system

Abstract

The system according to this embodiment aims to improve the efficiency of toilet space utilization and make it easier for users to find a suitable toilet space. [Solution] The system according to the embodiment comprises a collection unit, a matching unit, and a provision unit. The collection unit collects information on toilet spaces. The matching unit matches users with toilet spaces based on the information collected by the collection unit. The provision unit provides the toilet spaces matched by the matching unit to the users.

Description

Technical Field

[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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the utilization efficiency of toilet space is low and it is difficult for users to find an appropriate toilet space.

[0005] The system according to the embodiment aims to improve the utilization efficiency of toilet space and make it easier for users to find an appropriate toilet space.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a matching unit, and a provision unit. The collection unit collects information on toilet spaces. The matching unit matches users with toilet spaces based on the information collected by the collection unit. The provision unit provides the toilet spaces matched by the matching unit to the users. [Effects of the Invention]

[0007] The system according to this embodiment can improve the efficiency of toilet space utilization and make it easier for users to find a suitable toilet space. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The matching service for buying and selling toilet spaces according to an embodiment of the present invention is a system that matches users with currently unused toilet spaces by putting them up for auction, thereby completing the sale. The auctioneer collects a fee from both the seller and the bidder. In the first phase, existing facilities are utilized, and shared office spaces are used as shared toilet spaces. In the second phase, the service is deployed in locations with high toilet demand, such as fireworks venues, festivals, and live music venues. In the third phase, this format is expanded to other uses, such as applying makeup, changing clothes, breastfeeding, and napping. This service targets tourists visiting Japan, event attendees, travelers, drivers, affluent individuals who do not want to wait in line, and affluent individuals who do not want to share with others, solving problems such as not being able to find a toilet, long waiting times, being unable to go to places without toilets, and feeling uneasy. Generative AI is used to perform auctions, ratings, person identification, collection of payments, analysis of human flow, demand forecasting, and matching, displaying real-time availability and providing the optimal toilet space. As a result, the matching service for buying and selling toilet spaces can provide toilet spaces to users quickly and efficiently.

[0029] The matching system for buying and selling toilet spaces according to this embodiment comprises a collection unit, a matching unit, and a provision unit. The collection unit collects information about toilet spaces. For example, the collection unit collects information such as the location, availability time, and type of facilities of the toilet space. The collection unit can also monitor the usage status of toilet spaces in real time using sensors and collect that information. The collection unit can also collect feedback from users and collect evaluation information for toilet spaces. For example, the collection unit provides an interface for users to input evaluations after using a toilet space and collects that evaluation information. The matching unit matches users with toilet spaces based on the information collected by the collection unit. For example, the matching unit selects the most suitable toilet space based on information such as the user's location, usage history, and preferences. For example, the matching unit prioritizes matching users with toilet spaces close to their current location. The matching unit can also analyze the user's past usage history and prioritize matching users with frequently used toilet spaces. For example, the matching unit prioritizes matching users with toilet spaces that have received high ratings in the past. The provision unit provides the user with the toilet space matched by the matching unit. The service provider, for example, provides users with detailed information about toilet spaces and guides them on how to use them. The service provider also provides an interface for users to reserve toilet spaces and manages reservation information. Furthermore, the service provider can also provide support to users when they use the toilet spaces. For example, the service provider guides users upon arrival at the toilet space and explains how to use it. In this way, the matching system for buying and selling toilet spaces according to this embodiment can provide toilet spaces to users quickly and efficiently.

[0030] The data collection unit collects information about toilet spaces. For example, it collects information such as the location, availability, and type of facilities. Specifically, the unit uses GPS devices and Wi-Fi location information to pinpoint the exact location of toilet spaces and stores it in a database. Regarding availability, it collects schedule information provided by the toilet space manager and updates it in real time. Regarding the type of facilities, it collects detailed information about the type of toilet installed in the toilet space (Japanese style, Western style, barrier-free access, etc.) and ancillary facilities such as sinks, soap, paper towels, and hand dryers. Furthermore, the data collection unit can use sensors to monitor the usage status of toilet spaces in real time and collect this information. For example, it can install door sensors and motion sensors to detect whether the toilet space is occupied. It can also monitor the environmental conditions of the toilet space using temperature and humidity sensors. This provides information to help users determine whether the toilet space is comfortable to use. Additionally, the data collection unit can collect user feedback and collect evaluation information about the toilet space. For example, it can provide an interface for users to input their evaluations after using the toilet space and collect this evaluation information. The evaluation criteria include cleanliness, the completeness of facilities, and ease of access, and this information serves as useful reference information for other users. The collection department centrally manages this diverse information and stores it in a database, enabling the matching and provision departments to use it efficiently.

[0031] The matching unit matches users with toilet spaces based on information collected by the data collection unit. Specifically, it selects the most suitable toilet space based on information such as the user's location, usage history, and preferences. For example, to prioritize matching users with toilet spaces close to their current location, it acquires GPS data in real time and lists the toilet spaces closest to the user. It can also analyze the user's past usage history and prioritize matching users with frequently used toilet spaces. For example, it can prioritize matching users with toilet spaces that they have previously given high ratings to. This allows users to quickly find toilet spaces that suit their preferences. Furthermore, the matching unit can use AI to learn user preferences and behavior patterns, enabling more accurate matching. For example, if a user tends to use a toilet space at a specific time and location, the unit learns this pattern and reflects it in future matches. It can also evaluate the quality of toilet spaces based on user ratings and prioritize matching users with highly rated toilet spaces. In this way, the matching unit can provide users with the most suitable toilet space and improve user satisfaction.

[0032] The service provider provides users with toilet spaces matched by the matching service provider. Specifically, it provides users with detailed information about the toilet spaces and guides them on how to use them. For example, the service provider provides an interface on the user's smartphone that displays the location, available hours, type of facilities, and rating information of the toilet space. Through this interface, users can check the detailed information about the toilet space and decide whether to use it. The service provider also provides an interface for users to reserve toilet spaces and manages reservation information. For example, if a user wants to use a toilet space during a specific time slot, they can make a reservation in advance through the reservation system. Reservation information is updated in real time and managed to prevent overlapping reservations by other users during the same time slot. Furthermore, the service provider can also provide support to users when they use the toilet space. For example, it guides users when they arrive at the toilet space and explains how to use it. This includes guiding them to the entrance of the toilet space and explaining how to use the facilities. The service provider also provides an interface for users to provide feedback after using the toilet space and collects user ratings. This allows the service provider to provide toilet spaces to users quickly and efficiently and improve user satisfaction.

