system

Abstract

The system according to this embodiment aims to efficiently manage the availability of seats in a cafe and optimize the turnover rate. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an adjustment unit, and a provision unit. The collection unit collects information on seat availability. The analysis unit analyzes the data collected by the collection unit. The adjustment unit adjusts the usage time based on the analysis results obtained by the analysis unit. The provision unit provides incentives based on the usage time adjusted by the adjustment unit.

Description

Technical Field

[0004] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 prior art, there is a problem that it is difficult to efficiently manage the vacancy situation of a café and optimize the turnover rate.

[0005] The system according to the embodiment aims to efficiently manage the vacancy situation of a café and optimize the turnover rate.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an adjustment unit, and a provision unit. The collection unit collects the vacancy situation. The analysis unit analyzes the data collected by the collection unit. The adjustment unit adjusts the usage time based on the analysis result obtained by the analysis unit. The provision unit provides an incentive based on the usage time adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the availability of seats in a cafe and optimize the turnover rate. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI ​​assistant system for providing a cafe environment where users can always find a seat and for cafe owners to optimize turnover, according to an embodiment of the present invention, is a system for providing a cafe environment where users can always find a seat and for cafe owners to optimize turnover. This system allows users to check seat availability in real time via a smartphone app and to reserve seats or check in. The generating AI analyzes past data and real-time usage data to predict congestion levels. Based on this, it dynamically adjusts the maximum available time for seats and provides an incentive system to increase turnover. For example, users can earn stamps according to their usage time, and receive rewards when a certain number of stamps are accumulated. In addition, users who leave early during busy times will receive additional stamps or rewards. Furthermore, there is also an option to be seated preferentially by paying an additional fee, differentiating the system from co-working spaces. First, users check seat availability in real time via a smartphone app. For example, when the app is opened, the current seat availability is displayed, and users can check which seats are available. Next, users can reserve seats or check in. For example, they can reserve seats in advance, or they can reserve available seats instantly in real time. Next, the generating AI analyzes past data and real-time usage data to predict congestion levels. For example, based on past usage data, it is possible to predict congestion levels during specific time periods and combine this with real-time data to understand the current congestion level. Based on this, the maximum available time for seats is dynamically adjusted. For example, the available time is automatically shortened during peak hours and extended during off-peak hours. Furthermore, an incentive system is provided to increase turnover. For example, stamps are awarded based on usage time, with stamps given for usage of less than 30 minutes, and rewards are offered once a certain number of stamps are collected. Additional stamps and rewards are also given for leaving early during peak hours. This encourages users to use the facilities for shorter periods, improving turnover. A priority seating system is also available, allowing users to pay an additional fee to secure a seat. For example, users can pay an additional fee to secure a seat preferentially.This allows users to secure a seat even during peak hours, and enables cafe owners to increase their revenue. Thus, the present invention provides an environment where cafe users can always find a seat and offers an AI assistant to help cafe owners optimize their turnover rate. This improves customer satisfaction and enhances the operational efficiency of cafe owners. This allows cafe users to always find a seat and enables cafe owners to optimize their turnover rate.

[0029] The AI ​​assistant system according to this embodiment, which provides a cafe environment where users can always find a seat and allows cafe owners to optimize turnover, comprises a data collection unit, an analysis unit, an adjustment unit, and a service unit. The data collection unit collects information on available seats. The data collection unit can collect information on available seats, for example, through a smartphone application. The data collection unit can also grasp the availability of seats in real time, for example, using sensors within the cafe. The data collection unit can also collect information on available seats, for example, based on user check-in information. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can predict congestion levels, for example, by analyzing historical data and real-time usage data. The analysis unit can also predict congestion levels, for example, by analyzing data using generative AI. The analysis unit can also predict congestion levels, for example, by analyzing data using data mining technology. The adjustment unit adjusts the usage time based on the analysis results obtained by the analysis unit. The adjustment unit can, for example, automatically shorten the available time when it is crowded and set a longer usage time when it is not crowded. The adjustment unit can also adjust the usage time based on user behavior data, for example. The adjustment unit can, for example, adjust usage time using a generating AI. The provision unit provides incentives based on the usage time adjusted by the adjustment unit. The provision unit can, for example, award stamps according to usage time, and provide rewards when a certain number are accumulated. The provision unit can, for example, offer additional stamps or rewards for leaving early during busy times. The provision unit can, for example, offer a queue-queueing system where customers can be seated preferentially by paying an additional fee. In this way, the AI ​​assistant system for providing an environment where cafe users can always find a seat and for cafe owners to optimize their turnover rate can provide an environment where cafe users can always find a seat and for cafe owners to optimize their turnover rate.

[0030] The data collection unit collects information on seat availability. For example, the data collection unit can collect seat availability through a smartphone application. Specifically, when a user arrives at the cafe, they launch the app and check in, updating the current seat availability in real time. The app obtains the user's location information and compares it with the seating arrangement in the cafe to accurately determine which seats are available. The system can also use sensors within the cafe to determine seat availability in real time. For example, pressure sensors or infrared sensors installed on each table detect seat occupancy and transmit the information to a central database. This allows the system to always maintain the most up-to-date seat availability information. Furthermore, seat availability can also be collected based on user check-in information. When a user enters the cafe, they check in by using the smartphone app or scanning a QR code (registered trademark), and this information is transmitted to the data collection unit. This allows the data collection unit to accurately determine seat availability in various ways and update it in real time. The collected data is stored on a cloud server, making it accessible to the analysis and adjustment units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze historical data and real-time usage data to predict congestion levels. Specifically, it analyzes congestion patterns on specific days of the week and time slots based on historical usage data to predict future congestion levels. Furthermore, it can also analyze data using generative AI to predict congestion levels. The generative AI takes collected data as input and simulates multiple scenarios to identify the most likely congestion level. For example, it can take external factors such as weather and event information into consideration to make more accurate predictions. It can also analyze data using data mining techniques to predict congestion levels. Data mining techniques extract useful patterns and trends from large amounts of data to identify the causes and impacts of congestion. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. 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, improving the reliability and safety of the entire system.

[0032] The adjustment unit adjusts the usage time based on the analysis results obtained by the analysis unit. Specifically, it can automatically shorten the available time during peak hours and set a longer usage time when the cafe is not busy. For example, if the cafe is crowded, it can send a notification to the user encouraging them to shorten their usage time. Conversely, if the cafe is not busy, it can offer the user a longer usage time so they can relax. It can also adjust the usage time based on user behavior data. For example, it can analyze past usage history and current behavior patterns to suggest the optimal usage time for each individual user. It can also adjust the usage time using generative AI. The generative AI calculates the optimal usage time based on the collected data and suggests it to the user. This allows the adjustment unit to adjust the usage time efficiently and flexibly, optimizing the cafe's turnover rate. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy and effectiveness of the adjustments. This allows the adjustment unit to always provide highly accurate adjustments based on the latest information, improving the operational efficiency of the cafe.

