system

Abstract

The system according to this embodiment aims to predict future visitor numbers by utilizing past data, information on surrounding events, and weather information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a forecasting unit, and a provisioning unit. The collection unit collects past visitor data for the facility, information on surrounding events, and weather information. The analysis unit analyzes the data collected by the collection unit. The forecasting unit makes predictions about future visitor numbers based on the data analyzed by the analysis unit. The provisioning unit provides the user with the results predicted by the forecasting 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 in response to the user utterance.

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, there is a problem that it is difficult to effectively utilize past data, surrounding event information, and weather information when predicting the customer attraction of a facility.

[0005] The system according to the embodiment aims to predict future customer attraction by utilizing past data, surrounding event information, and weather information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a forecasting unit, and a provisioning unit. The data collection unit collects past visitor data for the facility, information on surrounding events, and weather information. The analysis unit analyzes the data collected by the data collection unit. The forecasting unit makes predictions about future visitor numbers based on the data analyzed by the analysis unit. The provisioning unit provides the user with the results predicted by the forecasting unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict future customer traffic by utilizing past data, information on nearby events, and weather 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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 visitor forecasting system according to an embodiment of the present invention is a system that collects past visitor data for a facility, information on surrounding events, and weather information, and uses a generating AI to perform dialogue and data analysis using natural language processing. The visitor forecasting system collects past visitor data for a facility, information on surrounding events, and weather information, and forecasts visitor numbers for a specified future date. In this process, the generating AI analyzes the relationship between weather, specific dates (such as holidays), and visitor numbers, and automatically acquires and analyzes information on surrounding events. It also provides a library that allows users to easily refer to past visitor data and related weather and event information. For example, the visitor forecasting system collects past visitor data for a facility, information on surrounding events, and weather information. In this process, the visitor data for each facility includes the number of visitors and sales data for the facility. The information on surrounding events includes the schedule and content of events held in the area. The weather information includes past weather data and forecast data. This data is input into the generating AI. Next, the generating AI analyzes the collected data and forecasts visitor numbers for a specified future date. The generating AI analyzes the relationship between weather, specific dates (such as holidays), and visitor numbers, and automatically acquires and analyzes information on surrounding events. For example, if past data shows a tendency for visitor numbers to increase on specific holidays, this information can be used to predict future visitor numbers. Furthermore, if large-scale events are held nearby, their impact can be considered when making visitor predictions. The system also provides a library that allows users to easily access past visitor data and related weather and event information. This library allows users to search past data and filter it based on specific criteria. For example, users can search for visitor data for a specific period or under specific weather conditions. This enables users to make more accurate visitor predictions based on past data. This system allows facility owners and event organizers to develop effective marketing strategies based on future visitor predictions. For example, if high visitor numbers are expected on a particular day, promotions can be timed accordingly. Considering the impact of weather and surrounding events improves the accuracy of visitor predictions, enabling more effective event management.This allows the visitor forecasting system to provide facility owners and event organizers with effective marketing strategies based on future visitor forecasts.

[0029] The customer attraction forecasting system according to this embodiment comprises a collection unit, an analysis unit, a forecasting unit, and a provisioning unit. The collection unit collects past customer attraction data for a facility, information on surrounding events, and weather information. The collection unit collects, for example, visitor numbers and sales data for a facility. The collection unit collects, for example, schedules and details of events held in the area. The collection unit collects, for example, past weather data and forecast data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. The analysis unit analyzes, for example, the relationship between weather or a specific date and the number of visitors. The analysis unit automatically acquires, for example, information on surrounding events. The forecasting unit makes a forecast of future customer attraction based on the data analyzed by the analysis unit. The forecasting unit makes a forecast of customer attraction based on, for example, a forecasting model and a forecasting period. The forecasting unit makes a forecast of customer attraction considering, for example, the tendency for the number of visitors to increase on a specific public holiday. The forecasting unit makes a forecast of customer attraction considering, for example, the impact of large-scale events held in the surrounding area. The provisioning unit provides the user with the results predicted by the forecasting unit. The provisioning unit provides the results, for example, using a user interface or notification method. The provisioning unit provides, for example, a library that allows the user to easily refer to past visitor data and related weather and event information. The provisioning unit provides, for example, a function to search for visitor data for a specific period or visitor data under specific weather conditions. As a result, the visitor forecasting system according to the embodiment can collect and analyze past visitor data for a facility, surrounding event information, and weather information, make future visitor forecasts, and provide them to the user.

[0030] The data collection unit collects historical visitor data for the facility, information on surrounding events, and weather information. Specifically, it acquires data from various sensors and POS systems within the facility to collect visitor numbers and sales data. This allows for the collection of detailed visitor data on a daily, weekly, and monthly basis. In addition, it automatically acquires information from local event calendars, social media, and official websites to collect schedules and details of events held in the area. Furthermore, it obtains detailed weather information using weather data provision services and the Japan Meteorological Agency's API to collect historical and forecast data. This allows the data collection unit to collect a wide range of information from diverse data sources and update it in real time. The collected data is stored on a cloud server and made accessible to the analysis and forecasting units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. For example, during specific event periods or sudden weather changes, the frequency of data collection can be increased to obtain more detailed information. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the data using statistical analysis and machine learning algorithms to analyze the relationship between weather, specific dates, and visitor numbers. For example, it combines historical weather data with visitor data to identify visitor patterns during sunny and rainy weather. It also analyzes visitor trends on specific dates, holidays, and weekends to clarify what factors influence visitor numbers. Furthermore, it automatically acquires information on surrounding events and evaluates the impact of event scale and content on visitor numbers. Based on this data, the analysis unit builds a model for predicting visitor numbers. Machine learning algorithms such as regression analysis, time series analysis, and deep learning can be used. This allows the analysis unit to quickly and accurately analyze collected data and provide the information necessary for predicting future visitor numbers. In addition, the analysis unit can also utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, based on data from the past several years, it can predict visitor trends during specific seasons or event periods and formulate future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The forecasting unit makes future visitor forecasts based on data analyzed by the analysis unit. Specifically, it makes visitor forecasts based on prediction models and forecast periods. For example, when making visitor forecasts considering the tendency for visitor numbers to increase on specific holidays, it constructs a prediction model based on past holiday data and predicts visitor numbers on future holidays. Also, when a large-scale event is held in the surrounding area, it uses a prediction model that incorporates information such as the scale, content, and location of the event in order to make visitor forecasts that take the impact of the event into account. Using these prediction models, the forecasting unit can predict future visitor numbers with high accuracy, which can be used to help with facility management and marketing strategies. Furthermore, the forecasting unit can continuously revise its prediction results based on data that is updated in real time, and respond to the latest situations. For example, if the weather forecast changes suddenly or new event information is added, the forecasting unit immediately incorporates the new data and updates the prediction results. In addition, the forecasting unit can perform more accurate risk assessments by considering the characteristics of each region and past visitor history. As a result, the forecasting unit can always provide highly accurate visitor forecasts based on the latest information and support quick and appropriate responses.

