, the priority determination unit may input user emotion data into a generative AI and have the generative AI perform the priority determination of the prediction results.

[0114] The congestion prediction system may further include a movement history analysis unit that prioritizes acquiring data for highly relevant areas, taking into account the user's movement history. The movement history analysis unit may, for example, prioritize acquiring congestion information for places the user has visited in the past. It can also prioritize acquiring data for highly relevant areas from the user's movement history. It can also analyze the user's movement patterns and prioritize acquiring data for the most relevant areas. This allows for the priority acquisition of data for highly relevant areas based on the user's movement history. Some or all of the above-described processes in the movement history analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the movement history analysis unit can input the user's movement history data into a generative AI and cause the generative AI to prioritize acquiring data for highly relevant areas.

[0115] The congestion prediction system may further include an alternative adjustment unit that estimates the user's emotions and adjusts the method of presenting alternatives based on the estimated emotions. For example, if the user is stressed, the alternative adjustment unit may provide a simple and highly visible alternative. If the user is relaxed, it may also provide a more detailed alternative. If the user is in a hurry, it may also provide the most relevant alternative. This allows the system to provide alternatives in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the alternative adjustment unit may be performed using a generative AI, or not using a generative AI. For example, the alternative adjustment unit may input user emotion data into a generative AI and have the generative AI adjust the method of presenting alternatives.

[0116] The congestion prediction system may further include a social media analysis unit that analyzes users' social media activity and acquires data for relevant areas. The social media analysis unit may, for example, prioritize acquiring congestion information for locations where users have checked in on social media. It may also prioritize acquiring data for highly relevant areas based on users' social media activity. It may also analyze users' social media posts and prioritize acquiring data for the most relevant areas. This allows for the acquisition of data for highly relevant areas based on users' social media activity. Some or all of the above processing in the social media analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the social media analysis unit may input user social media data into a generative AI and have the generative AI acquire data for relevant areas.

[0117] The congestion prediction system may further include an algorithm adjustment unit that estimates the user's emotions and adjusts the prediction algorithm based on the estimated emotions. For example, if the user is feeling stressed, the algorithm adjustment unit can enhance the prediction algorithm to avoid congestion. If the user is relaxed, it can also enhance the prediction algorithm for tourist destinations and leisure facilities. If the user is in a hurry, it can also enhance the prediction algorithm for the most crowded areas. This allows the system to provide an appropriate prediction algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the algorithm adjustment unit may be performed using the generative AI, or not using the generative AI. For example, the algorithm adjustment unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the prediction algorithm.

[0118] The congestion prediction system may further include a movement history prediction unit that prioritizes providing highly relevant prediction results by considering the user's movement history. The movement history prediction unit may, for example, prioritize providing prediction results for places the user has visited in the past. It may also prioritize providing highly relevant prediction results based on the user's movement history. It may also analyze the user's movement patterns and prioritize providing the most relevant prediction results. This makes it possible to provide highly relevant prediction results based on the user's movement history. Some or all of the above processing in the movement history prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the movement history prediction unit may input the user's movement history data into a generative AI and cause the generative AI to prioritize providing highly relevant prediction results.

[0119] The congestion prediction system may further include a search information adjustment unit that estimates the user's emotions and adjusts how search information is incorporated based on the estimated emotions. For example, if the user is stressed, the search information adjustment unit may provide simple search results and avoid providing too much information. If the user is relaxed, it may provide detailed search results to enrich the information. If the user is in a hurry, it may prioritize providing the most relevant search results. This allows search information to be incorporated in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search information adjustment unit may be performed using a generative AI, or not using a generative AI. For example, the search information adjustment unit may input user emotion data into a generative AI and have the generative AI perform the adjustment of how search information is incorporated.