[0033] The service department provides incentives based on usage time adjusted by the adjustment department. Specifically, users can earn stamps according to their usage time, and receive rewards once a certain number of stamps are collected. For example, users can earn stamps each time they use the cafe, and once a certain number are collected, they can receive free drinks or discount coupons. Additional stamps or rewards can also be offered for leaving early during busy times. This provides users with an incentive to vacate their seats early during busy periods, improving the cafe's turnover rate. A priority seating system can also be offered for an additional fee. For example, users can pay an additional fee to secure a seat even during busy times. This allows the service department to offer diverse incentives to users and promote cafe use. Furthermore, the service department can collect user feedback and continuously improve the accuracy and effectiveness of the incentives. This allows the service department to always provide highly accurate incentives based on the latest information, improving the operational efficiency of the cafe.

[0034] The queue management system implements a queue management mechanism. For example, the queue management system allows users to secure seats preferentially by paying an additional fee. For example, users can send a queue request through an app and receive a notification when a seat becomes available. For example, the queue management system can also provide information on the current waiting time and available seats when a user sends a queue request. This allows users to secure seats preferentially by paying an additional fee. Some or all of the above processing in the queue management system may be performed using AI, or not. For example, the queue management system can input the user's queue request into a generating AI and have the generating AI process information on available seats.

[0035] The behavioral analysis unit analyzes user behavior data. For example, the behavioral analysis unit can analyze a user's movement history to understand their behavioral patterns. For example, the behavioral analysis unit can analyze a user's purchase history to understand their preferences. For example, the behavioral analysis unit can analyze a user's app usage history to understand their usage trends. By analyzing user behavior data, it becomes possible to provide more appropriate services. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input user behavior data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0036] The data collection unit can collect seat availability information through a smartphone application. For example, when a user opens the app, the data collection unit can display the current seat availability information, allowing the user to check for available seats. For example, the data collection unit can enable users to reserve seats or check in through the app. For example, the data collection unit can enable users to check seat availability in real time through the app. This allows users to check seat availability in real time through a smartphone application. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the seat availability data acquired through the app into a generating AI and have the generating AI perform an analysis of the seat availability information.

[0037] The analysis unit can analyze historical data and real-time usage data to predict congestion levels. For example, the analysis unit can predict congestion levels during specific time periods based on historical usage data. For example, the analysis unit can combine historical data with real-time data to understand current congestion levels. The analysis unit can also predict congestion levels by analyzing data using a generative AI. This allows for congestion level prediction by analyzing historical data and real-time usage data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical data and real-time usage data into a generative AI and have the generative AI perform congestion level predictions.

[0038] The adjustment unit can automatically shorten the available time during peak hours and set a longer available time during off-peak hours. For example, the adjustment unit can shorten the available time to 30 minutes during peak hours and set it to 1 hour during off-peak hours. The adjustment unit can also adjust the available time based on user behavior data, for example. The adjustment unit can also adjust the available time using a generating AI, for example. This optimizes the turnover rate by shortening the available time during peak hours and setting a longer available time during off-peak hours. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can have a generating AI perform the shortening of the available time during peak hours.

[0039] The service provider can award stamps based on usage time, and offer rewards once a certain number of stamps are accumulated. For example, the service provider could award stamps for usage of less than 30 minutes, and offer free drinks or discount coupons once a certain number of stamps are accumulated. The service provider could also offer additional stamps or rewards for early departure during peak hours. The service provider could also award bonus stamps to users who use the service during specific time slots. This improves user satisfaction by awarding stamps and providing rewards based on usage time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider could have a generating AI perform the awarding of stamps based on usage time.

[0040] The data collection unit can analyze past collected data and select the optimal collection timing. For example, the data collection unit can analyze congestion patterns during specific time periods from past data and set the optimal collection timing. For example, the data collection unit can also understand congestion trends by day of the week based on past data and adjust the collection timing accordingly. For example, the data collection unit can analyze past data and set collection timings according to specific events or seasons. This allows for the selection of the optimal collection timing by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection timing.

[0041] The data collection unit can filter the collected seat availability information based on the user's current location and behavioral patterns. For example, the data collection unit can prioritize collecting seat availability information for cafes close to the user's current location. The data collection unit can also analyze the user's behavioral patterns and prioritize collecting seat availability information for cafes the user frequently visits. For example, the data collection unit can consider the user's current speed of movement and filter seat availability information based on the estimated arrival time. This allows for the provision of more appropriate seat availability information by filtering seat availability information based on the user's current location and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location information and behavioral pattern data into a generating AI and have the generating AI perform the filtering of seat availability information.

[0042] The data collection unit can select the optimal data collection method when collecting seat availability information, taking into account the user's device information. For example, if the smartphone's battery level is low, the data collection unit will collect seat availability information in low-power mode. For example, if a high-performance device is being used, the data collection unit can collect seat availability information at a high frequency. The data collection unit can also adjust the collection range according to the accuracy of the device's location information. This enables efficient collection of seat availability information by selecting the optimal data collection method, taking into account the user's device information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's device information into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can analyze the user's social media activity when collecting seat availability information and collect relevant seat availability information. For example, the data collection unit can prioritize collecting seat availability information for cafes that the user has checked into on social media. For example, the data collection unit can collect seat availability information for cafes that the user follows on social media. For example, the data collection unit can analyze the content of the user's social media posts and collect seat availability information for cafes the user likes. In this way, relevant seat availability information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant seat availability information.

[0044] The analysis unit can improve analysis accuracy by combining historical data and real-time data during analysis. For example, the analysis unit can combine historical congestion data with real-time usage data for analysis. For example, the analysis unit can combine historical user behavior data with real-time location information for analysis. For example, the analysis unit can combine historical weather data with real-time weather information for analysis. By combining historical data and real-time data, the analysis accuracy can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical data and real-time data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0045] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis while considering the user's age and gender. For example, the analysis unit can perform analysis while considering the user's occupation and lifestyle. For example, the analysis unit can also perform analysis while considering the user's past usage history. By performing analysis while considering the user's attribute information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's attribute information into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can perform analysis while considering the geographical distribution of seat availability. For example, the analysis unit can analyze the geographical distribution of seat availability based on the location information of cafes. For example, the analysis unit can analyze seat availability while considering the distance from the user's current location. For example, the analysis unit can also analyze trends in seat availability in a specific area. By performing analysis while considering the geographical distribution of seat availability, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of seat availability into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can perform analysis by referring to research literature on café usage patterns. For example, the analysis unit can perform analysis by referring to usage data from other cafés. For example, the analysis unit can perform analysis by referring to relevant market research data. In this way, the accuracy of the analysis can be improved by referring to relevant literature and data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0048] The adjustment unit can set the optimal usage time by referring to past usage data during the adjustment process. For example, the adjustment unit can set the optimal usage time for a specific time period based on past usage data. For example, the adjustment unit can set the optimal usage time by referring to past user behavior data. For example, the adjustment unit can also set the optimal usage time based on past congestion data. In this way, the optimal usage time can be set by referring to past usage data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past usage data into a generating AI and have the generating AI execute the setting of the optimal usage time.