[0033] The auction unit can conduct sales in an auction format. The auction unit can buy and sell toilet spaces using formats such as English auctions or Dutch auctions. The auction unit can, for example, have users list toilet spaces for sale, and other users bid to complete the sale. The auction unit can also display the progress of the auction in real time, allowing users to check the current bidding status. For example, the auction unit can display the current highest bid and the remaining time, allowing users to decide when to bid. This enables the buying and selling of toilet spaces in an auction format. Some or all of the above processes in the auction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the auction unit can input the progress of the auction into a generative AI and have the generative AI output the auction results.

[0034] The display unit can display real-time availability. For example, the display unit can monitor the usage status of toilet spaces in real time and display that information. For example, the display unit can update the availability status of toilet spaces every second or minute so that users can check the latest information. The display unit can also prioritize displaying the availability status of nearby toilet spaces based on the user's location information. For example, the display unit can display the availability status of toilet spaces close to the user's current location, making it easier for the user to find a toilet space that can be used immediately. This allows for the display of toilet space availability in real time. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input the usage status of toilet spaces into a generating AI and have the generating AI output the availability status display.

[0035] The analysis unit can analyze pedestrian flow and forecast demand. For example, the analysis unit can monitor pedestrian flow using sensors and collect the data. For example, the analysis unit can perform tasks such as counting people and analyzing movement patterns to understand trends in pedestrian flow. The analysis unit can also forecast demand based on past data. For example, the analysis unit can analyze past event data and predict the tendency for demand for toilet spaces to increase during specific events. This enables the analysis of pedestrian flow and forecasting demand. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input pedestrian flow data into generative AI and have the generative AI output the results of the demand forecast.

[0036] The data collection unit can analyze the past usage history of toilet spaces and select the optimal information collection method. For example, the data collection unit can analyze trends in usage concentration during specific time periods based on past usage history and focus on collecting information during those times. For example, the data collection unit can identify trends in increased usage on specific days of the week or during events based on usage history and strengthen information collection accordingly. Furthermore, the data collection unit can analyze past usage history and prioritize collecting information on frequently used toilet spaces if necessary. This allows for the selection of the optimal information collection method by analyzing past usage history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past usage history data into a generative AI and have the generative AI select the optimal information collection method.

[0037] The data collection unit can filter information about toilet spaces based on the user's current location and behavioral patterns. For example, the data collection unit can prioritize collecting information about toilet spaces close to the user's current location. The data collection unit can also analyze the user's behavioral patterns and focus on collecting information about toilet spaces in frequently visited locations. Furthermore, the data collection unit can predict the user's travel route and collect information about toilet spaces along that route. This allows for more appropriate information to be provided by filtering the information based on the user's location and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's location information and behavioral pattern data into a generative AI and have the generative AI perform the information filtering.

[0038] The data collection unit can analyze the user's social media activity and collect relevant information when gathering information on toilet spaces. For example, the data collection unit can collect information on toilet spaces near locations where the user has checked in on social media. The data collection unit can also predict the demand for toilet spaces from content posted by the user on social media and collect relevant information. Furthermore, the data collection unit can collect information on toilet spaces visited by the user's social media followers. In this way, relevant information can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI and have the generative AI perform the collection of relevant information.

[0039] The data collection unit can adjust the collected information on toilet spaces based on past user feedback. For example, the data collection unit can prioritize collecting information on toilet spaces that users have previously given high ratings to. The data collection unit can also exclude information on toilet spaces that users have previously been dissatisfied with. Furthermore, the data collection unit can analyze past user feedback and collect information on toilet spaces that meet specific criteria. This allows for the provision of more appropriate information by adjusting the collected content based on past feedback. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user feedback data into a generative AI and have the generative AI perform the adjustment of the collected content.

[0040] The matching unit can improve the accuracy of matching based on the usage history of toilet spaces during the matching process. For example, the matching unit analyzes the past usage history of toilet spaces and prioritizes matching spaces with high usage frequency. For example, the matching unit can also prioritize matching toilet spaces that are used intensively during specific time periods, based on usage history. Furthermore, the matching unit can consider past usage history and prioritize matching toilet spaces that see increased usage during specific events. This improves the accuracy of matching based on usage history, enabling more appropriate matching. Some or all of the above-described processes in the matching unit may be performed using, for example, a generation AI, or without a generation AI. For example, the matching unit can input toilet space usage history data into a generation AI and have the generation AI perform the matching accuracy improvement.

[0041] The matching unit can select the optimal toilet space based on the user's attribute information during the matching process. For example, the matching unit can select an appropriate toilet space based on the user's age and gender. The matching unit can also select an optimal toilet space based on the user's health condition and special needs. Furthermore, the matching unit can select an optimal toilet space by considering the user's past usage history and feedback. This allows for more appropriate matching by selecting the optimal toilet space based on attribute information. Some or all of the above processing in the matching unit may be performed using, for example, a generation AI, or without a generation AI. For example, the matching unit can input user attribute information data into a generation AI and have the generation AI select the optimal toilet space.

[0042] The matching unit can perform matching based on the geographical distribution of toilet spaces. For example, the matching unit can prioritize matching users with toilet spaces close to their current location. The matching unit can also analyze the geographical distribution of toilet spaces and match users with spaces along their travel route. Furthermore, the matching unit can prioritize matching users with toilet spaces concentrated in specific areas. This allows for more appropriate matching by performing matching based on geographical distribution. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input geographical distribution data of toilet spaces into a generative AI and have the generative AI perform the matching.

[0043] The matching unit can improve the accuracy of matching based on relevant reviews of toilet spaces during the matching process. For example, the matching unit analyzes reviews of toilet spaces and prioritizes matching with highly-rated spaces. The matching unit can also select the optimal toilet space by referring to past user reviews. Furthermore, the matching unit can match spaces that meet specific conditions based on toilet space reviews. This improves the accuracy of matching based on relevant reviews, enabling more appropriate matching. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input toilet space review information into a generative AI and have the generative AI perform the matching accuracy improvement.

[0044] The service provider can select the optimal service method based on the user's past usage history when providing toilet spaces. For example, the service provider may prioritize providing toilet spaces that the user has used in the past. The service provider may also provide toilet spaces that meet specific conditions based on the user's past usage history. Furthermore, the service provider may provide the optimal toilet space by referring to the user's past feedback. By selecting the optimal service method based on past usage history, a more appropriate toilet space can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider may input the user's past usage history data into a generation AI and have the generation AI select the optimal service method.