[0033] The service provider delivers the results predicted by the forecasting department to the user. Specifically, it delivers results using user interfaces and notification methods. For example, it allows users to easily check prediction results through web and mobile applications. Important prediction results and warnings can also be quickly communicated to users via push notifications and email notifications. Furthermore, it provides a library that allows users to easily refer to past visitor data and related weather and event information. This enables users to perform analysis and comparisons based on past data. The service provider also provides a function to search for visitor data for specific periods or visitor data under specific weather conditions, enabling users to quickly obtain the information they need. For example, users can search for visitor data during a specific event period and analyze visitor trends during that period. This allows the service provider to quickly provide users with appropriate information and support the development of facility operations and marketing strategies. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can improve prediction models and notification methods based on user evaluations and opinions on prediction results. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through notifications from web and mobile applications, but also through a combination of voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, supporting the optimization of facility operations and marketing strategies.

[0034] The analysis unit includes a correlation analysis unit that analyzes the relationship between weather or a specific date and the number of visitors. The analysis unit analyzes the relationship between weather and the number of visitors using, for example, a correlation coefficient. The analysis unit analyzes the relationship between a specific date and the number of visitors using, for example, regression analysis. The analysis unit analyzes the relationship between weather or a specific date and the number of visitors using, for example, a machine learning algorithm. By analyzing the relationship between weather or a specific date and the number of visitors, it becomes possible to make more accurate visitor forecasts. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input weather data and visitor data into a generative AI, and the generative AI can analyze the relationship.

[0035] The analysis unit includes an event acquisition unit that automatically acquires information on surrounding events. The analysis unit automatically acquires information on surrounding events using, for example, an API. The analysis unit automatically acquires information on surrounding events using, for example, scraping technology. The analysis unit automatically acquires information on surrounding events using, for example, a generative AI. This improves the accuracy of the attendance forecast by automatically acquiring information on surrounding events. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input information on surrounding events into a generative AI, and the generative AI can automatically acquire the event information.

[0036] The service provider provides a library that allows users to easily access past customer acquisition data and related weather and event information. For example, the service provider stores past customer acquisition data using a database and makes it searchable by users. For example, the service provider provides a function to search for customer acquisition data for a specific period or customer acquisition data under specific weather conditions. For example, the service provider displays past customer acquisition data and related weather and event information through a user interface. This allows users to easily access past customer acquisition data and related weather and event information. 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 past customer acquisition data into a generative AI, which can then analyze the data and provide it to the user.

[0037] The data collection unit collects facility visitor numbers and sales data. The data collection unit collects visitor numbers on a daily, weekly, and monthly basis, for example. The data collection unit collects sales data by product, on a daily, and monthly basis, for example. The data collection unit builds a system that automatically collects facility visitor numbers and sales data. By collecting facility visitor numbers and sales data, more detailed customer acquisition data can be obtained. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input facility visitor numbers and sales data into a generating AI, which can then analyze and collect the data.

[0038] The data collection unit collects the schedules and details of events held in the region. For example, the data collection unit collects event schedules including the date and time and duration. For example, the data collection unit collects event details including the theme and number of participants. For example, the data collection unit builds a system that automatically collects the schedules and details of events held in the region. This improves the accuracy of attendance forecasts by collecting the schedules and details of events held in the region. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the schedules and details of events held in the region into a generative AI, which can then analyze and collect the data.

[0039] The data collection unit analyzes past data collection history and selects the optimal collection method. For example, the data collection unit identifies the most efficient collection method from past data collection history and reflects this in future data collection. For example, the data collection unit selects the optimal collection method for specific time periods or days of the week based on past data collection history. For example, the data collection unit analyzes past data collection history to identify areas for improvement in collection methods and improve collection efficiency. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past data collection history into a generation AI, which can then select the optimal collection method.

[0040] The data collection unit filters data based on the facility's current operating status and specific events during data collection. For example, the data collection unit adjusts the type and amount of data collected according to the facility's operating status. For example, if a specific event is being held, the data collection unit prioritizes collecting data related to that event. For example, the data collection unit filters the collected data based on the facility's operating status and event information to obtain only the necessary information. This ensures that only the necessary information is obtained by filtering the data based on the facility's operating status and event information. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the data collection unit can input the facility's operating status and event information into a generation AI, which can then filter the data.

[0041] The data collection unit prioritizes collecting highly relevant data, taking into account the geographical location of the facility. For example, the data collection unit prioritizes collecting nearby event information based on the facility's geographical location. For example, the data collection unit prioritizes collecting region-specific data, taking into account the facility's geographical location. For example, the data collection unit prioritizes collecting traffic conditions and access information based on the facility's geographical location. This improves data collection efficiency by prioritizing the collection of highly relevant data while considering the facility's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the facility's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit analyzes the facility's social media activity and collects relevant data during data collection. For example, the data collection unit analyzes the facility's social media activity and collects relevant event information. For example, the data collection unit identifies factors that influence visitor numbers based on the facility's social media activity and reflects this in data collection. For example, the data collection unit analyzes the facility's social media activity and prioritizes collecting data that attracts user interest. This allows for the efficient collection of relevant data by analyzing the facility's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the facility's social media activity data into a generative AI, which can then collect relevant data.

[0043] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit optimally allocates analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0044] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. For example, the analysis unit applies an event impact analysis algorithm to event data. For example, the analysis unit applies a customer attraction forecasting algorithm to customer attraction data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0045] The analysis unit determines the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of the latest data to provide real-time information. For example, the analysis unit analyzes long-term trends based on historical data. For example, the analysis unit optimally allocates analysis resources according to the data collection timing. This allows for the rapid provision of the latest information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data collection timing into the generative AI, which can then determine the priority of analysis.

[0046] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data to quickly provide important information. For example, the analysis unit postpones the analysis of less relevant data to perform efficient analysis. For example, the analysis unit optimally allocates analysis resources according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0047] The prediction unit optimizes the prediction algorithm by referring to past prediction data during the prediction process. The prediction unit improves the accuracy of the prediction algorithm based on past prediction data, for example. The prediction unit minimizes the prediction error by referring to past prediction data, for example. The prediction unit analyzes past prediction data to identify areas for improvement in the prediction algorithm, for example. This allows the accuracy of the prediction algorithm to be improved by referring to past prediction data. Some or all of the above processes 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 past prediction data into a generative AI, which can then optimize the prediction algorithm.

[0048] The forecasting unit improves the accuracy of its forecasts based on specific events or weather conditions. For example, if a specific event is held, the forecasting unit improves its accuracy by considering its impact. For example, the forecasting unit improves its accuracy by considering the impact of weather conditions on attendance. For example, the forecasting unit optimizes its forecasting algorithm based on events or weather conditions. This makes it possible to improve the accuracy of forecasts based on specific events or weather conditions. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the forecasting unit can input specific events or weather conditions into a generative AI, which can then improve the accuracy of its forecasts.

[0049] The forecasting unit selects the optimal forecasting method when making a forecast, taking into account the facility's geographical location information. For example, the forecasting unit makes a forecast based on the facility's geographical location information, taking into account the region's specific visitor trends. For example, the forecasting unit makes a forecast that reflects nearby event information, taking into account the facility's geographical location information. For example, the forecasting unit makes a forecast that considers traffic conditions and access information, taking into account the facility's geographical location information. By selecting the optimal forecasting method while considering the facility's geographical location information, it becomes possible to make a forecast that reflects the region's specific visitor trends. Some or all of the above processing in the forecasting unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the forecasting unit can input the facility's geographical location information into a generation AI, which can then select the optimal forecasting method.