[0120] The congestion prediction system may further include a selection history analysis unit that analyzes the user's past selection history and optimizes alternatives for specific areas and times. The selection history analysis unit may, for example, prioritize providing alternatives for areas previously selected by the user. It may also provide alternatives for specific times based on the user's past selection history. It may also analyze the user's selection patterns and provide the most relevant alternatives. This allows the system to provide optimal alternatives based on the user's past selection history. Some or all of the above processing in the selection history analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection history analysis unit may input the user's past selection history data into a generative AI and have the generative AI perform the optimization of alternatives for specific areas and times.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The acquisition unit acquires real-time congestion information. The acquisition unit collects data such as the flow of people and their length of stay in a specific area, for example, using Agoop's location information. This allows for real-time monitoring of congestion in tourist areas, shopping malls, and other locations. It is also possible to detect the flow of people using sensors and collect data. Step 2: The prediction unit makes future congestion predictions based on the information acquired by the acquisition unit. For example, the prediction unit makes future congestion predictions based on past payment information from an electronic payment system. By analyzing past payment data, it grasps customer attraction trends at specific times and locations. It is also possible to make future congestion predictions using machine learning algorithms. Step 3: The embedded unit incorporates user search information based on the information predicted by the prediction unit. For example, the embedded unit understands the user's level of interest in specific areas or time periods based on the information the user has searched for on the app, and reflects this in the prediction. It is also possible to analyze the user's search information using natural language processing technology and reflect this in the prediction. Step 4: The provider unit provides forecast results based on the information incorporated by the embedded unit. For example, the provider unit can provide users with measures to avoid congestion. It can suggest alternatives to avoid places and times where congestion is predicted. It can also provide companies with information to support demand forecasting and enable appropriate inventory management and staffing.

[0123] 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.

[0124] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the acquisition unit, prediction unit, embedding unit, provision unit, alternative solution presentation unit, and demand forecasting support unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires real-time congestion status using the camera 42 and sensors of the smart device 14, and analyzes it using the identification processing unit 290 of the data processing device 12. The prediction unit analyzes past settlement data using the identification processing unit 290 of the data processing device 12 and makes a prediction of future congestion. The embedding unit incorporates user search information using the control unit 46A of the smart device 14 and reflects it in the prediction. The provision unit provides the prediction results to the user through the display 40A and speaker 40B of the smart device 14. The alternative solution presentation unit generates alternative solutions to avoid congestion using the identification processing unit 290 of the data processing device 12 and presents them through the output device 40 of the smart device 14. The demand forecasting support unit performs demand forecasting using the identification processing unit 290 of the data processing device 12 and provides appropriate inventory management information to companies. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the acquisition unit, prediction unit, embedding unit, provision unit, alternative solution presentation unit, and demand forecasting support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires real-time congestion status using the camera 42 and sensors of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing device 12. The prediction unit analyzes past settlement data using the identification processing unit 290 of the data processing device 12 and makes a prediction of future congestion. The embedding unit incorporates user search information using the control unit 46A of the smart glasses 214 and reflects it in the prediction. The provision unit provides the prediction results to the user through the display and speaker of the smart glasses 214. The alternative solution presentation unit generates alternative solutions to avoid congestion using the identification processing unit 290 of the data processing device 12 and presents them through the output device of the smart glasses 214. The demand forecasting support unit performs demand forecasting using the identification processing unit 290 of the data processing device 12 and provides appropriate inventory management information to companies. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the acquisition unit, forecasting unit, embedding unit, provisioning unit, alternative proposal unit, and demand forecasting support unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires real-time congestion status using the camera 42 and sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing device 12. The forecasting unit analyzes past settlement data using the specific processing unit 290 of the data processing device 12 and makes a forecast of future congestion. The embedding unit incorporates user search information using the control unit 46A of the headset terminal 314 and reflects it in the forecast. The provisioning unit provides the forecast results to the user through the display and speaker of the headset terminal 314. The alternative proposal unit generates alternatives to avoid congestion using the specific processing unit 290 of the data processing device 12 and presents them through the output device of the headset terminal 314. The demand forecasting support unit performs demand forecasting using the specific processing unit 290 of the data processing device 12 and provides appropriate inventory management information to companies. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0164] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the acquisition unit, prediction unit, embedding unit, provision unit, alternative solution presentation unit, and demand forecasting support unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires real-time congestion status using the camera 42 and sensors of the robot 414, and analyzes it using the specific processing unit 290 of the data processing unit 12. The prediction unit analyzes past settlement data using the specific processing unit 290 of the data processing unit 12 to predict future congestion. The embedding unit incorporates user search information using the control unit 46A of the robot 414 and reflects it in the prediction. The provision unit provides the prediction results to the user through the display and speaker of the robot 414. The alternative solution presentation unit generates alternative solutions to avoid congestion using the specific processing unit 290 of the data processing unit 12 and presents them through the output device of the robot 414. The demand forecasting support unit performs demand forecasting using the specific processing unit 290 of the data processing unit 12 and provides appropriate inventory management information to companies. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] 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.

[0177] Figure 9 shows the 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.

[0178] 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.

[0179] 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.

[0180] 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, and motorcycles, 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 based, for example, 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.