[0045] The service provider can customize the provision of toilet spaces based on the user's current situation. For example, the service provider can provide the nearest toilet space based on the user's current location. The service provider can also provide the optimal toilet space by considering the user's current health condition. Furthermore, the service provider can provide the optimal toilet space by referring to the user's current schedule. This allows for the provision of more appropriate toilet spaces by customizing the provision method based on the current situation. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's current situation data into a generative AI and have the generative AI perform the customization of the provision method.

[0046] The service provider can select the optimal service method based on the user's geographical location when providing toilet spaces. For example, the service provider may prioritize providing toilet spaces close to the user's current location. The service provider may also predict the user's travel route and provide toilet spaces along that route. Furthermore, the service provider may provide the optimal toilet space based on the user's geographical location. By selecting the optimal service method based on geographical location, a more appropriate toilet space can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's geographical location data into a generative AI and have the generative AI select the optimal service method.

[0047] The service provider can analyze the user's social media activity and propose a suitable toilet space when providing one. For example, the service provider can provide a toilet space near a location where the user has checked in on social media. The service provider can also predict the demand for toilet spaces based on the content posted by the user on social media and provide a suitable space. Furthermore, the service provider can provide the most suitable space based on information about toilet spaces visited by the user's social media followers. In this way, by analyzing social media activity, a more appropriate toilet space can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media data into a generative AI and have the generative AI propose a suitable toilet space.

[0048] The auction unit can select the optimal auction format based on past auction data during an auction. For example, the auction unit can analyze past auction data and select the auction format with the highest success rate. For example, the auction unit can also select an auction format that meets specific conditions based on past auction data. Furthermore, the auction unit can refer to past auction data and select the auction format that best suits the user's needs. This allows for more appropriate auctions to be conducted by selecting the optimal auction format based on past auction data. Some or all of the above processes in the auction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the auction unit can input past auction data into a generative AI and have the generative AI select the optimal auction format.

[0049] The auction unit can select the optimal auction format based on the user's geographical location information during an auction. For example, the auction unit can prioritize auctions for toilet spaces close to the user's current location. The auction unit can also select the optimal auction format based on the user's geographical location information. Furthermore, the auction unit can predict the user's travel route and conduct auctions for toilet spaces along that route. By selecting the optimal auction format based on geographical location information, a more appropriate auction can be conducted. Some or all of the above processing in the auction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the auction unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal auction format.

[0050] The display unit can select the optimal display method based on the user's past operation history when displaying availability. For example, the display unit may prioritize displaying methods that the user has previously preferred. The display unit may also provide display methods that meet specific conditions based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and select the optimal display method. This allows for a more appropriate display of availability by selecting the optimal display method based on past operation history. Some or all of the above-described processes in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input user operation history data into a generation AI and have the generation AI select the optimal display method.

[0051] The display unit can select the optimal display method based on the user's device information when displaying availability. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. By selecting the optimal display method based on device information, a more appropriate display of availability can be achieved. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input user device information data into a generation AI and have the generation AI select the optimal display method.

[0052] The analysis unit can optimize its analysis algorithm based on past pedestrian flow data during pedestrian flow analysis. For example, the analysis unit can analyze past pedestrian flow data and select the most accurate analysis algorithm. The analysis unit can also select an analysis algorithm that meets specific conditions based on past pedestrian flow data. Furthermore, the analysis unit can refer to past pedestrian flow data and select the analysis algorithm that best suits the user's needs. By optimizing the analysis algorithm based on past pedestrian flow data, more accurate pedestrian flow analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past pedestrian flow data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0053] The analysis unit can select the optimal analysis method based on the user's geographical location information when performing pedestrian flow analysis. For example, the analysis unit can prioritize pedestrian flow analysis in areas close to the user's current location. The analysis unit can also select the optimal analysis method based on the user's geographical location information. Furthermore, the analysis unit can predict the user's travel route and perform pedestrian flow analysis in areas along that route. By selecting the optimal analysis method based on geographical location information, more accurate pedestrian flow analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information data into a generative AI and have the generative AI select the optimal analysis method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can monitor the user's health condition while collecting information on toilet spaces and adjust the frequency and content of information collection based on that condition. For example, if the user is feeling unwell, the data collection unit will prioritize collecting information on the nearest clean toilet space. Furthermore, if the user has a specific allergy, the data collection unit can collect information on toilet spaces that do not contain that allergen. Additionally, if the user is tired after a long journey, the data collection unit can collect information on toilet spaces with a relaxing environment. By optimizing information collection based on the user's health condition, more appropriate toilet space information can be provided.

[0056] The matching system learns the user's preferences and tendencies based on their past toilet usage history and can suggest the most suitable toilet space. For example, the matching system analyzes the characteristics of toilet spaces that the user has previously given high ratings to and prioritizes matching with toilet spaces that have similar characteristics. The matching system can also consider information about toilet spaces that the user has avoided in the past and exclude such spaces. Furthermore, the matching system can analyze the user's usage frequency and time of day trends and suggest toilet spaces at the optimal time. This makes it possible to suggest more personalized toilet spaces by utilizing the user's past usage history.

[0057] The service provider can select the most suitable method of providing toilet space, taking into account the user's current schedule. For example, if the user has limited time before their next appointment, the service provider can provide the nearest and most quickly accessible toilet space. Alternatively, if the user plans to take a longer break, the service provider can provide a toilet space with a more relaxing environment. Furthermore, if the user plans to attend a specific event, the service provider can provide a toilet space near the event venue. This allows for a more appropriate service by providing the most suitable toilet space based on the user's schedule.

[0058] The display unit can adjust its display method to show toilet space availability, taking into account the user's device's battery level. For example, if the user's device battery is low, the display unit provides a simple and power-saving display method. If the user's device has sufficient battery power, the display unit can also provide a display method that includes detailed information. Furthermore, if the user's device is charging, the display unit can provide information on the most suitable toilet space while charging is complete. This allows for more appropriate information to be provided by adjusting the display method based on the device's battery status.