[0050] The forecasting unit analyzes the facility's social media activity and proposes forecasting methods. For example, the forecasting unit analyzes the facility's social media activity, identifies factors that influence visitor numbers, and reflects them in the forecast. For example, the forecasting unit proposes forecasting methods that will attract user interest based on the facility's social media activity. For example, the forecasting unit analyzes the facility's social media activity and proposes methods to improve the accuracy of visitor number forecasts. Thus, by analyzing the facility's social media activity, it is possible to propose methods to improve the accuracy of visitor number forecasts. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the forecasting unit can input the facility's social media activity data into a generative AI, and the generative AI can propose forecasting methods.

[0051] The service provider selects the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider prioritizes providing display methods that the user has previously preferred. For example, the service provider selects the most efficient display method based on the user's past operation history. For example, the service provider analyzes the user's past operation history, identifies areas for improvement in the display method, and incorporates them. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, which can then select the optimal display method.

[0052] The information provider customizes the information based on the user's areas of interest when providing it. For example, the provider prioritizes displaying information related to the user's areas of interest. For example, the provider adjusts the display order of information based on the user's areas of interest. For example, the provider adjusts the level of detail of information according to the user's areas of interest. By customizing the information based on the user's areas of interest, the provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's areas of interest into a generative AI, which can then customize the information.

[0053] The service provider selects the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. By selecting the optimal display method considering the user's device information, the service provider can provide a display method that is easy for the user to see. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0054] The service provider analyzes the user's social media activity and adjusts how the information is displayed at the time of delivery. For example, the service provider analyzes the user's social media activity and prioritizes the display of relevant information. For example, the service provider adjusts the display order of information based on the user's social media activity. For example, the service provider analyzes the user's social media activity and adjusts the level of detail of the information. This allows the service provider to prioritize the display of highly relevant information by analyzing 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 can input the user's social media activity data into a generative AI, which can then adjust how the information is displayed.

[0055] The relevance analysis unit optimizes the analysis algorithm by referring to past data during relevance analysis. The relevance analysis unit improves the accuracy of the relevance analysis algorithm based on past data, for example. The relevance analysis unit minimizes errors in relevance analysis by referring to past data, for example. The relevance analysis unit identifies areas for improvement in the relevance analysis algorithm by analyzing past data, for example. This allows the accuracy of the relevance analysis algorithm to be improved by referring to past data. Some or all of the above processes in the relevance analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the relevance analysis unit can input past data into a generative AI, which can then optimize the analysis algorithm.

[0056] The relevance analysis unit weights the analysis based on the data collection period during relevance analysis. For example, the relevance analysis unit may prioritize the most recent data. For example, the relevance analysis unit may prioritize long-term trends based on historical data. For example, the relevance analysis unit may adjust the weighting of the relevance analysis according to the data collection period. This makes it possible to perform relevance analysis that prioritizes the latest information by weighting the analysis based on the data collection period. Some or all of the above processing in the relevance analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the relevance analysis unit can input the data collection period into a generative AI, and the generative AI can perform the weighting of the analysis.

[0057] The event acquisition unit selects the optimal acquisition method by referring to past event data when acquiring event information. For example, the event acquisition unit selects the optimal acquisition method based on past event data. For example, the event acquisition unit improves the accuracy of event information acquisition by referring to past event data. For example, the event acquisition unit analyzes past event data to identify areas for improvement in the acquisition method. This allows the optimal event information acquisition method to be selected by referring to past event data. Some or all of the above processing in the event acquisition unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the event acquisition unit can input past event data into a generation AI, and the generation AI can select the optimal acquisition method.

[0058] The event acquisition unit selects the optimal acquisition method when acquiring event information, taking into account the geographical location of the event. For example, the event acquisition unit prioritizes acquiring nearby event information based on the geographical location of the event. For example, the event acquisition unit acquires region-specific event information, taking into account the geographical location of the event. For example, the event acquisition unit acquires event information, taking into account traffic conditions and access information based on the geographical location of the event. By selecting the optimal acquisition method while considering the geographical location of the event, region-specific event information can be acquired efficiently. Some or all of the above processing in the event acquisition unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the event acquisition unit can input the geographical location of the event into a generation AI, which can then select the optimal acquisition method.

[0059] The library provider selects the optimal display method by referring to the user's past search history when providing the library. For example, the library provider prioritizes providing display methods that the user has preferred to use in the past. For example, the library provider selects the most efficient display method based on the user's past search history. For example, the library provider analyzes the user's past search history, identifies areas for improvement in the display method, and incorporates them. This allows the library provider to select the optimal display method by referring to the user's past search history. Some or all of the above processing in the library provider may be performed using, for example, a generation AI, or without a generation AI. For example, the library provider can input the user's past search history into a generation AI, which can then select the optimal display method.

[0060] The library provider selects the optimal display method when providing the library, taking into account the user's device information. For example, if the user is using a smartphone, the library provider provides a display method that matches the screen size. For example, if the user is using a tablet, the library provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the library provider provides a concise and highly visible display method. By selecting the optimal display method considering the user's device information, the library provider can provide a display method that is easy for the user to view. Some or all of the above processing in the library provider may be performed using, for example, a generation AI, or without a generation AI. For example, the library provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can collect not only past visitor data for the facility, information on surrounding events, and weather information, but also data on the facility's social media activity. For example, the data collection unit collects the content of posts and user reactions from the facility's official social media accounts and provides this data to the analysis unit. The analysis unit can analyze the collected social media activity data to identify factors that influence the facility's visitor numbers. For example, if a particular post receives a lot of attention, it can analyze the impact that post has on visitor numbers. The data collection unit can also filter the data based on specific hashtags and keywords when collecting social media activity data for the facility. This allows for improved accuracy in visitor forecasts by collecting and analyzing data on the facility's social media activity.

[0063] The analysis unit can select the optimal acquisition method when automatically acquiring information on surrounding events, taking into account the geographical location of the events. For example, it can prioritize acquiring information on nearby events based on the geographical location of the events. It can also acquire region-specific event information by considering the geographical location of the events. Furthermore, it can acquire event information by considering traffic conditions and access information based on the geographical location of the events. As a result, by selecting the optimal acquisition method while considering the geographical location of the events, region-specific event information can be acquired efficiently.

[0064] The data collection unit can collect facility operation data in addition to visitor numbers and sales data. For example, the data collection unit can collect facility operating hours and the status of specific events and provide this data to the analysis unit. The analysis unit can analyze the collected operation data and identify factors that influence facility attendance. For example, if there is a tendency for attendance to increase during certain operating hours, this information can be used to predict future attendance. The data collection unit can also filter the data based on specific conditions when collecting facility operation data. This allows for improved accuracy in attendance predictions by collecting and analyzing facility operation data.