[0181] 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."

[0182] 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.

[0183] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] 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 other things 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.

[0193] 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 to be incorporated by reference.

[0194] (Note 1) A unit that acquires real-time congestion status, A prediction unit that predicts future congestion based on the information acquired by the acquisition unit, An embedding unit that incorporates user search information based on the information predicted by the prediction unit, The system includes a providing unit that provides prediction results based on the information incorporated by the aforementioned built-in unit. A system characterized by the following features. (Note 2) It includes an alternative proposal unit that presents alternative solutions. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a demand forecasting support unit to assist with demand forecasting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, Collect data such as the flow of people and their length of stay in a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, By analyzing past payment data, we can understand customer acquisition trends at specific times and locations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned integrated part is Based on information users search for on the app, we can understand their level of interest in specific areas and time periods and incorporate this into our predictions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring congestion information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, We analyze past congestion data and optimize data acquisition methods for specific events and seasons. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring congestion data, the data is corrected by taking into account weather information for a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of areas to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring congestion information, the system prioritizes retrieving data from highly relevant areas, taking into account the user's movement history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When obtaining congestion information, the system analyzes users' social media activity and retrieves data for relevant areas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, It estimates the user's emotions and adjusts the prediction algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, We analyze past payment data and optimize predictive models based on specific events and seasons. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, When making a forecast, the forecast results are corrected by taking into account weather information for a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, When making predictions, the system prioritizes displaying prediction results for highly relevant areas, taking into account the user's travel history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, During the prediction process, the system analyzes the user's social media activity and displays prediction results for relevant areas. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integrated part is We estimate the user's sentiment and adjust how search information is embedded based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned integrated part is Analyze users' past search history to optimize their interests in specific areas and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned integrated part is When incorporating search information, the data is adjusted to take into account weather information for a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned integrated part is It estimates the user's sentiment and determines the priority of search information to include based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned integrated part is When incorporating search results, the system prioritizes the inclusion of highly relevant search results by considering the user's browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned integrated part is When incorporating search information, analyze the user's social media activity and incorporate relevant search information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide prediction results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We analyze users' past usage history and optimize prediction results for specific areas and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing forecast results, the data is corrected to take into account weather information for a specific area. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the prediction results to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing prediction results, the system prioritizes providing highly relevant predictions by considering the user's movement history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing prediction results, we analyze the user's social media activity and provide relevant prediction results. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned alternative proposal unit, It estimates the user's emotions and adjusts how alternatives are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned alternative proposal unit, Analyze the user's past selection history and optimize alternatives for specific areas and time periods. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned alternative proposal unit, It estimates the user's emotions and determines the priority of alternatives to present based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned alternative proposal unit, When presenting alternatives, the system prioritizes showing highly relevant alternatives by considering the user's movement history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned demand forecasting support unit, We estimate user sentiment and adjust demand forecasting methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned demand forecasting support unit, We analyze historical demand data and optimize demand forecasting models for specific areas and time periods. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned demand forecasting support unit, It estimates user sentiment and prioritizes demand forecast results based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned demand forecasting support unit, When forecasting demand, the system prioritizes providing highly relevant demand forecast results by considering the user's movement history. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit that acquires real-time congestion status, A prediction unit that predicts future congestion based on the information acquired by the acquisition unit, An embedding unit that incorporates user search information based on the information predicted by the prediction unit, The system includes a providing unit that provides prediction results based on the information incorporated by the aforementioned built-in unit. A system characterized by the following features.

2. It includes an alternative proposal unit that presents alternative solutions. The system according to feature 1.

3. Equipped with a demand forecasting support unit to assist with demand forecasting. The system according to feature 1.

4. The acquisition unit is, Collect data such as the flow of people and their length of stay in a specific area. The system according to feature 1.

5. The prediction unit, By analyzing past payment data, we can understand customer acquisition trends at specific times and locations. The system according to feature 1.

6. The aforementioned integrated part is Based on information users search for on the app, we can understand their level of interest in specific areas and time periods and incorporate this into our predictions. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring congestion information based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, We analyze past congestion data and optimize data acquisition methods for specific events and seasons. The system according to feature 1.

9. The acquisition unit is, When acquiring congestion data, the data is corrected by taking into account weather information for a specific area. The system according to feature 1.

10. The acquisition unit is, It estimates the user's emotions and determines the priority of areas to acquire based on the estimated user emotions. The system according to feature 1.

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