[0049] The adjustment unit can customize usage time based on the user's current behavior patterns during adjustment. For example, if the user is in a hurry, the adjustment unit may recommend shorter usage times. For example, if the user is relaxed, the adjustment unit may allow longer usage times. The adjustment unit can also analyze the user's current behavior patterns and set the optimal usage time. This allows for the provision of more appropriate usage time by customizing it based on the user's current behavior patterns. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user behavior pattern data into a generating AI and have the generating AI perform the customization of usage time.

[0050] The adjustment unit can set the optimal usage time by considering the user's geographical location information during the adjustment process. For example, the adjustment unit can set the optimal usage time by considering the distance from the user's current location to the cafe. For example, the adjustment unit can set the usage time by predicting the congestion level based on the user's geographical location information. For example, the adjustment unit can also set the optimal usage time by referring to the user's geographical location information. By setting the optimal usage time while considering the user's geographical location information, a more appropriate usage time can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI execute the setting of the optimal usage time.

[0051] The adjustment unit can analyze the user's social media activity and adjust usage time during the adjustment process. For example, the adjustment unit can set the optimal usage time based on the time of day the user checked in on social media. For example, the adjustment unit can analyze the content of the user's social media posts and set the optimal usage time. For example, the adjustment unit can analyze the user's social media activity patterns and set the optimal usage time. This allows for the provision of more appropriate usage time by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of usage time.

[0052] The service provider can select the most suitable incentive by referring to the user's past behavioral data at the time of provision. For example, the service provider can provide benefits that the user prefers based on past usage data. The service provider can select the most suitable incentive for the user by referring to past behavioral data. The service provider can also provide the most suitable benefits for the user based on past usage history. In this way, the service provider can provide the most suitable incentive by referring to the user's past behavioral data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past behavioral data into a generating AI and have the generating AI perform the selection of the most suitable incentive.

[0053] The service provider can customize incentives based on the user's current situation at the time of delivery. For example, if the user is currently in a cafe, the service provider can offer an immediate reward. For example, if the user is heading to a cafe, the service provider can offer a reward available upon arrival. For example, if the service provider is leaving the cafe, the service provider can offer a reward for the next visit. This allows for the provision of more appropriate incentives by customizing them based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current situation data into a generating AI and have the generating AI perform the incentive customization.

[0054] The service provider can provide the most suitable incentives at the time of delivery, taking into account the user's geographical location information. For example, the service provider can offer a reward usable at a cafe near the user's current location. For example, the service provider can provide the most suitable incentives based on the user's geographical location information. For example, the service provider can also offer a reward considering the distance from the user's current location. This allows for the provision of more appropriate incentives by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of the most suitable incentives.

[0055] The service provider can analyze the user's social media activity and provide incentives at the time of delivery. For example, the service provider can offer benefits usable at cafes where the user has checked in on social media. For example, the service provider can analyze the content of the user's social media posts and provide benefits tailored to their preferences. For example, the service provider can analyze the user's social media activity patterns and provide the most suitable benefits. This allows for the provision of more appropriate incentives by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the provision of incentives.

[0056] The interrupt unit can select the optimal interrupt method by referring to past interrupt data when an interrupt occurs. For example, the interrupt unit can select the optimal interrupt method based on past interrupt data. For example, the interrupt unit can select the optimal interrupt method for the user by referring to past interrupt history. For example, the interrupt unit can also analyze past interrupt data and select the most effective interrupt method. In this way, the optimal interrupt method can be selected by referring to past interrupt data. Some or all of the above processing in the interrupt unit may be performed using AI, for example, or without AI. For example, the interrupt unit can input past interrupt data to a generating AI and have the generating AI perform the selection of the optimal interrupt method.

[0057] The interrupt unit can select the optimal interrupt method when an interrupt occurs, taking into account the user's device information. For example, if the smartphone's battery level is low, the interrupt unit will interrupt in low-power mode. For example, if a high-performance device is being used, the interrupt unit can interrupt frequently. The interrupt unit can also adjust the interrupt range according to the accuracy of the device's location information. This enables efficient interrupts by selecting the optimal interrupt method considering the user's device information. Some or all of the above processing in the interrupt unit may be performed using AI, for example, or without AI. For example, the interrupt unit can input the user's device information into a generating AI and have the generating AI select the optimal interrupt method.

[0058] The behavioral analysis unit can improve the accuracy of its analysis by referring to past behavioral data during behavioral analysis. For example, the behavioral analysis unit can analyze a user's behavioral patterns based on past behavioral data. For example, the behavioral analysis unit can analyze a user's behavioral tendencies by referring to past behavioral data. For example, the behavioral analysis unit can analyze past behavioral data to identify the most efficient behavioral patterns. This allows for improved analysis accuracy by referring to past behavioral data. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving analysis accuracy.

[0059] The behavior analysis unit can perform behavioral analysis while considering the user's geographical location information. For example, the behavior analysis unit can analyze behavioral patterns while considering the distance from the user's current location. For example, the behavior analysis unit can analyze behavioral tendencies based on the user's geographical location information. For example, the behavior analysis unit can also analyze behavioral patterns by referring to the user's geographical location information. By performing analysis while considering the user's geographical location information, more appropriate analysis results can be provided. Some or all of the above processing in the behavior analysis unit may be performed using AI, for example, or without using AI. For example, the behavior analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also include a function to recommend specific menu items based on the user's purchase history. For example, if a user has frequently ordered a particular drink in the past, that drink can be recommended. If a user wants to try a new menu item, it can also suggest new menu items based on past order history. Furthermore, if a user tends to order a particular menu item at a specific time of day, menu items can be recommended accordingly. By recommending specific menu items based on the user's purchase history, customer satisfaction can be improved.

[0062] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also include a function to optimize seating arrangements based on user behavior patterns. For example, if users tend to stay for long periods, seats can be placed in quieter areas. If users tend to leave after short periods, seats closer to the entrance can be placed. Furthermore, if users prefer a particular area, seats can be placed in that area. In this way, a more comfortable cafe experience can be provided by optimizing seating arrangements based on user behavior patterns.

[0063] An AI assistant system that provides cafe users with a seat at any time and allows cafe owners to optimize turnover can also be equipped with a function to suggest the best seat based on the user's location. For example, if a user is near the cafe, it can prioritize suggesting available seats. If a user is far away, it can suggest seats based on their estimated arrival time. It can also suggest seats close to a specific area if the user is in that area. This allows for a more comfortable cafe experience by suggesting the best seat based on the user's location.

[0064] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also be equipped with the ability to suggest specific events based on users' social media activity. For example, if a user shows interest in a particular event on social media, it can suggest cafe events related to that event. If a user follows a particular cafe, it can also suggest events held at that cafe. Furthermore, if a user is active on social media at a specific time, it can suggest events tailored to that time. This can improve user satisfaction by suggesting specific events based on users' social media activity.