[0065] The data collection unit can collect not only the schedules and details of events held in the area, but also local traffic data. For example, the data collection unit collects local traffic congestion information and public transport operating status and provides it to the analysis unit. The analysis unit can analyze the collected traffic data and identify factors that affect the number of visitors to a facility. For example, if the number of visitors tends to decrease during times when a particular traffic congestion occurs, it can use that information to predict future visitor numbers. The data collection unit can also filter the data based on specific conditions when collecting local traffic data. This allows for improved accuracy in visitor number predictions by collecting and analyzing local traffic data.

[0066] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can identify the most efficient collection method from past data collection history and reflect this in future data collection. It can also select the optimal collection method for specific time periods or days of the week based on past data collection history. Furthermore, it can analyze past data collection history to identify areas for improvement in collection methods and enhance collection efficiency. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0067] The data collection unit can filter data based on the facility's current operating status or specific events during data collection. For example, it can adjust the type and amount of data collected according to the facility's operating status. If a specific event is being held, it can also prioritize the collection of data related to that event. Furthermore, it can filter collected data based on the facility's operating status and event information to obtain only the necessary information. This allows for the acquisition of only the necessary information by filtering data based on the facility's operating status and event information.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects past visitor data for the facility, information on surrounding events, and weather information. For example, it collects data on the number of visitors and sales for the facility, schedules and details of events held in the area, and past weather data and forecast data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using statistical analysis and machine learning algorithms to analyze the relationship between weather, specific dates, and visitor numbers. It also automatically acquires information on nearby events. Step 3: The forecasting unit makes future visitor forecasts based on the data analyzed by the analysis unit. For example, it makes visitor forecasts based on the prediction model and forecast period, and makes forecasts considering trends such as an increase in visitor numbers on specific holidays and the impact of large-scale events held in the surrounding area. Step 4: The delivery unit provides the user with the results predicted by the forecasting unit. For example, it provides results using a user interface and notification methods, a library that allows users to easily refer to past customer acquisition data and related weather and event information, and a function to search for customer acquisition data for a specific period or customer acquisition data under specific weather conditions.

[0070] (Example of form 2) The visitor forecasting system according to an embodiment of the present invention is a system that collects past visitor data for a facility, information on surrounding events, and weather information, and uses a generating AI to perform dialogue and data analysis using natural language processing. The visitor forecasting system collects past visitor data for a facility, information on surrounding events, and weather information, and forecasts visitor numbers for a specified future date. In this process, the generating AI analyzes the relationship between weather, specific dates (such as holidays), and visitor numbers, and automatically acquires and analyzes information on surrounding events. It also provides a library that allows users to easily refer to past visitor data and related weather and event information. For example, the visitor forecasting system collects past visitor data for a facility, information on surrounding events, and weather information. In this process, the visitor data for each facility includes the number of visitors and sales data for the facility. The information on surrounding events includes the schedule and content of events held in the area. The weather information includes past weather data and forecast data. This data is input into the generating AI. Next, the generating AI analyzes the collected data and forecasts visitor numbers for a specified future date. The generating AI analyzes the relationship between weather, specific dates (such as holidays), and visitor numbers, and automatically acquires and analyzes information on surrounding events. For example, if past data shows a tendency for visitor numbers to increase on specific holidays, this information can be used to predict future visitor numbers. Furthermore, if large-scale events are held nearby, their impact can be considered when making visitor predictions. The system also provides a library that allows users to easily access past visitor data and related weather and event information. This library allows users to search past data and filter it based on specific criteria. For example, users can search for visitor data for a specific period or under specific weather conditions. This enables users to make more accurate visitor predictions based on past data. This system allows facility owners and event organizers to develop effective marketing strategies based on future visitor predictions. For example, if high visitor numbers are expected on a particular day, promotions can be timed accordingly. Considering the impact of weather and surrounding events improves the accuracy of visitor predictions, enabling more effective event management.This allows the visitor forecasting system to provide facility owners and event organizers with effective marketing strategies based on future visitor forecasts.

[0071] The customer attraction forecasting system according to this embodiment comprises a collection unit, an analysis unit, a forecasting unit, and a provisioning unit. The collection unit collects past customer attraction data for a facility, information on surrounding events, and weather information. The collection unit collects, for example, visitor numbers and sales data for a facility. The collection unit collects, for example, schedules and details of events held in the area. The collection unit collects, for example, past weather data and forecast data. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. The analysis unit analyzes, for example, the relationship between weather or a specific date and the number of visitors. The analysis unit automatically acquires, for example, information on surrounding events. The forecasting unit makes a forecast of future customer attraction based on the data analyzed by the analysis unit. The forecasting unit makes a forecast of customer attraction based on, for example, a forecasting model and a forecasting period. The forecasting unit makes a forecast of customer attraction considering, for example, the tendency for the number of visitors to increase on a specific public holiday. The forecasting unit makes a forecast of customer attraction considering, for example, the impact of large-scale events held in the surrounding area. The provisioning unit provides the user with the results predicted by the forecasting unit. The provisioning unit provides the results, for example, using a user interface or notification method. The provisioning unit provides, for example, a library that allows the user to easily refer to past visitor data and related weather and event information. The provisioning unit provides, for example, a function to search for visitor data for a specific period or visitor data under specific weather conditions. As a result, the visitor forecasting system according to the embodiment can collect and analyze past visitor data for a facility, surrounding event information, and weather information, make future visitor forecasts, and provide them to the user.

[0072] The data collection unit collects historical visitor data for the facility, information on surrounding events, and weather information. Specifically, it acquires data from various sensors and POS systems within the facility to collect visitor numbers and sales data. This allows for the collection of detailed visitor data on a daily, weekly, and monthly basis. In addition, it automatically acquires information from local event calendars, social media, and official websites to collect schedules and details of events held in the area. Furthermore, it obtains detailed weather information using weather data provision services and the Japan Meteorological Agency's API to collect historical and forecast data. This allows the data collection unit to collect a wide range of information from diverse data sources and update it in real time. The collected data is stored on a cloud server and made accessible to the analysis and forecasting units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. For example, during specific event periods or sudden weather changes, the frequency of data collection can be increased to obtain more detailed information. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0073] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the data using statistical analysis and machine learning algorithms to analyze the relationship between weather, specific dates, and visitor numbers. For example, it combines historical weather data with visitor data to identify visitor patterns during sunny and rainy weather. It also analyzes visitor trends on specific dates, holidays, and weekends to clarify what factors influence visitor numbers. Furthermore, it automatically acquires information on surrounding events and evaluates the impact of event scale and content on visitor numbers. Based on this data, the analysis unit builds a model for predicting visitor numbers. Machine learning algorithms such as regression analysis, time series analysis, and deep learning can be used. This allows the analysis unit to quickly and accurately analyze collected data and provide the information necessary for predicting future visitor numbers. In addition, the analysis unit can also utilize historical data and statistical information to perform long-term trend analysis and risk assessment. For example, based on data from the past several years, it can predict visitor trends during specific seasons or event periods and formulate future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0074] The forecasting unit makes future visitor forecasts based on data analyzed by the analysis unit. Specifically, it makes visitor forecasts based on prediction models and forecast periods. For example, when making visitor forecasts considering the tendency for visitor numbers to increase on specific holidays, it constructs a prediction model based on past holiday data and predicts visitor numbers on future holidays. Also, when a large-scale event is held in the surrounding area, it uses a prediction model that incorporates information such as the scale, content, and location of the event in order to make visitor forecasts that take the impact of the event into account. Using these prediction models, the forecasting unit can predict future visitor numbers with high accuracy, which can be used to help with facility management and marketing strategies. Furthermore, the forecasting unit can continuously revise its prediction results based on data that is updated in real time, and respond to the latest situations. For example, if the weather forecast changes suddenly or new event information is added, the forecasting unit immediately incorporates the new data and updates the prediction results. In addition, the forecasting unit can perform more accurate risk assessments by considering the characteristics of each region and past visitor history. As a result, the forecasting unit can always provide highly accurate visitor forecasts based on the latest information and support quick and appropriate responses.

