system

Abstract

The system according to this embodiment aims to efficiently manage the use of multipurpose toilets and ensure that those who need them can use them appropriately. [Solution] The system according to the embodiment comprises a collection unit, an authentication unit, a guidance unit, and an advertising unit. The collection unit collects information on available toilets. The authentication unit performs reservation authentication based on the user's location information and attributes, based on the information collected by the collection unit. The guidance unit provides route guidance to the toilet based on the reservation authenticated by the authentication unit. The advertising unit delivers advertisements to users guided by the guidance unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that there was no appropriate reservation method for using a multi-purpose toilet, and there were cases where necessary people could not use it.

[0005] The system according to the embodiment aims to efficiently manage the use of a multi-purpose toilet so that necessary people can use it appropriately.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an authentication unit, a guidance unit, and an advertising unit. The data collection unit collects information on available toilets. The authentication unit performs reservation authentication based on the user's location information and attributes, based on the information collected by the data collection unit. The guidance unit provides route guidance to the toilet based on the reservation authenticated by the authentication unit. The advertising unit delivers advertisements to users guided by the guidance unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the use of multipurpose toilets and ensure that those who need them can use them appropriately. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 smart toilet network reservation system according to an embodiment of the present invention is a system that uses AI to streamline the reservation and use of multipurpose toilets. This system includes functions such as checking and securing registered available toilets, minute-by-minute reservation authentication considering arrival time and priority based on the user's location information and attributes, route guidance to the location, and advertising distribution as compensation to multipurpose toilet providers. These functions are realized using AI and are installed in an IoT door lock and a smartphone app. This provides a comfortable smart toilet network reservation system. For example, a user checks the availability of multipurpose toilets using a smartphone app. The app displays the availability information of registered toilets in real time. For example, a user can search for the nearest available toilet from their current location. Next, reservation authentication is performed considering arrival time and priority based on the user's location information and attributes. For example, reservations are authenticated preferentially for users with specific attributes, such as wheelchair users and the elderly. The AI ​​analyzes the user's location information and predicts the time until arrival. This allows for toilet reservations at the appropriate time. Furthermore, the app provides route guidance to the user to the toilet. For example, it displays the shortest route from the user's current location to the toilet and provides navigation. This allows the user to arrive at the toilet without getting lost. Furthermore, the app displays advertisements as compensation to multi-purpose toilet providers. For example, relevant advertisements are displayed when a user uses a toilet. This allows providers to earn advertising revenue. This system eliminates the problem of inappropriate users preventing those who genuinely need the toilets from using them. Users can easily reserve toilets through the smartphone app and use them comfortably. In addition, providers can cover the maintenance costs of the toilets by earning advertising revenue. Thus, the smart toilet network reservation system provides an environment where users can use toilets comfortably and also serves as a means for providers to generate revenue.

[0029] The smart toilet network reservation system according to this embodiment comprises a data collection unit, an authentication unit, a guidance unit, and an advertising unit. The data collection unit collects information on available toilets. The data collection unit can collect information such as the location of the toilet, the available time, and the cleaning status. For example, the data collection unit collects toilet location information using GPS data. The data collection unit can also update the available time of the toilet in real time. Furthermore, the data collection unit can also grasp the cleaning status of the toilet using sensor information. For example, the data collection unit displays the cleaning status of the toilet in real time based on data from sensors. The authentication unit performs reservation authentication considering the user's location information and attributes based on the information collected by the data collection unit. For example, the authentication unit obtains the user's location information using GPS data and performs reservation authentication considering the user's attributes. For example, the authentication unit can prioritize reservations for users with specific attributes, such as wheelchair users or the elderly. The authentication unit analyzes the user's location information using AI and predicts the time until arrival. For example, the authentication unit can predict the arrival time based on the user's location information and reserve the toilet at an appropriate time. The guidance unit provides route guidance to the toilet based on the reservation authenticated by the authentication unit. The guidance unit can, for example, display the shortest route from the user's current location to the toilet. The guidance unit uses GPS data to determine the user's current location and calculate the shortest route. For example, the guidance unit can navigate the user to the toilet via the shortest route from their current location. The advertising unit delivers advertisements to users guided by the guidance unit. For example, the advertising unit can display relevant advertisements when the user is using the toilet. The advertising unit selects and displays appropriate advertisements based on the user's attributes and location information. For example, the advertising unit can display advertisements for relevant products when the user is using the toilet. As a result, the smart toilet network reservation system according to this embodiment can efficiently perform tasks such as checking and securing available toilets, reservation authentication based on the user's location information and attributes, route guidance, and advertisement delivery.