[0059] The analysis department can improve the accuracy of its toilet space demand forecasts by considering weather data. For example, the analysis department can predict that the demand for indoor toilet spaces tends to increase during rainy weather and use that information to make demand forecasts. It can also predict that the demand for outdoor toilet spaces tends to increase during sunny weather. Furthermore, the analysis department can understand how the demand for toilet spaces tends to fluctuate depending on specific seasons and temperatures and use that data to make demand forecasts. By utilizing weather data to improve the accuracy of demand forecasts, it becomes possible to provide more appropriate toilet spaces.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects information about the toilet space. For example, the data collection unit collects information such as the location of the toilet space, the available time, and the type of equipment. The data collection unit can monitor the usage status of the toilet space in real time using sensors and collect that information. The data collection unit can also collect feedback from users and collect evaluation information about the toilet space. For example, the data collection unit can provide an interface for users to input an evaluation after using the toilet space and collect that evaluation information. Step 2: The matching unit matches users with toilet spaces based on the information collected by the data collection unit. The matching unit selects the most suitable toilet space based on information such as the user's location, usage history, and preferences. For example, it prioritizes matching users with toilet spaces close to their current location. It can also analyze the user's past usage history and prioritize matching users with frequently used toilet spaces. For example, it prioritizes matching users with toilet spaces that they have previously given high ratings to. Step 3: The service provider provides the user with a toilet space matched by the matching service provider. The service provider provides the user with detailed information about the toilet space and guides them on how to use it. For example, it provides an interface for the user to reserve a toilet space and manages the reservation information. The service provider can also provide support to the user when they use the toilet space. For example, it guides the user when they arrive at the toilet space and explains how to use it.

[0062] (Example of form 2) The matching service for buying and selling toilet spaces according to an embodiment of the present invention is a system that matches users with currently unused toilet spaces by putting them up for auction, thereby completing the sale. The auctioneer collects a fee from both the seller and the bidder. In the first phase, existing facilities are utilized, and shared office spaces are used as shared toilet spaces. In the second phase, the service is deployed in locations with high toilet demand, such as fireworks venues, festivals, and live music venues. In the third phase, this format is expanded to other uses, such as applying makeup, changing clothes, breastfeeding, and napping. This service targets tourists visiting Japan, event attendees, travelers, drivers, affluent individuals who do not want to wait in line, and affluent individuals who do not want to share with others, solving problems such as not being able to find a toilet, long waiting times, being unable to go to places without toilets, and feeling uneasy. Generative AI is used to perform auctions, ratings, person identification, collection of payments, analysis of human flow, demand forecasting, and matching, displaying real-time availability and providing the optimal toilet space. As a result, the matching service for buying and selling toilet spaces can provide toilet spaces to users quickly and efficiently.

[0063] The matching system for buying and selling toilet spaces according to this embodiment comprises a collection unit, a matching unit, and a provision unit. The collection unit collects information about toilet spaces. For example, the collection unit collects information such as the location, availability time, and type of facilities of the toilet space. The collection unit can also monitor the usage status of toilet spaces in real time using sensors and collect that information. The collection unit can also collect feedback from users and collect evaluation information for toilet spaces. For example, the collection unit provides an interface for users to input evaluations after using a toilet space and collects that evaluation information. The matching unit matches users with toilet spaces based on the information collected by the collection unit. For example, the matching unit selects the most suitable toilet space based on information such as the user's location, usage history, and preferences. For example, the matching unit prioritizes matching users with toilet spaces close to their current location. The matching unit can also analyze the user's past usage history and prioritize matching users with frequently used toilet spaces. For example, the matching unit prioritizes matching users with toilet spaces that have received high ratings in the past. The provision unit provides the user with the toilet space matched by the matching unit. The service provider, for example, provides users with detailed information about toilet spaces and guides them on how to use them. The service provider also provides an interface for users to reserve toilet spaces and manages reservation information. Furthermore, the service provider can also provide support to users when they use the toilet spaces. For example, the service provider guides users upon arrival at the toilet space and explains how to use it. In this way, the matching system for buying and selling toilet spaces according to this embodiment can provide toilet spaces to users quickly and efficiently.

[0064] The data collection unit collects information about toilet spaces. For example, it collects information such as the location, availability, and type of facilities. Specifically, the unit uses GPS devices and Wi-Fi location information to pinpoint the exact location of toilet spaces and stores it in a database. Regarding availability, it collects schedule information provided by the toilet space manager and updates it in real time. Regarding the type of facilities, it collects detailed information about the type of toilet installed in the toilet space (Japanese style, Western style, barrier-free access, etc.) and ancillary facilities such as sinks, soap, paper towels, and hand dryers. Furthermore, the data collection unit can use sensors to monitor the usage status of toilet spaces in real time and collect this information. For example, it can install door sensors and motion sensors to detect whether the toilet space is occupied. It can also monitor the environmental conditions of the toilet space using temperature and humidity sensors. This provides information to help users determine whether the toilet space is comfortable to use. Additionally, the data collection unit can collect user feedback and collect evaluation information about the toilet space. For example, it can provide an interface for users to input their evaluations after using the toilet space and collect this evaluation information. The evaluation criteria include cleanliness, the completeness of facilities, and ease of access, and this information serves as useful reference information for other users. The collection department centrally manages this diverse information and stores it in a database, enabling the matching and provision departments to use it efficiently.

[0065] The matching unit matches users with toilet spaces based on information collected by the data collection unit. Specifically, it selects the most suitable toilet space based on information such as the user's location, usage history, and preferences. For example, to prioritize matching users with toilet spaces close to their current location, it acquires GPS data in real time and lists the toilet spaces closest to the user. It can also analyze the user's past usage history and prioritize matching users with frequently used toilet spaces. For example, it can prioritize matching users with toilet spaces that they have previously given high ratings to. This allows users to quickly find toilet spaces that suit their preferences. Furthermore, the matching unit can use AI to learn user preferences and behavior patterns, enabling more accurate matching. For example, if a user tends to use a toilet space at a specific time and location, the unit learns this pattern and reflects it in future matches. It can also evaluate the quality of toilet spaces based on user ratings and prioritize matching users with highly rated toilet spaces. In this way, the matching unit can provide users with the most suitable toilet space and improve user satisfaction.

[0066] The service provider provides users with toilet spaces matched by the matching service provider. Specifically, it provides users with detailed information about the toilet spaces and guides them on how to use them. For example, the service provider provides an interface on the user's smartphone that displays the location, available hours, type of facilities, and rating information of the toilet space. Through this interface, users can check the detailed information about the toilet space and decide whether to use it. The service provider also provides an interface for users to reserve toilet spaces and manages reservation information. For example, if a user wants to use a toilet space during a specific time slot, they can make a reservation in advance through the reservation system. Reservation information is updated in real time and managed to prevent overlapping reservations by other users during the same time slot. Furthermore, the service provider can also provide support to users when they use the toilet space. For example, it guides users when they arrive at the toilet space and explains how to use it. This includes guiding them to the entrance of the toilet space and explaining how to use the facilities. The service provider also provides an interface for users to provide feedback after using the toilet space and collects user ratings. This allows the service provider to provide toilet spaces to users quickly and efficiently and improve user satisfaction.