[0075] The service provider delivers the results predicted by the forecasting department to the user. Specifically, it delivers results using user interfaces and notification methods. For example, it allows users to easily check prediction results through web and mobile applications. Important prediction results and warnings can also be quickly communicated to users via push notifications and email notifications. Furthermore, it provides a library that allows users to easily refer to past visitor data and related weather and event information. This enables users to perform analysis and comparisons based on past data. The service provider also provides a function to search for visitor data for specific periods or visitor data under specific weather conditions, enabling users to quickly obtain the information they need. For example, users can search for visitor data during a specific event period and analyze visitor trends during that period. This allows the service provider to quickly provide users with appropriate information and support the development of facility operations and marketing strategies. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can improve prediction models and notification methods based on user evaluations and opinions on prediction results. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through notifications from web and mobile applications, but also through a combination of voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, supporting the optimization of facility operations and marketing strategies.

[0076] The analysis unit includes a correlation analysis unit that analyzes the relationship between weather or a specific date and the number of visitors. The analysis unit analyzes the relationship between weather and the number of visitors using, for example, a correlation coefficient. The analysis unit analyzes the relationship between a specific date and the number of visitors using, for example, regression analysis. The analysis unit analyzes the relationship between weather or a specific date and the number of visitors using, for example, a machine learning algorithm. By analyzing the relationship between weather or a specific date and the number of visitors, it becomes possible to make more accurate visitor forecasts. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input weather data and visitor data into a generative AI, and the generative AI can analyze the relationship.

[0077] The analysis unit includes an event acquisition unit that automatically acquires information on surrounding events. The analysis unit automatically acquires information on surrounding events using, for example, an API. The analysis unit automatically acquires information on surrounding events using, for example, scraping technology. The analysis unit automatically acquires information on surrounding events using, for example, a generative AI. This improves the accuracy of the attendance forecast by automatically acquiring information on surrounding events. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input information on surrounding events into a generative AI, and the generative AI can automatically acquire the event information.

[0078] The service provider provides a library that allows users to easily access past customer acquisition data and related weather and event information. For example, the service provider stores past customer acquisition data using a database and makes it searchable by users. For example, the service provider provides a function to search for customer acquisition data for a specific period or customer acquisition data under specific weather conditions. For example, the service provider displays past customer acquisition data and related weather and event information through a user interface. This allows users to easily access past customer acquisition data and related weather and event information. 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 past customer acquisition data into a generative AI, which can then analyze the data and provide it to the user.

[0079] The data collection unit collects facility visitor numbers and sales data. The data collection unit collects visitor numbers on a daily, weekly, and monthly basis, for example. The data collection unit collects sales data by product, on a daily, and monthly basis, for example. The data collection unit builds a system that automatically collects facility visitor numbers and sales data. By collecting facility visitor numbers and sales data, more detailed customer acquisition data can be obtained. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input facility visitor numbers and sales data into a generating AI, which can then analyze and collect the data.

[0080] The data collection unit collects the schedules and details of events held in the region. For example, the data collection unit collects event schedules including the date and time and duration. For example, the data collection unit collects event details including the theme and number of participants. For example, the data collection unit builds a system that automatically collects the schedules and details of events held in the region. This improves the accuracy of attendance forecasts by collecting the schedules and details of events held in the region. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the schedules and details of events held in the region into a generative AI, which can then analyze and collect the data.

[0081] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit collects more detailed data to obtain more information. For example, if the user is in a hurry, the data collection unit collects only the minimum necessary data to provide information quickly. This reduces the user's burden by adjusting the timing of data collection based on 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 data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of data collection.

[0082] The data collection unit analyzes past data collection history and selects the optimal collection method. For example, the data collection unit identifies the most efficient collection method from past data collection history and reflects this in future data collection. For example, the data collection unit selects the optimal collection method for specific time periods or days of the week based on past data collection history. For example, the data collection unit analyzes past data collection history to identify areas for improvement in collection methods and improve collection efficiency. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using, for example, a generation AI, or without a generation AI. For example, the data collection unit can input past data collection history into a generation AI, which can then select the optimal collection method.

[0083] The data collection unit filters data based on the facility's current operating status and specific events during data collection. For example, the data collection unit adjusts the type and amount of data collected according to the facility's operating status. For example, if a specific event is being held, the data collection unit prioritizes collecting data related to that event. For example, the data collection unit filters the collected data based on the facility's operating status and event information to obtain only the necessary information. This ensures that only the necessary information is obtained by filtering the data based on the facility's operating status and event information. Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the data collection unit can input the facility's operating status and event information into a generation AI, which can then filter the data.

[0084] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. For example, if the user is relaxed, the data collection unit prioritizes collecting detailed data. For example, if the user is in a hurry, the data collection unit prioritizes collecting data that can be collected quickly. This ensures that important data is collected preferentially by prioritizing the data to be collected based on 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 data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the data.

[0085] The data collection unit prioritizes collecting highly relevant data, taking into account the geographical location of the facility. For example, the data collection unit prioritizes collecting nearby event information based on the facility's geographical location. For example, the data collection unit prioritizes collecting region-specific data, taking into account the facility's geographical location. For example, the data collection unit prioritizes collecting traffic conditions and access information based on the facility's geographical location. This improves data collection efficiency by prioritizing the collection of highly relevant data while considering the facility's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the facility's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0086] The data collection unit analyzes the facility's social media activity and collects relevant data during data collection. For example, the data collection unit analyzes the facility's social media activity and collects relevant event information. For example, the data collection unit identifies factors that influence visitor numbers based on the facility's social media activity and reflects this in data collection. For example, the data collection unit analyzes the facility's social media activity and prioritizes collecting data that attracts user interest. This allows for the efficient collection of relevant data by analyzing the facility's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the facility's social media activity data into a generative AI, which can then collect relevant data.

[0087] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. By adjusting the presentation of the analysis based on the user's emotions, the analysis unit can provide results that are easy for the user to understand. 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 analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0088] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit optimally allocates analysis resources according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0089] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. For example, the analysis unit applies an event impact analysis algorithm to event data. For example, the analysis unit applies a customer attraction forecasting algorithm to customer attraction data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0090] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is excited, the analysis unit provides a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as 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 processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0091] The analysis unit determines the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of the latest data to provide real-time information. For example, the analysis unit analyzes long-term trends based on historical data. For example, the analysis unit optimally allocates analysis resources according to the data collection timing. This allows for the rapid provision of the latest information by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data collection timing into the generative AI, which can then determine the priority of analysis.