[0030] The data collection unit can collect information on available toilets. For example, the data collection unit can collect toilet location information using GPS data. For example, the data collection unit can update toilet location information in real time. The data collection unit can also collect the available time of toilets. For example, the data collection unit can display the available time of toilets in real time. Furthermore, the data collection unit can understand the cleaning status of toilets using sensor information. For example, the data collection unit can display the cleaning status of toilets in real time based on data from sensors. As a result, the data collection unit can collect information on available toilets in real time and provide it to users.

[0031] The authentication unit may include an attribute setting unit for setting user attributes. The attribute setting unit can set attributes such as the user's age, gender, and physical characteristics. For example, the attribute setting unit can set the user's age. It can also set the user's gender. Furthermore, the attribute setting unit can set the user's physical characteristics. For example, the attribute setting unit can set specific attributes such as wheelchair users or elderly individuals. This allows the authentication unit to perform more appropriate reservation authentication by setting the user's attributes.

[0032] The authentication unit may include a prediction unit that predicts the arrival time. The prediction unit can predict the arrival time based on, for example, the user's location information. For example, the prediction unit can obtain the user's location information using GPS data and predict the time until arrival. The prediction unit can also predict the arrival time by considering the user's speed and traffic conditions. For example, the prediction unit can predict the arrival time based on the user's speed. Furthermore, the prediction unit can also predict the arrival time by considering traffic conditions. For example, the prediction unit can predict the arrival time based on information such as traffic congestion and road construction. As a result, the authentication unit can predict the user's arrival time, enabling timely reservation authentication.

[0033] The guidance unit can display the route from the user's current location to the restroom. For example, the guidance unit can display the shortest route from the user's current location to the restroom. For example, the guidance unit uses GPS data to determine the user's current location and calculates the shortest route. The guidance unit can also customize the route considering the user's mode of transportation (walking, wheelchair, etc.). For example, if the user is walking, the guidance unit will suggest a pedestrian-only route. Furthermore, if the user is in a wheelchair, the guidance unit can suggest a barrier-free route. For example, if the user is in a wheelchair, the guidance unit will display a barrier-free route. In this way, the guidance unit can guide the user so that they can reach the restroom via the shortest route.

[0034] The advertising department can display advertisements when users use the restroom. For example, the advertising department can display relevant advertisements when users use the restroom. For example, the advertising department can select and display appropriate advertisements based on the user's attributes and location information. The advertising department can also customize advertisements based on the user's past purchase history and current areas of interest. For example, the advertising department can display advertisements related to products the user has purchased in the past. Furthermore, the advertising department can display advertisements based on the user's current areas of interest. For example, the advertising department can display advertisements related to the user's areas of interest based on the user's recent search history. In this way, the advertising department can earn compensation from businesses by displaying relevant advertisements when users use the restroom.

[0035] The data collection unit can display registered toilet availability information. For example, the data collection unit can display toilet availability information in real time. For example, the data collection unit can update toilet availability information in real time and provide it to users. The data collection unit can also analyze toilet usage history and predict availability based on user trends. For example, the data collection unit can predict toilets that are likely to become available during specific time periods based on past usage history. Furthermore, the data collection unit can collect toilet sensor information and understand the toilet's cleaning status and equipment condition in real time. For example, the data collection unit can display the cleaning status in real time based on toilet sensor information. This allows the data collection unit to display toilet availability information in real time, enabling users to quickly check for available toilets.