[0065] An AI assistant system that provides cafe users with a seating environment at all times and allows cafe owners to optimize turnover can also include a function to select the optimal notification method based on the user's device information. For example, if a smartphone's battery level is low, notifications can be sent in low-power mode. If a high-performance device is used, notifications can be sent more frequently. Furthermore, the notification range can be adjusted according to the accuracy of the device's location information. This enables efficient notifications by selecting the optimal notification method based on the user's device information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects information on seat availability. The data collection unit can collect information on seat availability, for example, through a smartphone application. It can also monitor seat availability in real time using sensors within the cafe. Furthermore, it can collect information on seat availability based on user check-in information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze historical data and real-time usage data to predict congestion levels. It can also analyze data using generative AI or data mining techniques to predict congestion levels. Step 3: The adjustment unit adjusts the usage time based on the analysis results obtained by the analysis unit. For example, the adjustment unit can automatically shorten the available time during peak hours and set a longer usage time when it is not busy. It can also adjust the usage time using user behavior data or generated AI. Step 4: The service provider offers incentives based on the usage time adjusted by the adjustment department. For example, the service provider can award stamps based on usage time, and offer rewards when a certain number of stamps are collected. They can also offer additional stamps or rewards for leaving early during peak hours. They can also offer a priority seating system where customers can pay an additional fee.

[0068] (Example of form 2) The AI ​​assistant system for providing a cafe environment where users can always find a seat and for cafe owners to optimize turnover, according to an embodiment of the present invention, is a system for providing a cafe environment where users can always find a seat and for cafe owners to optimize turnover. This system allows users to check seat availability in real time via a smartphone app and to reserve seats or check in. The generating AI analyzes past data and real-time usage data to predict congestion levels. Based on this, it dynamically adjusts the maximum available time for seats and provides an incentive system to increase turnover. For example, users can earn stamps according to their usage time, and receive rewards when a certain number of stamps are accumulated. In addition, users who leave early during busy times will receive additional stamps or rewards. Furthermore, there is also an option to be seated preferentially by paying an additional fee, differentiating the system from co-working spaces. First, users check seat availability in real time via a smartphone app. For example, when the app is opened, the current seat availability is displayed, and users can check which seats are available. Next, users can reserve seats or check in. For example, they can reserve seats in advance, or they can reserve available seats instantly in real time. Next, the generating AI analyzes past data and real-time usage data to predict congestion levels. For example, based on past usage data, it is possible to predict congestion levels during specific time periods and combine this with real-time data to understand the current congestion level. Based on this, the maximum available time for seats is dynamically adjusted. For example, the available time is automatically shortened during peak hours and extended during off-peak hours. Furthermore, an incentive system is provided to increase turnover. For example, stamps are awarded based on usage time, with stamps given for usage of less than 30 minutes, and rewards are offered once a certain number of stamps are collected. Additional stamps and rewards are also given for leaving early during peak hours. This encourages users to use the facilities for shorter periods, improving turnover. A priority seating system is also available, allowing users to pay an additional fee to secure a seat. For example, users can pay an additional fee to secure a seat preferentially.This allows users to secure a seat even during peak hours, and enables cafe owners to increase their revenue. Thus, the present invention provides an environment where cafe users can always find a seat and offers an AI assistant to help cafe owners optimize their turnover rate. This improves customer satisfaction and enhances the operational efficiency of cafe owners. This allows cafe users to always find a seat and enables cafe owners to optimize their turnover rate.

[0069] The AI ​​assistant system according to this embodiment, which provides a cafe environment where users can always find a seat and allows cafe owners to optimize turnover, comprises a data collection unit, an analysis unit, an adjustment unit, and a service unit. The data collection unit collects information on available seats. The data collection unit can collect information on available seats, for example, through a smartphone application. The data collection unit can also grasp the availability of seats in real time, for example, using sensors within the cafe. The data collection unit can also collect information on available seats, for example, based on user check-in information. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can predict congestion levels, for example, by analyzing historical data and real-time usage data. The analysis unit can also predict congestion levels, for example, by analyzing data using generative AI. The analysis unit can also predict congestion levels, for example, by analyzing data using data mining technology. The adjustment unit adjusts the usage time based on the analysis results obtained by the analysis unit. The adjustment unit can, for example, automatically shorten the available time when it is crowded and set a longer usage time when it is not crowded. The adjustment unit can also adjust the usage time based on user behavior data, for example. The adjustment unit can, for example, adjust usage time using a generating AI. The provision unit provides incentives based on the usage time adjusted by the adjustment unit. The provision unit can, for example, award stamps according to usage time, and provide rewards when a certain number are accumulated. The provision unit can, for example, offer additional stamps or rewards for leaving early during busy times. The provision unit can, for example, offer a queue-queueing system where customers can be seated preferentially by paying an additional fee. In this way, the AI ​​assistant system for providing an environment where cafe users can always find a seat and for cafe owners to optimize their turnover rate can provide an environment where cafe users can always find a seat and for cafe owners to optimize their turnover rate.

[0070] The data collection unit collects information on seat availability. For example, the data collection unit can collect seat availability through a smartphone application. Specifically, when a user arrives at the cafe, they launch the app and check in, updating the current seat availability in real time. The app obtains the user's location information and compares it with the seating arrangement in the cafe to accurately determine which seats are available. The system can also use sensors within the cafe to determine seat availability in real time. For example, pressure sensors or infrared sensors installed on each table detect seat occupancy and transmit the information to a central database. This ensures the system always maintains the most up-to-date seat availability information. Furthermore, seat availability can also be collected based on user check-in information. When a user enters the cafe, they check in by scanning a smartphone app or QR code, and this information is sent to the data collection unit. This allows the data collection unit to accurately determine seat availability in various ways and update it in real time. The collected data is stored on a cloud server, making it accessible to the analysis and adjustment units. This allows the data collection unit to efficiently and effectively collect data and improve the overall system performance.

[0071] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze historical data and real-time usage data to predict congestion levels. Specifically, it analyzes congestion patterns on specific days of the week and time slots based on historical usage data to predict future congestion levels. Furthermore, it can also analyze data using generative AI to predict congestion levels. The generative AI takes collected data as input and simulates multiple scenarios to identify the most likely congestion level. For example, it can take external factors such as weather and event information into consideration to make more accurate predictions. It can also analyze data using data mining techniques to predict congestion levels. Data mining techniques extract useful patterns and trends from large amounts of data to identify the causes and impacts of congestion. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. 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, improving the reliability and safety of the entire system.