[0067] The auction unit can conduct sales in an auction format. The auction unit can buy and sell toilet spaces using formats such as English auctions or Dutch auctions. The auction unit can, for example, have users list toilet spaces for sale, and other users bid to complete the sale. The auction unit can also display the progress of the auction in real time, allowing users to check the current bidding status. For example, the auction unit can display the current highest bid and the remaining time, allowing users to decide when to bid. This enables the buying and selling of toilet spaces in an auction format. Some or all of the above processes in the auction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the auction unit can input the progress of the auction into a generative AI and have the generative AI output the auction results.

[0068] The display unit can display real-time availability. For example, the display unit can monitor the usage status of toilet spaces in real time and display that information. For example, the display unit can update the availability status of toilet spaces every second or minute so that users can check the latest information. The display unit can also prioritize displaying the availability status of nearby toilet spaces based on the user's location information. For example, the display unit can display the availability status of toilet spaces close to the user's current location, making it easier for the user to find a toilet space that can be used immediately. This allows for the display of toilet space availability in real time. Some or all of the above processing in the display unit may be performed using, for example, a generating AI, or without a generating AI. For example, the display unit can input the usage status of toilet spaces into a generating AI and have the generating AI output the availability status display.

[0069] The analysis unit can analyze pedestrian flow and forecast demand. For example, the analysis unit can monitor pedestrian flow using sensors and collect the data. For example, the analysis unit can perform tasks such as counting people and analyzing movement patterns to understand trends in pedestrian flow. The analysis unit can also forecast demand based on past data. For example, the analysis unit can analyze past event data and predict the tendency for demand for toilet spaces to increase during specific events. This enables the analysis of pedestrian flow and forecasting demand. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input pedestrian flow data into generative AI and have the generative AI output the results of the demand forecast.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of information collection in the restroom space based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect information frequently and update it in real time. For example, if the user is relaxed, the data collection unit can reduce the frequency of information collection and update only when necessary. Also, if the user is in a hurry, the data collection unit can quickly collect information and provide it immediately. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection 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 data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0071] The data collection unit can analyze the past usage history of toilet spaces and select the optimal information collection method. For example, the data collection unit can analyze trends in usage concentration during specific time periods based on past usage history and focus on collecting information during those times. For example, the data collection unit can identify trends in increased usage on specific days of the week or during events based on usage history and strengthen information collection accordingly. Furthermore, the data collection unit can analyze past usage history and prioritize collecting information on frequently used toilet spaces if necessary. This allows for the selection of the optimal information collection method by analyzing past usage history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past usage history data into a generative AI and have the generative AI select the optimal information collection method.

[0072] The data collection unit can filter information about toilet spaces based on the user's current location and behavioral patterns. For example, the data collection unit can prioritize collecting information about toilet spaces close to the user's current location. The data collection unit can also analyze the user's behavioral patterns and focus on collecting information about toilet spaces in frequently visited locations. Furthermore, the data collection unit can predict the user's travel route and collect information about toilet spaces along that route. This allows for more appropriate information to be provided by filtering the information based on the user's location and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's location information and behavioral pattern data into a generative AI and have the generative AI perform the information filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of toilet space information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information on the most comfortable toilet spaces. If the user is relaxed, the data collection unit can also collect information on a wide range of available toilet spaces. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information on the nearest toilet space. This allows for the provision of more appropriate information by prioritizing 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 processing described above in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0074] The data collection unit can analyze the user's social media activity and collect relevant information when gathering information on toilet spaces. For example, the data collection unit can collect information on toilet spaces near locations where the user has checked in on social media. The data collection unit can also predict the demand for toilet spaces from content posted by the user on social media and collect relevant information. Furthermore, the data collection unit can collect information on toilet spaces visited by the user's social media followers. In this way, relevant information can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI and have the generative AI perform the collection of relevant information.

[0075] The data collection unit can adjust the collected information on toilet spaces based on past user feedback. For example, the data collection unit can prioritize collecting information on toilet spaces that users have previously given high ratings to. The data collection unit can also exclude information on toilet spaces that users have previously been dissatisfied with. Furthermore, the data collection unit can analyze past user feedback and collect information on toilet spaces that meet specific criteria. This allows for the provision of more appropriate information by adjusting the collected content based on past feedback. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user feedback data into a generative AI and have the generative AI perform the adjustment of the collected content.

[0076] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is feeling stressed, the matching unit will prioritize matching with toilet spaces that prioritize comfort. If the user is relaxed, the matching unit can also broaden the range of available toilet spaces. Furthermore, if the user is in a hurry, the matching unit can prioritize matching with the nearest toilet space. This allows for more appropriate matching by adjusting the matching criteria 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 matching unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the matching unit can input user emotion data into a generative AI and have the generative AI adjust the matching criteria.

[0077] The matching unit can improve the accuracy of matching based on the usage history of toilet spaces during the matching process. For example, the matching unit analyzes the past usage history of toilet spaces and prioritizes matching spaces with high usage frequency. For example, the matching unit can also prioritize matching toilet spaces that are used intensively during specific time periods, based on usage history. Furthermore, the matching unit can consider past usage history and prioritize matching toilet spaces that see increased usage during specific events. This improves the accuracy of matching based on usage history, enabling more appropriate matching. Some or all of the above-described processes in the matching unit may be performed using, for example, a generation AI, or without a generation AI. For example, the matching unit can input toilet space usage history data into a generation AI and have the generation AI perform the matching accuracy improvement.

[0078] The matching unit can select the optimal toilet space based on the user's attribute information during the matching process. For example, the matching unit can select an appropriate toilet space based on the user's age and gender. The matching unit can also select an optimal toilet space based on the user's health condition and special needs. Furthermore, the matching unit can select an optimal toilet space by considering the user's past usage history and feedback. This allows for more appropriate matching by selecting the optimal toilet space based on attribute information. Some or all of the above processing in the matching unit may be performed using, for example, a generation AI, or without a generation AI. For example, the matching unit can input user attribute information data into a generation AI and have the generation AI select the optimal toilet space.

[0079] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated emotions. For example, if the user is stressed, the matching unit will display the most comfortable toilet space at the top. If the user is relaxed, the matching unit can also display a wide range of available toilet spaces. Furthermore, if the user is in a hurry, the matching unit can display the nearest toilet space at the top. By adjusting the display order based on the user's emotions, more appropriate matching results 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 matching unit may be performed using a generative AI, or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display order.