[0036] The data collection unit can analyze toilet usage history and predict toilet availability based on user trends. For example, it can predict toilets that are likely to be available during specific time periods based on past usage history. The data collection unit can also analyze user trends and predict toilets that are likely to be available on specific days of the week. For example, it can predict toilets that are likely to be available on specific days of the week based on user trends. Furthermore, the data collection unit can predict toilets that are likely to be available during specific events based on usage history. For example, it can predict toilets that are likely to be available during specific events. This allows the data collection unit to predict availability based on user trends and provide more accurate information.

[0037] The data collection unit can collect sensor information from toilets and understand the cleaning status and equipment condition of the toilets. For example, the data collection unit can collect sensor information from toilets and display the cleaning status in real time. For example, the data collection unit can collect cleaning status from toilet sensors and display toilets that need cleaning in real time. The data collection unit can also collect the equipment condition of toilets from sensor information and notify of malfunctions or abnormalities in real time. For example, the data collection unit can collect the equipment condition of toilets from sensor information and notify of malfunctions or abnormalities in real time. Furthermore, the data collection unit can collect toilet usage information from sensor information and display the congestion level in real time. For example, the data collection unit can collect toilet usage information from sensor information and display the congestion level in real time. As a result, the data collection unit can understand the cleaning status and equipment condition of toilets in real time and provide appropriate information to users.

[0038] The data collection unit can adjust the information it collects when gathering information on toilet availability, taking into account the surrounding congestion level. For example, the data collection unit can analyze the surrounding congestion level and prioritize collecting information on toilets that are not crowded. For example, the data collection unit can analyze the surrounding congestion level and prioritize collecting information on toilets that are not crowded. The data collection unit can also take surrounding event information into account and prioritize collecting information on toilets when events are being held. For example, the data collection unit can take surrounding event information into account and prioritize collecting information on toilets when events are being held. Furthermore, the data collection unit can take surrounding traffic conditions into account and prioritize collecting information on toilets that are easily accessible. For example, the data collection unit can take surrounding traffic conditions into account and prioritize collecting information on toilets that are easily accessible. In this way, the data collection unit can adjust the information taking surrounding congestion levels into account and provide more appropriate toilet information.

[0039] The data collection unit can analyze the user attributes of toilets when collecting toilet availability information and prioritize collecting availability information for specific attributes. For example, the data collection unit can prioritize collecting toilet information for wheelchair users. For example, the data collection unit analyzes the user attributes of toilets and prioritizes collecting toilet information for wheelchair users. The data collection unit can also prioritize collecting toilet information for the elderly. For example, the data collection unit prioritizes collecting toilet information for the elderly. Furthermore, the data collection unit can prioritize collecting toilet information for families with children. For example, the data collection unit prioritizes collecting toilet information for families with children. In this way, the data collection unit can prioritize collecting availability information for specific attributes and provide more appropriate toilet information.

[0040] The authentication unit can analyze a user's past usage history and select an authentication method. For example, the authentication unit can select the optimal authentication method based on the authentication methods the user has used in the past. For example, the authentication unit analyzes the user's past usage history and selects the optimal authentication method based on the authentication methods the user has used in the past. The authentication unit can also select an authentication method suitable for a specific time period based on the user's past usage history. For example, the authentication unit selects an authentication method suitable for a specific time period based on the user's past usage history. Furthermore, the authentication unit can analyze the user's past usage history and select the most efficient authentication method. For example, the authentication unit selects the most efficient authentication method based on the user's past usage history. In this way, the authentication unit can select the optimal authentication method by analyzing the user's past usage history.

[0041] The authentication unit can determine the authentication priority based on the user's health status and urgency during authentication. For example, if the user is in poor health, the authentication unit can increase the authentication priority. For instance, the authentication unit evaluates the user's health status and increases the authentication priority if the user is in poor health. The authentication unit can also maximize the authentication priority if the user's situation is urgent. For example, the authentication unit evaluates the user's urgency and maximizes the authentication priority if it is urgent. Furthermore, the authentication unit can select the optimal authentication method considering the user's health status and urgency. For example, the authentication unit selects the optimal authentication method based on the user's health status and urgency. This allows the authentication unit to perform more appropriate authentication by considering the user's health status and urgency.