[0072] The adjustment unit adjusts the usage time based on the analysis results obtained by the analysis unit. Specifically, it can automatically shorten the available time during peak hours and set a longer usage time when the cafe is not busy. For example, if the cafe is crowded, it can send a notification to the user encouraging them to shorten their usage time. Conversely, if the cafe is not busy, it can offer the user a longer usage time so they can relax. It can also adjust the usage time based on user behavior data. For example, it can analyze past usage history and current behavior patterns to suggest the optimal usage time for each individual user. It can also adjust the usage time using generative AI. The generative AI calculates the optimal usage time based on the collected data and suggests it to the user. This allows the adjustment unit to adjust the usage time efficiently and flexibly, optimizing the cafe's turnover rate. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy and effectiveness of the adjustments. This allows the adjustment unit to always provide highly accurate adjustments based on the latest information, improving the operational efficiency of the cafe.

[0073] The service department provides incentives based on usage time adjusted by the adjustment department. Specifically, users can earn stamps according to their usage time, and receive rewards once a certain number of stamps are collected. For example, users can earn stamps each time they use the cafe, and once a certain number are collected, they can receive free drinks or discount coupons. Additional stamps or rewards can also be offered for leaving early during busy times. This provides users with an incentive to vacate their seats early during busy periods, improving the cafe's turnover rate. A priority seating system can also be offered for an additional fee. For example, users can pay an additional fee to secure a seat even during busy times. This allows the service department to offer diverse incentives to users and promote cafe use. Furthermore, the service department can collect user feedback and continuously improve the accuracy and effectiveness of the incentives. This allows the service department to always provide highly accurate incentives based on the latest information, improving the operational efficiency of the cafe.

[0074] The queue management system implements a queue management mechanism. For example, the queue management system allows users to secure seats preferentially by paying an additional fee. For example, users can send a queue request through an app and receive a notification when a seat becomes available. For example, the queue management system can also provide information on the current waiting time and available seats when a user sends a queue request. This allows users to secure seats preferentially by paying an additional fee. Some or all of the above processing in the queue management system may be performed using AI, or not. For example, the queue management system can input the user's queue request into a generating AI and have the generating AI process information on available seats.

[0075] The behavioral analysis unit analyzes user behavior data. For example, the behavioral analysis unit can analyze a user's movement history to understand their behavioral patterns. For example, the behavioral analysis unit can analyze a user's purchase history to understand their preferences. For example, the behavioral analysis unit can analyze a user's app usage history to understand their usage trends. By analyzing user behavior data, it becomes possible to provide more appropriate services. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input user behavior data into a generating AI and have the generating AI perform behavioral pattern analysis.

[0076] The data collection unit can collect seat availability information through a smartphone application. For example, when a user opens the app, the data collection unit can display the current seat availability information, allowing the user to check for available seats. For example, the data collection unit can enable users to reserve seats or check in through the app. For example, the data collection unit can enable users to check seat availability in real time through the app. This allows users to check seat availability in real time through a smartphone application. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the seat availability data acquired through the app into a generating AI and have the generating AI perform an analysis of the seat availability information.

[0077] The analysis unit can analyze historical data and real-time usage data to predict congestion levels. For example, the analysis unit can predict congestion levels during specific time periods based on historical usage data. For example, the analysis unit can combine historical data with real-time data to understand current congestion levels. The analysis unit can also predict congestion levels by analyzing data using a generative AI. This allows for congestion level prediction by analyzing historical data and real-time usage data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical data and real-time usage data into a generative AI and have the generative AI perform congestion level predictions.

[0078] The adjustment unit can automatically shorten the available time during peak hours and set a longer available time during off-peak hours. For example, the adjustment unit can shorten the available time to 30 minutes during peak hours and set it to 1 hour during off-peak hours. The adjustment unit can also adjust the available time based on user behavior data, for example. The adjustment unit can also adjust the available time using a generating AI, for example. This optimizes the turnover rate by shortening the available time during peak hours and setting a longer available time during off-peak hours. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can have a generating AI perform the shortening of the available time during peak hours.

[0079] The service provider can award stamps based on usage time, and offer rewards once a certain number of stamps are accumulated. For example, the service provider could award stamps for usage of less than 30 minutes, and offer free drinks or discount coupons once a certain number of stamps are accumulated. The service provider could also offer additional stamps or rewards for early departure during peak hours. The service provider could also award bonus stamps to users who use the service during specific time slots. This improves user satisfaction by awarding stamps and providing rewards based on usage time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider could have a generating AI perform the awarding of stamps based on usage time.

[0080] The data collection unit can estimate the user's emotions and adjust the frequency of collecting seat availability information based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to provide real-time seat availability information. For example, if the user is relaxed, the data collection unit can decrease the collection frequency to conserve battery power. For example, if the user is in a hurry, the data collection unit can increase the collection frequency to provide seat availability information quickly. By adjusting the frequency of seat availability information collection based on the user's emotions, more appropriate seat availability information can be provided. 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 AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can analyze past collected data and select the optimal collection timing. For example, the data collection unit can analyze congestion patterns during specific time periods from past data and set the optimal collection timing. For example, the data collection unit can also understand congestion trends by day of the week based on past data and adjust the collection timing accordingly. For example, the data collection unit can analyze past data and set collection timings according to specific events or seasons. This allows for the selection of the optimal collection timing by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select the optimal collection timing.

[0082] The data collection unit can filter the collected seat availability information based on the user's current location and behavioral patterns. For example, the data collection unit can prioritize collecting seat availability information for cafes close to the user's current location. The data collection unit can also analyze the user's behavioral patterns and prioritize collecting seat availability information for cafes the user frequently visits. For example, the data collection unit can consider the user's current speed of movement and filter seat availability information based on the estimated arrival time. This allows for the provision of more appropriate seat availability information by filtering seat availability information based on the user's current location and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location information and behavioral pattern data into a generating AI and have the generating AI perform the filtering of seat availability information.

[0083] The data collection unit can estimate the user's emotions and determine the priority of available seat information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information on quiet seats. For example, if the user is relaxed, the data collection unit may prioritize collecting information on seats with good views. For example, if the user is in a hurry, the data collection unit may prioritize collecting information on seats that can be occupied immediately. This allows for the provision of more appropriate seat information by prioritizing available seat information 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 AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The data collection unit can select the optimal data collection method when collecting seat availability information, taking into account the user's device information. For example, if the smartphone's battery level is low, the data collection unit will collect seat availability information in low-power mode. For example, if a high-performance device is being used, the data collection unit can collect seat availability information at a high frequency. The data collection unit can also adjust the collection range according to the accuracy of the device's location information. This enables efficient collection of seat availability information by selecting the optimal data collection method, taking into account the user's device information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's device information into a generating AI and have the generating AI select the optimal data collection method.