[0092] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data to quickly provide important information. For example, the analysis unit postpones the analysis of less relevant data to perform efficient analysis. For example, the analysis unit optimally allocates analysis resources according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0093] The prediction unit estimates the user's emotions and adjusts its prediction method based on the estimated emotions. For example, if the user is relaxed, the prediction unit provides a detailed prediction result. For example, if the user is in a hurry, the prediction unit provides a concise prediction result. For example, if the user is excited, the prediction unit provides a visually stimulating prediction result. By adjusting the prediction method based on the user's emotions, the system can provide the user with the most optimal prediction result. 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 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, which can then estimate the emotions and adjust its prediction method.

[0094] The prediction unit optimizes the prediction algorithm by referring to past prediction data during the prediction process. The prediction unit improves the accuracy of the prediction algorithm based on past prediction data, for example. The prediction unit minimizes the prediction error by referring to past prediction data, for example. The prediction unit analyzes past prediction data to identify areas for improvement in the prediction algorithm, for example. This allows the accuracy of the prediction algorithm to be improved by referring to past prediction data. Some or all of the above processes 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 past prediction data into a generative AI, which can then optimize the prediction algorithm.

[0095] The forecasting unit improves the accuracy of its forecasts based on specific events or weather conditions. For example, if a specific event is held, the forecasting unit improves its accuracy by considering its impact. For example, the forecasting unit improves its accuracy by considering the impact of weather conditions on attendance. For example, the forecasting unit optimizes its forecasting algorithm based on events or weather conditions. This makes it possible to improve the accuracy of forecasts based on specific events or weather conditions. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the forecasting unit can input specific events or weather conditions into a generative AI, which can then improve the accuracy of its forecasts.

[0096] The prediction unit estimates the user's emotions and determines the priority of predictions based on the estimated emotions. For example, if the user is stressed, the prediction unit prioritizes providing important prediction results. For example, if the user is relaxed, the prediction unit prioritizes providing detailed prediction results. For example, if the user is in a hurry, the prediction unit prioritizes providing prediction results that can be delivered quickly. This ensures that important prediction results are prioritized by determining the priority of predictions based on 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 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, which can estimate the emotions and determine the priority of predictions.

[0097] The forecasting unit selects the optimal forecasting method when making a forecast, taking into account the facility's geographical location information. For example, the forecasting unit makes a forecast based on the facility's geographical location information, taking into account the region's specific visitor trends. For example, the forecasting unit makes a forecast that reflects nearby event information, taking into account the facility's geographical location information. For example, the forecasting unit makes a forecast that considers traffic conditions and access information, taking into account the facility's geographical location information. By selecting the optimal forecasting method while considering the facility's geographical location information, it becomes possible to make a forecast that reflects the region's specific visitor trends. Some or all of the above processing in the forecasting unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the forecasting unit can input the facility's geographical location information into a generation AI, which can then select the optimal forecasting method.

[0098] The forecasting unit analyzes the facility's social media activity and proposes forecasting methods. For example, the forecasting unit analyzes the facility's social media activity, identifies factors that influence visitor numbers, and reflects them in the forecast. For example, the forecasting unit proposes forecasting methods that will attract user interest based on the facility's social media activity. For example, the forecasting unit analyzes the facility's social media activity and proposes methods to improve the accuracy of visitor number forecasts. Thus, by analyzing the facility's social media activity, it is possible to propose methods to improve the accuracy of visitor number forecasts. Some or all of the above processing in the forecasting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the forecasting unit can input the facility's social media activity data into a generative AI, and the generative AI can propose forecasting methods.

[0099] The service provider estimates the user's emotions and adjusts the way information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider provides a simple and highly visible display method. For example, if the user is relaxed, the service provider provides a display method that includes detailed information. For example, if the user is in a hurry, the service provider provides a display method that gets straight to the point. By adjusting the way information is displayed based on the user's emotions, the service provider can provide the optimal display method for the user. 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 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, which can estimate the emotions and adjust the way information is displayed.

[0100] The service provider selects the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider prioritizes providing display methods that the user has previously preferred. For example, the service provider selects the most efficient display method based on the user's past operation history. For example, the service provider analyzes the user's past operation history, identifies areas for improvement in the display method, and incorporates them. This allows the service provider to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, which can then select the optimal display method.

[0101] The information provider customizes the information based on the user's areas of interest when providing it. For example, the provider prioritizes displaying information related to the user's areas of interest. For example, the provider adjusts the display order of information based on the user's areas of interest. For example, the provider adjusts the level of detail of information according to the user's areas of interest. By customizing the information based on the user's areas of interest, the provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's areas of interest into a generative AI, which can then customize the information.

[0102] The information provider estimates the user's emotions and prioritizes the information to be provided based on the estimated emotions. For example, if the user is stressed, the provider prioritizes providing only important information. For example, if the user is relaxed, the provider prioritizes providing detailed information. For example, if the user is in a hurry, the provider prioritizes providing information that can be delivered quickly. In this way, important information can be prioritized by prioritizing information based on 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 information provider may be performed using a generative AI, or not using a generative AI. For example, the information provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of information.

[0103] The service provider selects the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. By selecting the optimal display method considering the user's device information, the service provider can provide a display method that is easy for the user to see. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0104] The service provider analyzes the user's social media activity and adjusts how the information is displayed at the time of delivery. For example, the service provider analyzes the user's social media activity and prioritizes the display of relevant information. For example, the service provider adjusts the display order of information based on the user's social media activity. For example, the service provider analyzes the user's social media activity and adjusts the level of detail of the information. This allows the service provider to prioritize the display of highly relevant information by analyzing 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 can input the user's social media activity data into a generative AI, which can then adjust how the information is displayed.

[0105] The relevance analysis unit estimates the user's emotions and adjusts the relevance analysis method based on the estimated user emotions. For example, if the user is nervous, the relevance analysis unit provides simple and easy-to-understand relevance analysis results. For example, if the user is relaxed, the relevance analysis unit provides detailed relevance analysis results. For example, if the user is in a hurry, the relevance analysis unit provides concise and to-the-point relevance analysis results. In this way, by adjusting the relevance analysis method based on the user's emotions, it is possible to provide relevance analysis results that are easy for the user to understand. 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 relevance analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the relevance analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the relevance analysis method.

[0106] The relevance analysis unit optimizes the analysis algorithm by referring to past data during relevance analysis. The relevance analysis unit improves the accuracy of the relevance analysis algorithm based on past data, for example. The relevance analysis unit minimizes errors in relevance analysis by referring to past data, for example. The relevance analysis unit identifies areas for improvement in the relevance analysis algorithm by analyzing past data, for example. This allows the accuracy of the relevance analysis algorithm to be improved by referring to past data. Some or all of the above processes in the relevance analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the relevance analysis unit can input past data into a generative AI, which can then optimize the analysis algorithm.