[0042] The authentication unit can select an authentication method based on the user's device information during authentication. For example, if the user is using a smartphone, the authentication unit can select the most suitable authentication method for the smartphone. For example, the authentication unit selects the most suitable authentication method for the smartphone based on the user's device information. The authentication unit can also select the most suitable authentication method for a tablet if the user is using a tablet. For example, the authentication unit selects the most suitable authentication method for the tablet based on the user's device information. Furthermore, if the user is using a smartwatch, the authentication unit can select the most suitable authentication method for the smartwatch. For example, the authentication unit selects the most suitable authentication method for the smartwatch based on the user's device information. In this way, the authentication unit can select the most suitable authentication method by taking the user's device information into consideration.

[0043] The authentication unit can analyze the user's social media activity during authentication and perform authentication based on relevant information. For example, the authentication unit can analyze the current situation from the user's social media activity and select the optimal authentication method. For example, the authentication unit analyzes the current situation based on the user's social media activity and selects the optimal authentication method. The authentication unit can also determine the urgency of the situation from the user's social media activity and decide on the priority of authentication. For example, the authentication unit determines the urgency of the situation based on the user's social media activity and decides on the priority of authentication. Furthermore, the authentication unit can simplify the authentication procedure based on the user's social media activity. For example, the authentication unit simplifies the authentication procedure based on the user's social media activity. As a result, the authentication unit can perform more appropriate authentication by analyzing the user's social media activity.

[0044] The navigation system can suggest routes by referring to the user's past travel history. For example, the navigation system can suggest the optimal route based on routes the user has used in the past. For example, the navigation system can suggest the optimal route based on the user's past travel history. The navigation system can also suggest routes that avoid congestion based on the user's past travel history. For example, the navigation system can suggest routes that avoid congestion based on the user's past travel history. Furthermore, the navigation system can analyze the user's past travel history and suggest the most efficient route. For example, the navigation system can suggest the most efficient route based on the user's past travel history. In this way, the navigation system can suggest the optimal route by referring to the user's past travel history.

[0045] The guidance unit can customize the route based on the user's mode of transportation. For example, if the user is walking, the guidance unit can suggest a pedestrian-only route. For example, based on the user's mode of transportation, the guidance unit will suggest a pedestrian-only route if the user is walking. The guidance unit can also suggest a barrier-free route if the user is using a wheelchair. For example, based on the user's mode of transportation, the guidance unit will suggest a barrier-free route if the user is using a wheelchair. Furthermore, if the user is using a bicycle, the guidance unit can suggest a bicycle-only route. For example, based on the user's mode of transportation, the guidance unit will suggest a bicycle-only route if the user is using a bicycle. In this way, the guidance unit can provide the optimal route according to the user's mode of transportation.

[0046] The guidance unit can select a guidance method based on the user's device information when providing guidance. For example, if the user is using a smartphone, the guidance unit can provide the guidance method best suited for a smartphone. For example, the guidance unit provides the guidance method best suited for a smartphone based on the user's device information. The guidance unit can also provide the guidance method best suited for a tablet if the user is using a tablet. For example, the guidance unit provides the guidance method best suited for a tablet based on the user's device information. Furthermore, if the user is using a smartwatch, the guidance unit can provide the guidance method best suited for a smartwatch. For example, the guidance unit provides the guidance method best suited for a smartwatch based on the user's device information. In this way, the guidance unit can provide the optimal guidance method according to the user's device information.

[0047] The guidance system can suggest routes based on the user's geographical location. For example, it can suggest a route to the nearest restroom from the user's current location. It can also suggest a route to the most easily accessible restroom from the user's current location. Furthermore, it can suggest a route to the least crowded restroom from the user's current location. In this way, the guidance system can provide the optimal route based on the user's geographical location.