[0085] The data collection unit can analyze the user's social media activity when collecting seat availability information and collect relevant seat availability information. For example, the data collection unit can prioritize collecting seat availability information for cafes that the user has checked into on social media. For example, the data collection unit can collect seat availability information for cafes that the user follows on social media. For example, the data collection unit can analyze the content of the user's social media posts and collect seat availability information for cafes the user likes. In this way, relevant seat availability information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant seat availability information.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use an algorithm that provides quick analysis results. For example, if the user is relaxed, the analysis unit can use an algorithm that provides detailed analysis results. For example, if the user is in a hurry, the analysis unit can also use an algorithm that provides concise analysis results. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be provided. 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can improve analysis accuracy by combining historical data and real-time data during analysis. For example, the analysis unit can combine historical congestion data with real-time usage data for analysis. For example, the analysis unit can combine historical user behavior data with real-time location information for analysis. For example, the analysis unit can combine historical weather data with real-time weather information for analysis. By combining historical data and real-time data, the analysis accuracy can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input historical data and real-time data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0088] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis while considering the user's age and gender. For example, the analysis unit can perform analysis while considering the user's occupation and lifestyle. For example, the analysis unit can also perform analysis while considering the user's past usage history. By performing analysis while considering the user's attribute information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's attribute information into a generating AI and have the generating AI perform the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit can perform analysis while considering the geographical distribution of seat availability. For example, the analysis unit can analyze the geographical distribution of seat availability based on the location information of cafes. For example, the analysis unit can analyze seat availability while considering the distance from the user's current location. For example, the analysis unit can also analyze trends in seat availability in a specific area. By performing analysis while considering the geographical distribution of seat availability, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of seat availability into a generating AI and have the generating AI perform the analysis.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can perform analysis by referring to research literature on café usage patterns. For example, the analysis unit can perform analysis by referring to usage data from other cafés. For example, the analysis unit can perform analysis by referring to relevant market research data. In this way, the accuracy of the analysis can be improved by referring to relevant literature and data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0092] The adjustment unit can estimate the user's emotions and change the method of adjusting usage time based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit may recommend shorter usage times. For example, if the user is relaxed, the adjustment unit may allow longer usage times. For example, if the user is in a hurry, the adjustment unit may prioritize shorter usage times. This allows for the provision of more appropriate usage time by changing the method of adjusting usage time 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 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 adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The adjustment unit can set the optimal usage time by referring to past usage data during the adjustment process. For example, the adjustment unit can set the optimal usage time for a specific time period based on past usage data. For example, the adjustment unit can set the optimal usage time by referring to past user behavior data. For example, the adjustment unit can also set the optimal usage time based on past congestion data. In this way, the optimal usage time can be set by referring to past usage data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past usage data into a generating AI and have the generating AI execute the setting of the optimal usage time.

[0094] The adjustment unit can customize usage time based on the user's current behavior patterns during adjustment. For example, if the user is in a hurry, the adjustment unit may recommend shorter usage times. For example, if the user is relaxed, the adjustment unit may allow longer usage times. The adjustment unit can also analyze the user's current behavior patterns and set the optimal usage time. This allows for the provision of more appropriate usage time by customizing it based on the user's current behavior patterns. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user behavior pattern data into a generating AI and have the generating AI perform the customization of usage time.

[0095] The adjustment unit can estimate the user's emotions and determine the priority of usage time based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit may prioritize short usage times. For example, if the user is relaxed, the adjustment unit may prioritize longer usage times. For example, if the user is in a hurry, the adjustment unit may also prioritize short usage times. This allows for the provision of more appropriate usage time by determining the priority of usage time 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 adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The adjustment unit can set the optimal usage time by considering the user's geographical location information during the adjustment process. For example, the adjustment unit can set the optimal usage time by considering the distance from the user's current location to the cafe. For example, the adjustment unit can set the usage time by predicting the congestion level based on the user's geographical location information. For example, the adjustment unit can also set the optimal usage time by referring to the user's geographical location information. By setting the optimal usage time while considering the user's geographical location information, a more appropriate usage time can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI execute the setting of the optimal usage time.

[0097] The adjustment unit can analyze the user's social media activity and adjust usage time during the adjustment process. For example, the adjustment unit can set the optimal usage time based on the time of day the user checked in on social media. For example, the adjustment unit can analyze the content of the user's social media posts and set the optimal usage time. For example, the adjustment unit can analyze the user's social media activity patterns and set the optimal usage time. This allows for the provision of more appropriate usage time by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of usage time.

[0098] The service provider can estimate the user's emotions and adjust the method of providing incentives based on the estimated emotions. For example, if the user is stressed, the service provider can offer a relaxing perk. For example, if the user is relaxed, the service provider can offer a perk that encourages longer usage. For example, if the user is in a hurry, the service provider can offer a perk that can be used quickly. This allows for the provision of more appropriate incentives by adjusting the method of providing incentives 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The service provider can select the most suitable incentive by referring to the user's past behavioral data at the time of provision. For example, the service provider can provide benefits that the user prefers based on past usage data. The service provider can select the most suitable incentive for the user by referring to past behavioral data. The service provider can also provide the most suitable benefits for the user based on past usage history. In this way, the service provider can provide the most suitable incentive by referring to the user's past behavioral data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past behavioral data into a generating AI and have the generating AI perform the selection of the most suitable incentive.

[0100] The service provider can customize incentives based on the user's current situation at the time of delivery. For example, if the user is currently in a cafe, the service provider can offer an immediate reward. For example, if the user is heading to a cafe, the service provider can offer a reward available upon arrival. For example, if the service provider is leaving the cafe, the service provider can offer a reward for the next visit. This allows for the provision of more appropriate incentives by customizing them based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current situation data into a generating AI and have the generating AI perform the incentive customization.

[0101] The service provider can estimate the user's emotions and prioritize incentives based on those emotions. For example, if the user is stressed, the service provider can prioritize offering relaxing perks. If the user is relaxed, the service provider can prioritize offering perks that encourage longer usage. If the user is in a hurry, the service provider can prioritize offering perks that can be used quickly. By prioritizing incentives based on the user's emotions, more appropriate incentives can be provided. 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The service provider can provide the most suitable incentives at the time of delivery, taking into account the user's geographical location information. For example, the service provider can offer a reward usable at a cafe near the user's current location. For example, the service provider can provide the most suitable incentives based on the user's geographical location information. For example, the service provider can also offer a reward considering the distance from the user's current location. This allows for the provision of more appropriate incentives by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of the most suitable incentives.

[0103] The service provider can analyze the user's social media activity and provide incentives at the time of delivery. For example, the service provider can offer benefits usable at cafes where the user has checked in on social media. For example, the service provider can analyze the content of the user's social media posts and provide benefits tailored to their preferences. For example, the service provider can analyze the user's social media activity patterns and provide the most suitable benefits. This allows for the provision of more appropriate incentives by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the provision of incentives.