[0080] The matching unit can perform matching based on the geographical distribution of toilet spaces. For example, the matching unit can prioritize matching users with toilet spaces close to their current location. The matching unit can also analyze the geographical distribution of toilet spaces and match users with spaces along their travel route. Furthermore, the matching unit can prioritize matching users with toilet spaces concentrated in specific areas. This allows for more appropriate matching by performing matching based on geographical distribution. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input geographical distribution data of toilet spaces into a generative AI and have the generative AI perform the matching.

[0081] The matching unit can improve the accuracy of matching based on relevant reviews of toilet spaces during the matching process. For example, the matching unit analyzes reviews of toilet spaces and prioritizes matching with highly-rated spaces. The matching unit can also select the optimal toilet space by referring to past user reviews. Furthermore, the matching unit can match spaces that meet specific conditions based on toilet space reviews. This improves the accuracy of matching based on relevant reviews, enabling more appropriate matching. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input toilet space review information into a generative AI and have the generative AI perform the matching accuracy improvement.

[0082] The service provider can estimate the user's emotions and adjust how it provides toilet spaces based on those emotions. For example, if the user is stressed, the service provider can quickly provide a toilet space. If the user is relaxed, the service provider can also provide more detailed information and broaden the options. If the user is in a hurry, the service provider can prioritize providing the nearest toilet space. This allows for the provision of more appropriate toilet spaces by adjusting the service 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 service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the service method.

[0083] The service provider can select the optimal service method based on the user's past usage history when providing toilet spaces. For example, the service provider may prioritize providing toilet spaces that the user has used in the past. The service provider may also provide toilet spaces that meet specific conditions based on the user's past usage history. Furthermore, the service provider may provide the optimal toilet space by referring to the user's past feedback. By selecting the optimal service method based on past usage history, a more appropriate toilet space can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider may input the user's past usage history data into a generation AI and have the generation AI select the optimal service method.

[0084] The service provider can customize the provision of toilet spaces based on the user's current situation. For example, the service provider can provide the nearest toilet space based on the user's current location. The service provider can also provide the optimal toilet space by considering the user's current health condition. Furthermore, the service provider can provide the optimal toilet space by referring to the user's current schedule. This allows for the provision of more appropriate toilet spaces by customizing the provision method based on the current situation. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's current situation data into a generative AI and have the generative AI perform the customization of the provision method.

[0085] The service provider can estimate the user's emotions and determine the priority of providing toilet spaces based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing the most comfortable toilet space. If the user is relaxed, the service provider may also provide a wide range of available toilet spaces. Furthermore, if the user is in a hurry, the service provider may prioritize providing the nearest toilet space. This allows for the provision of more appropriate toilet spaces by determining the priority of provision 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 processing described above in the service provider may be performed using a generative AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of provision.

[0086] The service provider can select the optimal service method based on the user's geographical location when providing toilet spaces. For example, the service provider may prioritize providing toilet spaces close to the user's current location. The service provider may also predict the user's travel route and provide toilet spaces along that route. Furthermore, the service provider may provide the optimal toilet space based on the user's geographical location. By selecting the optimal service method based on geographical location, a more appropriate toilet space can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's geographical location data into a generative AI and have the generative AI select the optimal service method.

[0087] The service provider can analyze the user's social media activity and propose a suitable toilet space when providing one. For example, the service provider can provide a toilet space near a location where the user has checked in on social media. The service provider can also predict the demand for toilet spaces based on the content posted by the user on social media and provide a suitable space. Furthermore, the service provider can provide the most suitable space based on information about toilet spaces visited by the user's social media followers. In this way, by analyzing social media activity, a more appropriate toilet space can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media data into a generative AI and have the generative AI propose a suitable toilet space.

[0088] The auction unit can estimate the user's emotions and adjust the auction start time based on the estimated emotions. For example, if the user is stressed, the auction unit can start the auction quickly. For example, if the user is relaxed, the auction unit can flexibly adjust the auction start time. Also, if the user is in a hurry, the auction unit can start the auction immediately. This allows the auction to start at a more appropriate time by adjusting the auction start 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 auction unit may be performed using a generative AI, or not using a generative AI. For example, the auction unit can input user emotion data into a generative AI and have the generative AI adjust the auction start time.

[0089] The auction unit can select the optimal auction format based on past auction data during an auction. For example, the auction unit can analyze past auction data and select the auction format with the highest success rate. For example, the auction unit can also select an auction format that meets specific conditions based on past auction data. Furthermore, the auction unit can refer to past auction data and select the auction format that best suits the user's needs. This allows for more appropriate auctions to be conducted by selecting the optimal auction format based on past auction data. Some or all of the above processes in the auction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the auction unit can input past auction data into a generative AI and have the generative AI select the optimal auction format.

[0090] The auction unit can estimate the user's emotions and determine the priority of auctions based on those emotions. For example, if the user is stressed, the auction unit will prioritize the most important auction. If the user is relaxed, the auction unit can also run multiple auctions simultaneously. Furthermore, if the user is in a hurry, the auction unit can prioritize the auction that can be run most quickly. This allows for more appropriate auctions by prioritizing them 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 auction unit may be performed using or without a generative AI. For example, the auction unit can input user emotion data into a generative AI and have the generative AI determine the auction priorities.

[0091] The auction unit can select the optimal auction format based on the user's geographical location information during an auction. For example, the auction unit can prioritize auctions for toilet spaces close to the user's current location. The auction unit can also select the optimal auction format based on the user's geographical location information. Furthermore, the auction unit can predict the user's travel route and conduct auctions for toilet spaces along that route. By selecting the optimal auction format based on geographical location information, a more appropriate auction can be conducted. Some or all of the above processing in the auction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the auction unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal auction format.

[0092] The display unit can estimate the user's emotions and adjust the display method of availability based on the estimated user emotions. For example, if the user is stressed, the display unit can provide a simple and highly visible display method. For example, if the user is relaxed, the display unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a concise display method. This allows for the display of more appropriate availability by adjusting the display method 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 display unit may be performed using a generative AI, or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0093] The display unit can select the optimal display method based on the user's past operation history when displaying availability. For example, the display unit may prioritize displaying methods that the user has previously preferred. The display unit may also provide display methods that meet specific conditions based on the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and select the optimal display method. This allows for a more appropriate display of availability by selecting the optimal display method based on past operation history. Some or all of the above-described processes in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input user operation history data into a generation AI and have the generation AI select the optimal display method.