[0107] The relevance analysis unit estimates the user's emotions and determines the priority of relevances based on the estimated emotions. For example, if the user is stressed, the relevance analysis unit prioritizes providing only important relevances. For example, if the user is relaxed, the relevance analysis unit prioritizes providing detailed relevances. For example, if the user is in a hurry, the relevance analysis unit prioritizes providing relevances that can be delivered quickly. This allows for the priority of important relevances by determining the priority of relevances based on 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 relevance analysis unit may be performed using a generative AI, or not using a generative AI. For example, the relevance analysis unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of relevances.

[0108] The relevance analysis unit weights the analysis based on the data collection period during relevance analysis. For example, the relevance analysis unit may prioritize the most recent data. For example, the relevance analysis unit may prioritize long-term trends based on historical data. For example, the relevance analysis unit may adjust the weighting of the relevance analysis according to the data collection period. This makes it possible to perform relevance analysis that prioritizes the latest information by weighting the analysis based on the data collection period. Some or all of the above processing in the relevance analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the relevance analysis unit can input the data collection period into a generative AI, and the generative AI can perform the weighting of the analysis.

[0109] The event acquisition unit estimates the user's emotions and adjusts the method of acquiring event information based on the estimated emotions. For example, if the user is nervous, the event acquisition unit provides simple and highly visible event information. For example, if the user is relaxed, the event acquisition unit provides detailed event information. For example, if the user is in a hurry, the event acquisition unit provides concise event information that gets straight to the point. In this way, by adjusting the method of acquiring event information based on the user's emotions, the system can provide the user with the most suitable event information. 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 event acquisition unit may be performed using a generative AI, or not using a generative AI. For example, the event acquisition unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the method of acquiring event information.

[0110] The event acquisition unit selects the optimal acquisition method by referring to past event data when acquiring event information. For example, the event acquisition unit selects the optimal acquisition method based on past event data. For example, the event acquisition unit improves the accuracy of event information acquisition by referring to past event data. For example, the event acquisition unit analyzes past event data to identify areas for improvement in the acquisition method. This allows the optimal event information acquisition method to be selected by referring to past event data. Some or all of the above processing in the event acquisition unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the event acquisition unit can input past event data into a generation AI, and the generation AI can select the optimal acquisition method.

[0111] The event acquisition unit estimates the user's emotions and prioritizes event information based on the estimated emotions. For example, if the user is stressed, the event acquisition unit prioritizes providing only important event information. For example, if the user is relaxed, the event acquisition unit prioritizes providing detailed event information. For example, if the user is in a hurry, the event acquisition unit prioritizes providing event information that can be provided quickly. In this way, important event information can be prioritized by prioritizing event information based on 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 event acquisition unit may be performed using a generative AI, or not using a generative AI. For example, the event acquisition unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of event information.

[0112] The event acquisition unit selects the optimal acquisition method when acquiring event information, taking into account the geographical location of the event. For example, the event acquisition unit prioritizes acquiring nearby event information based on the geographical location of the event. For example, the event acquisition unit acquires region-specific event information, taking into account the geographical location of the event. For example, the event acquisition unit acquires event information, taking into account traffic conditions and access information based on the geographical location of the event. By selecting the optimal acquisition method while considering the geographical location of the event, region-specific event information can be acquired efficiently. Some or all of the above processing in the event acquisition unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the event acquisition unit can input the geographical location of the event into a generation AI, which can then select the optimal acquisition method.

[0113] The library provider estimates the user's emotions and adjusts the library display method based on the estimated emotions. For example, if the user is nervous, the library provider provides a simple and highly visible library display method. For example, if the user is relaxed, the library provider provides a detailed library display method. For example, if the user is in a hurry, the library provider provides a concise library display method. In this way, by adjusting the library display method based on the user's emotions, the optimal display method can be provided to the user. 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 library provider may be performed using a generative AI, or not using a generative AI. For example, the library provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the library display method.

[0114] The library provider selects the optimal display method by referring to the user's past search history when providing the library. For example, the library provider prioritizes providing display methods that the user has preferred to use in the past. For example, the library provider selects the most efficient display method based on the user's past search history. For example, the library provider analyzes the user's past search history, identifies areas for improvement in the display method, and incorporates them. This allows the library provider to select the optimal display method by referring to the user's past search history. Some or all of the above processing in the library provider may be performed using, for example, a generation AI, or without a generation AI. For example, the library provider can input the user's past search history into a generation AI, which can then select the optimal display method.

[0115] The library provider estimates the user's emotions and determines the priority of the library based on the estimated emotions. For example, if the user is stressed, the library provider will prioritize providing only important information. For example, if the user is relaxed, the library provider will prioritize providing detailed information. For example, if the user is in a hurry, the library provider will prioritize providing information that can be delivered quickly. In this way, by determining the priority of the library based on the user's emotions, important information can be provided preferentially. 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 library provider may be performed using a generative AI, or not using a generative AI. For example, the library provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the library.

[0116] The library provider selects the optimal display method when providing the library, taking into account the user's device information. For example, if the user is using a smartphone, the library provider provides a display method that matches the screen size. For example, if the user is using a tablet, the library provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the library provider provides a concise and highly visible display method. By selecting the optimal display method considering the user's device information, the library provider can provide a display method that is easy for the user to view. Some or all of the above processing in the library provider may be performed using, for example, a generation AI, or without a generation AI. For example, the library provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The data collection unit can collect not only past visitor data for the facility, information on surrounding events, and weather information, but also data on the facility's social media activity. For example, the data collection unit collects the content of posts and user reactions from the facility's official social media accounts and provides this data to the analysis unit. The analysis unit can analyze the collected social media activity data to identify factors that influence the facility's visitor numbers. For example, if a particular post receives a lot of attention, it can analyze the impact that post has on visitor numbers. The data collection unit can also filter the data based on specific hashtags and keywords when collecting social media activity data for the facility. This allows for improved accuracy in visitor forecasts by collecting and analyzing data on the facility's social media activity.

[0119] The analysis unit can estimate user emotions when analyzing the relationship between weather, specific dates, and visitor numbers, and adjust the level of detail of the analysis based on the estimated emotions. For example, if a user is relaxed, it provides detailed analysis results. If a user is in a hurry, it provides concise analysis results that get straight to the point. It can also provide visually stimulating analysis results if a user is excited. In this way, by adjusting the level of detail of the analysis based on user emotions, it is possible to provide analysis results that are easy for users to understand.

[0120] The analysis unit can select the optimal acquisition method when automatically acquiring information on surrounding events, taking into account the geographical location of the events. For example, it can prioritize acquiring information on nearby events based on the geographical location of the events. It can also acquire region-specific event information by considering the geographical location of the events. Furthermore, it can acquire event information by considering traffic conditions and access information based on the geographical location of the events. As a result, by selecting the optimal acquisition method while considering the geographical location of the events, region-specific event information can be acquired efficiently.

[0121] The information provider can estimate the user's emotions and adjust how the information is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can also be provided. In this way, by adjusting how information is displayed based on the user's emotions, the optimal display method can be provided to the user.