[0104] The interruption unit can estimate the user's emotions and determine the priority of interruptions based on the estimated emotions. For example, if the user is stressed, the interruption unit may set a higher priority for the interruption. For example, if the user is relaxed, the interruption unit may set a lower priority for the interruption. For example, if the user is in a hurry, the interruption unit may set a higher priority for the interruption. This allows for the provision of more appropriate interruptions by determining the priority of interruptions 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 interruption unit may be performed using AI, for example, or not using AI. For example, the interruption unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The interrupt unit can select the optimal interrupt method by referring to past interrupt data when an interrupt occurs. For example, the interrupt unit can select the optimal interrupt method based on past interrupt data. For example, the interrupt unit can select the optimal interrupt method for the user by referring to past interrupt history. For example, the interrupt unit can also analyze past interrupt data and select the most effective interrupt method. In this way, the optimal interrupt method can be selected by referring to past interrupt data. Some or all of the above processing in the interrupt unit may be performed using AI, for example, or without AI. For example, the interrupt unit can input past interrupt data to a generating AI and have the generating AI perform the selection of the optimal interrupt method.

[0106] The interruption unit can estimate the user's emotions and adjust how the interruption is displayed based on the estimated emotions. For example, if the user is tense, the interruption unit can provide a simple and highly visible display. For example, if the user is relaxed, the interruption unit can provide a display that includes detailed information. For example, if the user is in a hurry, the interruption unit can provide a display that gets straight to the point. By adjusting how the interruption is displayed based on the user's emotions, more appropriate information can be provided. 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 interruption unit may be performed using AI, for example, or without AI. For example, the interruption unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The interrupt unit can select the optimal interrupt method when an interrupt occurs, taking into account the user's device information. For example, if the smartphone's battery level is low, the interrupt unit will interrupt in low-power mode. For example, if a high-performance device is being used, the interrupt unit can interrupt frequently. The interrupt unit can also adjust the interrupt range according to the accuracy of the device's location information. This enables efficient interrupts by selecting the optimal interrupt method considering the user's device information. Some or all of the above processing in the interrupt unit may be performed using AI, for example, or without AI. For example, the interrupt unit can input the user's device information into a generating AI and have the generating AI select the optimal interrupt method.

[0108] The behavioral analysis unit can estimate the user's emotions and adjust the behavioral analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the behavioral analysis unit can use an algorithm that provides analysis results quickly. For example, if the user is relaxed, the behavioral analysis unit can use an algorithm that provides detailed analysis results. For example, if the user is in a hurry, the behavioral analysis unit can also use an algorithm that provides concise analysis results. By adjusting the behavioral analysis algorithm based on the user's emotions, more appropriate analysis results can be provided. 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 behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The behavioral analysis unit can improve the accuracy of its analysis by referring to past behavioral data during behavioral analysis. For example, the behavioral analysis unit can analyze a user's behavioral patterns based on past behavioral data. For example, the behavioral analysis unit can analyze a user's behavioral tendencies by referring to past behavioral data. For example, the behavioral analysis unit can analyze past behavioral data to identify the most efficient behavioral patterns. This allows for improved analysis accuracy by referring to past behavioral data. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving analysis accuracy.

[0110] The behavioral analysis unit can estimate the user's emotions and adjust the display method of the behavioral analysis results based on the estimated user emotions. For example, if the user is nervous, the behavioral analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the behavioral analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the behavioral analysis unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the behavioral analysis results based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The behavior analysis unit can perform behavioral analysis while considering the user's geographical location information. For example, the behavior analysis unit can analyze behavioral patterns while considering the distance from the user's current location. For example, the behavior analysis unit can analyze behavioral tendencies based on the user's geographical location information. For example, the behavior analysis unit can also analyze behavioral patterns by referring to the user's geographical location information. By performing analysis while considering the user's geographical location information, more appropriate analysis results can be provided. Some or all of the above processing in the behavior analysis unit may be performed using AI, for example, or without using AI. For example, the behavior analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also be equipped with the ability to estimate user emotions and dynamically change seating arrangements based on those emotions. For example, if a user is feeling stressed, they can be seated in a quiet area. If a user is relaxed, they can be seated in an area with a good view. If a user is in a hurry, they can be seated in a seat closer to the entrance. In this way, a more comfortable cafe experience can be provided by dynamically changing seating arrangements based on user emotions.

[0114] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also include a function to recommend specific menu items based on the user's purchase history. For example, if a user has frequently ordered a particular drink in the past, that drink can be recommended. If a user wants to try a new menu item, it can also suggest new menu items based on past order history. Furthermore, if a user tends to order a particular menu item at a specific time of day, menu items can be recommended accordingly. By recommending specific menu items based on the user's purchase history, customer satisfaction can be improved.

[0115] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also be equipped with the ability to estimate the user's emotions and adjust music and lighting based on those emotions. For example, if a user is feeling stressed, it can play relaxing music and soften the lighting. If the user is relaxed, it can provide bright lighting and lively music. If the user is in a hurry, it can play fast-paced music and brighten the lighting. In this way, a more comfortable cafe experience can be provided by adjusting music and lighting based on the user's emotions.

[0116] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also include a function to optimize seating arrangements based on user behavior patterns. For example, if users tend to stay for long periods, seats can be placed in quieter areas. If users tend to leave after short periods, seats closer to the entrance can be placed. Furthermore, if users prefer a particular area, seats can be placed in that area. In this way, a more comfortable cafe experience can be provided by optimizing seating arrangements based on user behavior patterns.

[0117] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also be equipped with the ability to estimate the user's emotions and adjust the menu display based on those emotions. For example, if the user is stressed, a simple and easy-to-read menu can be displayed. If the user is relaxed, a menu with more detailed information can be displayed. And if the user is in a hurry, a menu with the essentials can be displayed. In this way, by adjusting the menu display based on the user's emotions, more relevant information can be provided.

[0118] An AI assistant system that provides cafe users with a seat at any time and allows cafe owners to optimize turnover can also be equipped with a function to suggest the best seat based on the user's location. For example, if a user is near the cafe, it can prioritize suggesting available seats. If a user is far away, it can suggest seats based on their estimated arrival time. It can also suggest seats close to a specific area if the user is in that area. This allows for a more comfortable cafe experience by suggesting the best seat based on the user's location.

[0119] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also be equipped with the ability to estimate user emotions and adjust how rewards are offered based on those emotions. For example, if a user is feeling stressed, it can offer relaxing rewards. If a user is relaxed, it can offer rewards that encourage longer stays. And if a user is in a hurry, it can offer rewards that can be used quickly. In this way, by adjusting how rewards are offered based on user emotions, more appropriate rewards can be provided.

[0120] An AI assistant system that provides a seating environment for cafe users at any time and allows cafe owners to optimize turnover can also be equipped with the ability to suggest specific events based on users' social media activity. For example, if a user shows interest in a particular event on social media, it can suggest cafe events related to that event. If a user follows a particular cafe, it can also suggest events held at that cafe. Furthermore, if a user is active on social media at a specific time, it can suggest events tailored to that time. This can improve user satisfaction by suggesting specific events based on users' social media activity.