[0048] The advertising department can select ads by referring to the user's past purchase history when displaying ads. For example, the advertising department can display ads related to products the user has previously purchased. For example, the advertising department can display ads related to products the user has previously purchased based on the user's past purchase history. The advertising department can also display ads that suggest products the user might be interested in based on the user's past purchase history. For example, the advertising department can display ads that suggest products the user might be interested in based on the user's past purchase history. Furthermore, the advertising department can analyze the user's purchase history and display ads related to specific brands or categories. For example, the advertising department can display ads related to specific brands or categories based on the user's purchase history. In this way, the advertising department can display the most suitable ads by referring to the user's past purchase history.

[0049] The advertising department can customize ads based on the user's current areas of interest when displaying them. For example, the advertising department can display ads related to areas the user is currently interested in. For example, the advertising department can display ads related to areas the user is interested in based on the user's current areas of interest. The advertising department can also display ads related to areas of interest based on the user's recent search history. For example, the advertising department can display ads related to areas of interest based on the user's recent search history. Furthermore, the advertising department can analyze areas of interest from the user's social media activity and display relevant ads. For example, the advertising department can analyze areas of interest from the user's social media activity and display relevant ads. This allows the advertising department to display ads based on the user's areas of interest, enabling more effective ad display.

[0050] The advertising department can select ads based on the user's device information when displaying them. For example, if the user is using a smartphone, the advertising department can display ads optimized for smartphones. For example, the advertising department can display ads optimized for smartphones based on the user's device information. The advertising department can also display ads optimized for tablets if the user is using a tablet. For example, the advertising department can display ads optimized for tablets based on the user's device information. Furthermore, if the user is using a smartwatch, the advertising department can display ads optimized for smartwatches. For example, the advertising department can display ads optimized for smartwatches based on the user's device information. In this way, the advertising department can display the most suitable ads according to the user's device information.

[0051] The advertising department can display ads based on the user's geographical location. For example, it can display ads for nearby stores and services based on the user's current location. The advertising department can also display region-specific promotional ads based on the user's geographical location. Furthermore, the advertising department can display ads for nearby events and activities, taking the user's location into consideration. This allows the advertising department to display more relevant ads based on the user's geographical location, enabling more effective ad display.

[0052] The attribute setting unit can set attributes by referring to the user's past usage history when setting attributes. For example, the attribute setting unit can automatically set the optimal attributes based on the attributes the user has set in the past. For example, the attribute setting unit automatically sets the optimal attributes based on the user's past usage history and the attributes they have set in the past. The attribute setting unit can also set attributes suitable for a specific time period based on the user's past usage history. For example, the attribute setting unit sets attributes suitable for a specific time period based on the user's past usage history. Furthermore, the attribute setting unit can analyze the user's past usage history and set the most efficient attributes. For example, the attribute setting unit sets the most efficient attributes based on the user's past usage history. In this way, the attribute setting unit can set the optimal attributes by referring to the user's past usage history.

[0053] The attribute setting unit can set attributes based on the user's device information when setting attributes. For example, if the user is using a smartphone, the attribute setting unit can set attributes optimized for smartphones. For example, the attribute setting unit sets attributes optimized for smartphones based on the user's device information. The attribute setting unit can also set attributes optimized for tablets if the user is using a tablet. For example, the attribute setting unit sets attributes optimized for tablets based on the user's device information. Furthermore, if the user is using a smartwatch, the attribute setting unit can also set attributes optimized for smartwatches. For example, the attribute setting unit sets attributes optimized for smartwatches based on the user's device information. In this way, the attribute setting unit can set optimal attributes according to the user's device information.

[0054] The prediction unit can select a prediction method by referring to the user's past travel history during prediction. For example, the prediction unit can select the optimal prediction method based on routes the user has used in the past. For example, the prediction unit selects the optimal prediction method based on routes the user has used in the past, based on the user's past travel history. The prediction unit can also select a prediction method that avoids congestion based on the user's past travel history. For example, the prediction unit selects a prediction method that avoids congestion based on the user's past travel history. Furthermore, the prediction unit can analyze the user's past travel history and select the most efficient prediction method. For example, the prediction unit selects the most efficient prediction method based on the user's past travel history. In this way, the prediction unit can select the optimal prediction method by referring to the user's past travel history.