[0122] The data collection unit can collect facility operation data in addition to visitor numbers and sales data. For example, the data collection unit can collect facility operating hours and the status of specific events and provide this data to the analysis unit. The analysis unit can analyze the collected operation data and identify factors that influence facility attendance. For example, if there is a tendency for attendance to increase during certain operating hours, this information can be used to predict future attendance. The data collection unit can also filter the data based on specific conditions when collecting facility operation data. This allows for improved accuracy in attendance predictions by collecting and analyzing facility operation data.

[0123] The data collection unit can collect not only the schedules and details of events held in the area, but also local traffic data. For example, the data collection unit collects local traffic congestion information and public transport operating status and provides it to the analysis unit. The analysis unit can analyze the collected traffic data and identify factors that affect the number of visitors to a facility. For example, if the number of visitors tends to decrease during times when a particular traffic congestion occurs, it can use that information to predict future visitor numbers. The data collection unit can also filter the data based on specific conditions when collecting local traffic data. This allows for improved accuracy in visitor number predictions by collecting and analyzing local traffic data.

[0124] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, more detailed data collection can be performed to obtain more information. If the user is in a hurry, only the minimum necessary data can be collected to provide information quickly. In this way, the user's burden can be reduced by adjusting the timing of data collection based on their emotions.

[0125] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can identify the most efficient collection method from past data collection history and reflect this in future data collection. It can also select the optimal collection method for specific time periods or days of the week based on past data collection history. Furthermore, it can analyze past data collection history to identify areas for improvement in collection methods and enhance collection efficiency. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0126] The data collection unit can filter data based on the facility's current operating status or specific events during data collection. For example, it can adjust the type and amount of data collected according to the facility's operating status. If a specific event is being held, it can also prioritize the collection of data related to that event. Furthermore, it can filter collected data based on the facility's operating status and event information to obtain only the necessary information. This allows for the acquisition of only the necessary information by filtering data based on the facility's operating status and event information.

[0127] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, it will prioritize collecting only important data. If the user is relaxed, it will prioritize collecting detailed data. If the user is in a hurry, it can also prioritize collecting data that can be retrieved quickly. This allows for the priority collection of important data by prioritizing the data to be collected based on the user's emotions.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The data collection unit collects past visitor data for the facility, information on surrounding events, and weather information. For example, it collects data on the number of visitors and sales for the facility, schedules and details of events held in the area, and past weather data and forecast data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using statistical analysis and machine learning algorithms to analyze the relationship between weather, specific dates, and visitor numbers. It also automatically acquires information on nearby events. Step 3: The forecasting unit makes future visitor forecasts based on the data analyzed by the analysis unit. For example, it makes visitor forecasts based on the forecasting model and forecasting period, and makes forecasts considering trends such as an increase in visitor numbers on specific holidays and the impact of large-scale events held in the surrounding area. Step 4: The delivery unit provides the user with the results predicted by the forecasting unit. For example, it provides results using a user interface and notification methods, a library that allows users to easily refer to past customer acquisition data and related weather and event information, and a function to search for customer acquisition data for a specific period or customer acquisition data under specific weather conditions.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, and provisioning unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the smart device 14 to collect past visitor data, surrounding event information, and weather information for the facility. The analysis unit is implemented in the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The forecasting unit is implemented in the specific processing unit 290 of the data processing device 12 and makes a forecast of future visitor numbers based on the analyzed data. The provisioning unit is implemented in the control unit 46A of the smart device 14 and provides the forecast results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, and provisioning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the smart glasses 214 to collect past visitor data for the facility, information on surrounding events, and weather information. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The forecasting unit is implemented, for example, in the specific processing unit 290 of the data processing device 12, and makes a forecast of future visitor numbers based on the analyzed data. The provisioning unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides the forecast results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, and provisioning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the headset terminal 314 to collect past visitor data for the facility, information on surrounding events, and weather information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The forecasting unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes a forecast of future visitor numbers based on the analyzed data. The provisioning unit is implemented in the control unit 46A of the headset terminal 314 and provides the forecast results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the collection unit, analysis unit, forecasting unit, and provisioning unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the robot 414 to collect past visitor data for the facility, information on surrounding events, and weather information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The forecasting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and makes a forecast of future visitor numbers based on the analyzed data. The provisioning unit is implemented, for example, by the control unit 46A of the robot 414, and provides the forecast results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) The facility has a data collection department that collects past visitor data, information on nearby events, and weather information. An analysis unit analyzes the data collected by the aforementioned collection unit, A forecasting unit that predicts future customer traffic based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the results predicted by the prediction unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a correlation analysis unit that analyzes the relationship between weather, specific dates, and the number of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It is equipped with an event acquisition unit that automatically acquires information on nearby events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, This library allows users to easily access past customer acquisition data and related weather and event information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect facility visitor numbers and sales data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect the schedules and details of events held in the local area. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the facility's current operating status and specific events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the geographical location of the facility is taken into consideration, and the collection of highly relevant data is prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze the facility's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned prediction unit, It estimates the user's emotions and adjusts the prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned prediction unit, When making predictions, the prediction algorithm is optimized by referring to past prediction data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned prediction unit, When making forecasts, improve the accuracy of the forecast based on specific events and weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned prediction unit, When making predictions, the optimal prediction method is selected by considering the geographical location information of the facility. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned prediction unit, When making predictions, we analyze the facility's social media activity and propose methods for making predictions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, we customize it based on the user's areas of interest. 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 prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust how the information is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned relationship analysis unit, We estimate the user's emotions and adjust the relevance analysis method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned relationship analysis unit, When performing relevance analysis, we optimize the analysis algorithm by referring to historical data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned relationship analysis unit, It estimates the user's emotions and determines the priority of relevance based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned relationship analysis unit, When performing correlation analysis, weight the analysis based on the data collection period. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned event acquisition unit, It estimates the user's emotions and adjusts how event information is retrieved based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned event acquisition unit, When retrieving event information, the system selects the optimal retrieval method by referring to past event data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned event acquisition unit, It estimates user sentiment and prioritizes event information based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned event acquisition unit, When acquiring event information, the system selects the most suitable acquisition method, taking into account the event's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned library provision unit, It estimates the user's emotions and adjusts how the library is displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned library provision unit, When providing the library, the system selects the optimal display method by referring to the user's past search history. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned library provision unit, It estimates the user's emotions and determines the priority of the library based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned library provision unit, When providing the library, the optimal display method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0202] 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. The facility has a data collection department that collects past visitor data, information on nearby events, and weather information. An analysis unit analyzes the data collected by the aforementioned collection unit, A forecasting unit that predicts future customer traffic based on the data analyzed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the results predicted by the prediction unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a correlation analysis unit that analyzes the relationship between weather, specific dates, and the number of visitors. The system according to feature 1.

3. The aforementioned analysis unit, It is equipped with an event acquisition unit that automatically acquires information on nearby events. The system according to feature 1.

4. The aforementioned supply unit is, This library allows users to easily access past customer acquisition data and related weather and event information. The system according to feature 1.

5. The aforementioned collection unit is Collect facility visitor numbers and sales data. The system according to feature 1.

6. The aforementioned collection unit is Collect the schedules and details of events held in the local area. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

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