[0094] The display unit can estimate the user's emotions and determine the display priority of availability based on the estimated emotions. For example, if the user is stressed, the display unit will prioritize displaying the availability of the most comfortable toilet space. If the user is relaxed, the display unit can also display a wide range of available toilet spaces. Furthermore, if the user is in a hurry, the display unit can prioritize displaying the availability of the nearest toilet space. This allows for the display of more appropriate availability by determining the display priority 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 display unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the display priority.

[0095] The display unit can select the optimal display method based on the user's device information when displaying availability. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. By selecting the optimal display method based on device information, a more appropriate display of availability can be achieved. Some or all of the above processing in the display unit may be performed using, for example, a generation AI, or without a generation AI. For example, the display unit can input user device information data into a generation AI and have the generation AI select the optimal display method.

[0096] The analysis unit can estimate the user's emotions and adjust the criteria for pedestrian flow analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can perform a rapid pedestrian flow analysis and provide the results. For example, if the user is relaxed, the analysis unit can also perform a detailed pedestrian flow analysis and provide the results. Furthermore, if the user is in a hurry, the analysis unit can perform a concise pedestrian flow analysis and provide the results. This allows for more appropriate pedestrian flow analysis by adjusting the criteria for pedestrian flow analysis 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-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the pedestrian flow analysis criteria.

[0097] The analysis unit can optimize its analysis algorithm based on past pedestrian flow data during pedestrian flow analysis. For example, the analysis unit can analyze past pedestrian flow data and select the most accurate analysis algorithm. The analysis unit can also select an analysis algorithm that meets specific conditions based on past pedestrian flow data. Furthermore, the analysis unit can refer to past pedestrian flow data and select the analysis algorithm that best suits the user's needs. By optimizing the analysis algorithm based on past pedestrian flow data, more accurate pedestrian flow analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past pedestrian flow data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0098] The analysis unit can estimate the user's emotions and determine the priority of demand forecasts based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize the most important demand forecasts. If the user is relaxed, the analysis unit can perform multiple demand forecasts simultaneously. If the user is in a hurry, the analysis unit can also prioritize the demand forecasts that can be performed most quickly. This allows for more accurate demand forecasts by prioritizing demand forecasts 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 analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of demand forecasts.

[0099] The analysis unit can select the optimal analysis method based on the user's geographical location information when performing pedestrian flow analysis. For example, the analysis unit can prioritize pedestrian flow analysis in areas close to the user's current location. The analysis unit can also select the optimal analysis method based on the user's geographical location information. Furthermore, the analysis unit can predict the user's travel route and perform pedestrian flow analysis in areas along that route. By selecting the optimal analysis method based on geographical location information, more accurate pedestrian flow analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information data into a generative AI and have the generative AI select the optimal analysis method.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can monitor the user's health condition while collecting information on toilet spaces and adjust the frequency and content of information collection based on that condition. For example, if the user is feeling unwell, the data collection unit will prioritize collecting information on the nearest clean toilet space. Furthermore, if the user has a specific allergy, the data collection unit can collect information on toilet spaces that do not contain that allergen. Additionally, if the user is tired after a long journey, the data collection unit can collect information on toilet spaces with a relaxing environment. By optimizing information collection based on the user's health condition, more appropriate toilet space information can be provided.

[0102] The matching system learns the user's preferences and tendencies based on their past toilet usage history and can suggest the most suitable toilet space. For example, the matching system analyzes the characteristics of toilet spaces that the user has previously given high ratings to and prioritizes matching with toilet spaces that have similar characteristics. The matching system can also consider information about toilet spaces that the user has avoided in the past and exclude such spaces. Furthermore, the matching system can analyze the user's usage frequency and time of day trends and suggest toilet spaces at the optimal time. This makes it possible to suggest more personalized toilet spaces by utilizing the user's past usage history.

[0103] The service provider can select the most suitable method of providing toilet space, taking into account the user's current schedule. For example, if the user has limited time before their next appointment, the service provider can provide the nearest and most quickly accessible toilet space. Alternatively, if the user plans to take a longer break, the service provider can provide a toilet space with a more relaxing environment. Furthermore, if the user plans to attend a specific event, the service provider can provide a toilet space near the event venue. This allows for a more appropriate service by providing the most suitable toilet space based on the user's schedule.

[0104] The display unit can adjust its display method to show toilet space availability, taking into account the user's device's battery level. For example, if the user's device battery is low, the display unit provides a simple and power-saving display method. If the user's device has sufficient battery power, the display unit can also provide a display method that includes detailed information. Furthermore, if the user's device is charging, the display unit can provide information on the most suitable toilet space while charging is complete. This allows for more appropriate information to be provided by adjusting the display method based on the device's battery status.

[0105] The analysis department can improve the accuracy of its toilet space demand forecasts by considering weather data. For example, the analysis department can predict that the demand for indoor toilet spaces tends to increase during rainy weather and use that information to make demand forecasts. It can also predict that the demand for outdoor toilet spaces tends to increase during sunny weather. Furthermore, the analysis department can understand how the demand for toilet spaces tends to fluctuate depending on specific seasons and temperatures and use that data to make demand forecasts. By utilizing weather data to improve the accuracy of demand forecasts, it becomes possible to provide more appropriate toilet spaces.

[0106] The data collection unit can estimate the user's emotions and adjust the timing of information collection in the restroom space based on those emotions. For example, if the user is stressed, the unit will collect information frequently and update it in real time. If the user is relaxed, for example, the unit can reduce the frequency of information collection and update only when necessary. Also, if the user is in a hurry, the unit can quickly collect information and provide it immediately. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions.

[0107] The matching unit can estimate the user's emotions and adjust the matching criteria based on those emotions. For example, if the user is feeling stressed, the matching unit will prioritize matching them with a toilet space that prioritizes comfort. If the user is relaxed, the matching unit can also broaden the range of available toilet spaces. Furthermore, if the user is in a hurry, the matching unit can prioritize matching them with the nearest toilet space. By adjusting the matching criteria based on the user's emotions, more appropriate matching becomes possible.

[0108] The service provider can estimate the user's emotions and adjust how toilet spaces are provided based on those estimates. For example, if the user is feeling stressed, the service provider can quickly provide a toilet space. If the user is relaxed, the service provider can also provide more detailed information and a wider range of options. Furthermore, if the user is in a hurry, the service provider can prioritize providing the nearest toilet space. In this way, by adjusting the service provider's approach based on the user's emotions, a more appropriate toilet space can be provided.