[0055] The prediction unit can select a prediction method based on the user's device information during prediction. For example, if the user is using a smartphone, the prediction unit can select the prediction method best suited for the smartphone. For example, the prediction unit selects the prediction method best suited for the smartphone based on the user's device information. The prediction unit can also select the prediction method best suited for the tablet if the user is using a tablet. For example, the prediction unit selects the prediction method best suited for the tablet based on the user's device information. Furthermore, if the user is using a smartwatch, the prediction unit can select the prediction method best suited for the smartwatch. For example, the prediction unit selects the prediction method best suited for the smartwatch based on the user's device information. In this way, the prediction unit can select the optimal prediction method according to the user's device information.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The smart toilet network reservation system can also be equipped with a health monitoring unit that monitors the user's health status. The health monitoring unit can, for example, measure the user's heart rate and blood pressure, and take emergency action if an abnormality is detected. For instance, if the user's heart rate suddenly increases, the health monitoring unit can provide guidance to the nearest medical facility. Furthermore, if the user's blood pressure shows an abnormal value, the health monitoring unit can temporarily restrict toilet use and prompt the user to contact a medical facility. In addition, the health monitoring unit can adjust toilet usage priority based on the user's health status. For example, if the user's health condition deteriorates, the health monitoring unit can prioritize toilet use. This allows the smart toilet network reservation system to provide a safer toilet environment that takes the user's health status into consideration.

[0058] The data collection unit can also collect feedback from toilet users. For example, it can conduct surveys with users after they use the toilets to collect evaluations regarding cleanliness and ease of use. Furthermore, the data collection unit can identify areas for improvement in the toilets based on user feedback and notify the operators. For instance, it can analyze user feedback and notify operators that certain toilets are not being cleaned properly. Additionally, the data collection unit can update toilet usage status in real time based on user feedback. For example, it can notify other users that certain toilets are crowded based on user feedback. This allows the data collection unit to utilize user feedback to improve the toilet environment and create a more comfortable toilet experience.

[0059] The data collection unit can also collect feedback from toilet users. For example, it can conduct surveys with users after they use the toilets to collect evaluations regarding cleanliness and ease of use. Furthermore, the data collection unit can identify areas for improvement in the toilets based on user feedback and notify the operators. For instance, it can analyze user feedback and notify operators that certain toilets are not being cleaned properly. Additionally, the data collection unit can update toilet usage status in real time based on user feedback. For example, it can notify other users that certain toilets are crowded based on user feedback. This allows the data collection unit to utilize user feedback to improve the toilet environment and create a more comfortable toilet experience.

[0060] The authentication unit may also include a health monitoring unit that monitors the user's health status. The health monitoring unit can, for example, measure the user's heart rate and blood pressure, and take emergency action if an abnormality is detected. For example, if the user's heart rate suddenly increases, the health monitoring unit can provide guidance to the nearest medical facility. The health monitoring unit can also temporarily restrict toilet use and prompt the user to contact a medical facility if the user's blood pressure shows an abnormal value. Furthermore, the health monitoring unit can adjust the priority of toilet use based on the user's health status. For example, if the user's health condition deteriorates, the health monitoring unit can prioritize toilet use. As a result, the smart toilet network reservation system can provide a safer toilet environment that takes the user's health status into consideration.

[0061] The guidance system can further suggest routes by referring to the user's past travel history. For example, the guidance system can suggest the optimal route based on routes the user has used in the past. For example, the guidance system can suggest the optimal route based on the user's past travel history. The guidance system can also suggest routes that avoid congestion based on the user's past travel history. For example, the guidance system can suggest routes that avoid congestion based on the user's past travel history. Furthermore, the guidance system can analyze the user's past travel history and suggest the most efficient route. For example, the guidance system can suggest the most efficient route based on the user's past travel history. In this way, the guidance system can suggest the optimal route by referring to the user's past travel history.