[0121] An AI assistant system designed to provide cafe users with a seating environment at any time and to help cafe owners optimize turnover can also be equipped with the ability to estimate a user's emotions and adjust the seating reservation process based on those emotions. For example, if a user is feeling stressed, they can reserve a seat with a simple procedure. If they are relaxed, they can reserve a seat by entering more detailed information. Furthermore, if a user is in a hurry, they can reserve a seat quickly. This allows for a more comfortable cafe experience by adjusting the seating reservation process based on the user's emotions.

[0122] An AI assistant system that provides cafe users with a seating environment at all times and allows cafe owners to optimize turnover can also include a function to select the optimal notification method based on the user's device information. For example, if a smartphone's battery level is low, notifications can be sent in low-power mode. If a high-performance device is used, notifications can be sent more frequently. Furthermore, the notification range can be adjusted according to the accuracy of the device's location information. This enables efficient notifications by selecting the optimal notification method based on the user's device information.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects information on seat availability. The data collection unit can collect information on seat availability, for example, through a smartphone application. It can also monitor seat availability in real time using sensors within the cafe. Furthermore, it can collect information on seat availability based on user check-in information. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze historical data and real-time usage data to predict congestion levels. It can also analyze data using generative AI or data mining techniques to predict congestion levels. Step 3: The adjustment unit adjusts the usage time based on the analysis results obtained by the analysis unit. For example, the adjustment unit can automatically shorten the available time during peak hours and set a longer usage time when it is not busy. It can also adjust the usage time using user behavior data or generated AI. Step 4: The service provider offers incentives based on the usage time adjusted by the adjustment department. For example, the service provider can award stamps based on usage time, and offer rewards when a certain number of stamps are collected. They can also offer additional stamps or rewards for leaving early during peak hours. They can also offer a priority seating system where customers can pay an additional fee.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, provision unit, interruption unit, behavior analysis unit, and emotion estimation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect seat availability and user emotions, and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to predict congestion levels and user behavior patterns. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and dynamically adjusts usage time based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides incentive and interruption systems. The interruption unit is implemented, for example, by the control unit 46A of the smart device 14, and allows users to secure seats preferentially by paying an additional fee. The behavior analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes user behavior data. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the collection frequency. 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.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, provision unit, interruption unit, behavior analysis unit, and emotion estimation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect seat availability and user emotions, and transmits this data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts congestion levels and user behavior patterns. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which dynamically adjusts usage time based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides incentive and interruption systems. The interruption unit is implemented, for example, by the control unit 46A of the smart glasses 214, which allows users to secure seats preferentially by paying an additional fee. The behavioral analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's behavioral data. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the collection frequency. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, provision unit, interruption unit, behavior analysis unit, and emotion estimation unit, is implemented, for example, 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 microphone 238 of the headset terminal 314 to collect seat availability and user emotions, and transmits this data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts congestion levels and user behavior patterns. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which dynamically adjusts usage time based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides incentive and interruption systems. The interruption unit is implemented, for example, by the control unit 46A of the headset terminal 314, which allows users to secure seats preferentially by paying an additional fee. The behavioral analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's behavioral data. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the collection frequency. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, adjustment unit, provision unit, interruption unit, behavior analysis unit, and emotion estimation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect seat availability and user emotions, and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts congestion levels and user behavior patterns. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which dynamically adjusts usage time based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides incentive and interruption systems. The interruption unit is implemented, for example, by the control unit 46A of the robot 414, which allows users to secure seats preferentially by paying an additional fee. The behavior analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes user behavior data. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotions and adjusts the collection frequency. 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection department that collects information on seat availability, An analysis unit analyzes the data collected by the aforementioned collection unit, An adjustment unit adjusts the usage time based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides incentives based on the usage time adjusted by the adjustment unit. A system characterized by the following features. (Note 2) Equipped with an interruption unit to implement an interruption system. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a behavioral analysis unit that analyzes user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collect seat availability information through a smartphone application. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze historical data and real-time usage data to predict congestion levels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, During peak hours, the system automatically shortens the available usage time, and during off-peak hours, it sets a longer usage time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Earn stamps based on usage time, and receive rewards once a certain number of stamps are collected. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting seat availability information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past collected data to select the optimal collection timing. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting seat availability data, filtering is performed based on the user's current location and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the collection of available seating information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting seat availability data, the optimal collection method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting seat availability information, we analyze users' social media activity to gather relevant seat availability data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, historical data and real-time data are combined to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the geographical distribution of available seats will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to relevant literature and data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, The system estimates the user's emotions and adjusts the usage time based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, During adjustment, past usage data is referenced to set the optimal usage time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, During adjustments, usage time is customized based on the user's current behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, It estimates the user's emotions and prioritizes usage time based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, During adjustment, the optimal usage time is set considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, During the adjustment process, we analyze users' social media activity and adjust usage time accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how incentives are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the system selects the most suitable incentive by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, At the time of delivery, the incentives will be customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates user emotions and determines incentive priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we will consider the user's geographical location to offer the most appropriate incentives. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and offer incentives. The system described in Appendix 1, characterized by the features described herein. (Note 32) The interrupt unit is, It estimates the user's emotions and determines the priority of interrupts based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The interrupt unit is, When an interrupt occurs, the system selects the optimal interrupt method by referring to past interrupt data. The system described in Appendix 2, characterized by the features described herein. (Note 34) The interrupt unit is, It estimates the user's emotions and adjusts how interrupts are displayed based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The interrupt unit is, When an interrupt occurs, the system selects the optimal interrupt method, taking into account the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned behavioral analysis unit, It estimates the user's emotions and adjusts the behavioral analysis algorithm based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned behavioral analysis unit, When analyzing behavior, past behavioral data is referenced to improve the accuracy of the analysis. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned behavioral analysis unit, It estimates the user's emotions and adjusts how the behavioral analysis results are displayed based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned behavioral analysis unit, When analyzing user behavior, the analysis takes into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on seat availability, An analysis unit analyzes the data collected by the aforementioned collection unit, An adjustment unit adjusts the usage time based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides incentives based on the usage time adjusted by the adjustment unit. A system characterized by the following features.

2. Equipped with an interruption unit to implement an interruption system. The system according to feature 1.

3. It includes a behavioral analysis unit that analyzes user behavior data. The system according to feature 1.

4. The aforementioned collection unit is We collect seat availability information through a smartphone application. The system according to feature 1.

5. The aforementioned analysis unit, We analyze historical data and real-time usage data to predict congestion levels. The system according to feature 1.

6. The adjustment unit is, During peak hours, the system automatically shortens the available usage time, and during off-peak hours, it sets a longer usage time. The system according to feature 1.

7. The aforementioned supply unit is, Earn stamps based on usage time, and receive rewards once a certain number of stamps are collected. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting seat availability information based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze past collected data to select the optimal collection timing. The system according to feature 1.

10. The aforementioned collection unit is When collecting seat availability data, filtering is performed based on the user's current location and behavioral patterns. The system according to feature 1.

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