[0109] The auction system can estimate the user's emotions and adjust the auction start time based on that estimation. For example, if the user is stressed, the auction system will start the auction quickly. If the user is relaxed, the auction system can also flexibly adjust the auction start time. Furthermore, if the user is in a hurry, the auction system can start the auction immediately. By adjusting the auction start time based on the user's emotions, the auction can be started at a more appropriate time.

[0110] The display unit can estimate the user's emotions and adjust the display method of availability based on those emotions. For example, if the user is stressed, the display unit provides a simple and highly visible display method. If the user is relaxed, the display unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a concise display method. By adjusting the display method based on the user's emotions, a more appropriate availability information can be displayed.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects information about the toilet space. For example, the data collection unit collects information such as the location of the toilet space, the available time, and the type of equipment. The data collection unit can monitor the usage status of the toilet space in real time using sensors and collect that information. The data collection unit can also collect feedback from users and collect evaluation information about the toilet space. For example, the data collection unit can provide an interface for users to input an evaluation after using the toilet space and collect that evaluation information. Step 2: The matching unit matches users with toilet spaces based on the information collected by the data collection unit. The matching unit selects the most suitable toilet space based on information such as the user's location, usage history, and preferences. For example, it prioritizes matching users with toilet spaces close to their current location. It can also analyze the user's past usage history and prioritize matching users with frequently used toilet spaces. For example, it prioritizes matching users with toilet spaces that they have previously given high ratings to. Step 3: The service provider provides the user with a toilet space matched by the matching service provider. The service provider provides the user with detailed information about the toilet space and guides them on how to use it. For example, it provides an interface for the user to reserve a toilet space and manages the reservation information. The service provider can also provide support to the user when they use the toilet space. For example, it guides the user when they arrive at the toilet space and explains how to use it.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, matching unit, provision unit, auction unit, display unit, and analysis unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on toilet spaces using the camera 42 and sensors of the smart device 14 and processes that information with the control unit 46A. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches users with toilet spaces based on the collected information. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides users with detailed information on toilet spaces. The auction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and conducts buying and selling in an auction format. The display unit is implemented, for example, by the display 40A of the smart device 14 and displays real-time availability. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes pedestrian flow and forecasts demand. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, matching unit, provision unit, auction unit, display unit, and analysis 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 collects information on toilet spaces using the camera 42 and sensors of the smart glasses 214 and processes that information with the control unit 46A. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and matches users with toilet spaces based on the collected information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides users with detailed information on toilet spaces. The auction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and conducts buying and selling in an auction format. The display unit is implemented, for example, by the display of the smart glasses 214 and displays real-time availability. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes pedestrian flow and forecasts demand. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, matching unit, provision unit, auction unit, display unit, and analysis unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on toilet spaces using the camera 42 and sensors of the headset terminal 314 and processes that information with the control unit 46A. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and matches users with toilet spaces based on the collected information. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides users with detailed information on toilet spaces. The auction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and conducts buying and selling in an auction format. The display unit is implemented, for example, by the display 343 of the headset terminal 314 and displays real-time availability. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes pedestrian flow and forecasts demand. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In 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.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 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.

[0165] Each of the multiple elements described above, including the collection unit, matching unit, provision unit, auction unit, display unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information on toilet spaces using the camera 42 and sensors of the robot 414 and processes that information with the control unit 46A. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and matches users with toilet spaces based on the collected information. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides users with detailed information on toilet spaces. The auction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and conducts buying and selling in an auction format. The display unit is implemented, for example, by the display of the robot 414 and displays real-time availability. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes pedestrian flow and forecasts demand. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that collects information on toilet spaces, A matching unit that matches users with toilet spaces based on the information collected by the aforementioned collection unit, The system includes a providing unit that provides the user with a toilet space matched by the matching unit. A system characterized by the following features. (Note 2) It has an auction section for buying and selling in an auction format. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a display unit that shows real-time availability. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has an analysis department that analyzes pedestrian traffic and performs demand forecasting. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of information collection for the toilet area based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past usage history of toilet spaces and select the most suitable method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting information about toilet spaces, 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 8) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the toilet space information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information about toilet spaces, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information about toilet spaces, the collected data is adjusted based on past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 11) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The matching unit is During the matching process, we improve the accuracy of matching based on the usage history of the toilet space. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is During the matching process, the system selects the most suitable toilet space based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is It estimates the user's emotions and adjusts the display order of matching results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is Matching is performed based on the geographical distribution of toilet spaces. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is During the matching process, we improve the accuracy of matching based on relevant reviews of toilet spaces. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the toilet space is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing toilet facilities, the optimal method of provision is selected based on the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing toilet facilities, customize the provision method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of providing toilet spaces based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing toilet facilities, the optimal method of provision is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing toilet facilities, we analyze users' social media activity and propose methods for providing them. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned auction department, The system estimates user sentiment and adjusts the auction start time based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned auction department, During the auction, the optimal auction format is selected based on past auction data. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned auction department, The system estimates user sentiment and determines auction priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned auction department, During the auction, the most suitable auction format is selected based on the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned display unit is The system estimates the user's emotions and adjusts how availability is displayed based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying availability, the system selects the optimal display method based on the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned display unit is The system estimates the user's emotions and determines the display priority of availability based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying availability, the system selects the optimal display method based on the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned analysis unit is We estimate user sentiment and adjust the criteria for pedestrian flow analysis based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned analysis unit is When analyzing pedestrian flow, the analysis algorithm is optimized based on past pedestrian flow data. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned analysis unit is It estimates user sentiment and determines demand forecast priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned analysis unit is When analyzing human traffic, the optimal analysis method is selected based on the user's geographical location information. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0185] 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 unit that collects information on toilet spaces, A matching unit that matches users with toilet spaces based on the information collected by the aforementioned collection unit, The system includes a providing unit that provides the user with a toilet space matched by the matching unit. A system characterized by the following features.

2. It has an auction section for buying and selling in an auction format. The system according to feature 1.

3. It is equipped with a display unit that shows real-time availability. The system according to feature 1.

4. It has an analysis department that analyzes pedestrian traffic and performs demand forecasting. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of information collection for the toilet area based on the estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze past usage history of toilet spaces and select the most suitable method for collecting information. The system according to feature 1.

7. The aforementioned collection unit is When collecting information about toilet spaces, filtering is performed based on the user's current location and behavioral patterns. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and prioritizes the toilet space information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When gathering information about toilet spaces, we analyze users' social media activity and collect relevant information. The system according to feature 1.

10. The aforementioned collection unit is When collecting information about toilet spaces, the collected data is adjusted based on past user feedback. The system according to feature 1.

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