[0062] The advertising department can further select ads by referring to the user's past purchase history. For example, the advertising department can display ads related to products the user has previously purchased. For example, the advertising department can display ads related to products the user has previously purchased based on the user's past purchase history. The advertising department can also display ads that suggest products the user might be interested in based on the user's past purchase history. For example, the advertising department can display ads that suggest products the user might be interested in based on the user's past purchase history. Furthermore, the advertising department can analyze the user's purchase history and display ads related to specific brands or categories. For example, the advertising department can display ads related to specific brands or categories based on the user's purchase history. In this way, the advertising department can display the most suitable ads by referring to the user's past purchase history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects information on available toilets. The collection unit can collect information such as the toilet's location, available hours, and cleaning status. The collection unit collects toilet location information using GPS data and updates the toilet's available hours in real time. Furthermore, the collection unit uses sensor information to understand the toilet's cleaning status and displays it in real time. Step 2: The authentication unit performs reservation authentication based on the information collected by the data collection unit, taking into account the user's location and attributes. The authentication unit obtains the user's location information using GPS data and performs reservation authentication considering the user's attributes. For example, it prioritizes the authentication of reservations for users with specific attributes, such as wheelchair users or the elderly. Furthermore, the authentication unit uses AI to analyze the user's location information, predict the time until arrival, and reserve a toilet at an appropriate time. Step 3: The guidance unit provides directions to the restroom based on the reservation authenticated by the authentication unit. The guidance unit displays the shortest route from the user's current location to the restroom, using GPS data to determine the user's current location and calculate the shortest route. For example, the guidance unit navigates the user to the shortest route from their current location to the restroom. Step 4: The advertising department delivers advertisements to users referred by the information department. The advertising department displays relevant advertisements when users use the restroom, selecting and displaying appropriate advertisements based on the user's attributes and location information. For example, when a user uses the restroom, advertisements for relevant products are displayed.

[0065] (Example of form 2) The smart toilet network reservation system according to an embodiment of the present invention is a system that uses AI to streamline the reservation and use of multipurpose toilets. This system includes functions such as checking and securing registered available toilets, minute-by-minute reservation authentication considering arrival time and priority based on the user's location information and attributes, route guidance to the location, and advertising distribution as compensation to multipurpose toilet providers. These functions are realized using AI and are installed in an IoT door lock and a smartphone app. This provides a comfortable smart toilet network reservation system. For example, a user checks the availability of multipurpose toilets using a smartphone app. The app displays the availability information of registered toilets in real time. For example, a user can search for the nearest available toilet from their current location. Next, reservation authentication is performed considering arrival time and priority based on the user's location information and attributes. For example, reservations are authenticated preferentially for users with specific attributes, such as wheelchair users and the elderly. The AI ​​analyzes the user's location information and predicts the time until arrival. This allows for toilet reservations at the appropriate time. Furthermore, the app provides route guidance to the user to the toilet. For example, it displays the shortest route from the user's current location to the toilet and provides navigation. This allows the user to arrive at the toilet without getting lost. Furthermore, the app displays advertisements as compensation to multi-purpose toilet providers. For example, relevant advertisements are displayed when a user uses a toilet. This allows providers to earn advertising revenue. This system eliminates the problem of inappropriate users preventing those who genuinely need the toilets from using them. Users can easily reserve toilets through the smartphone app and use them comfortably. In addition, providers can cover the maintenance costs of the toilets by earning advertising revenue. Thus, the smart toilet network reservation system provides an environment where users can use toilets comfortably and also serves as a means for providers to generate revenue.

[0066] The smart toilet network reservation system according to this embodiment comprises a data collection unit, an authentication unit, a guidance unit, and an advertising unit. The data collection unit collects information on available toilets. The data collection unit can collect information such as the location of the toilet, the available time, and the cleaning status. For example, the data collection unit collects toilet location information using GPS data. The data collection unit can also update the available time of the toilet in real time. Furthermore, the data collection unit can also grasp the cleaning status of the toilet using sensor information. For example, the data collection unit displays the cleaning status of the toilet in real time based on data from sensors. The authentication unit performs reservation authentication considering the user's location information and attributes based on the information collected by the data collection unit. For example, the authentication unit obtains the user's location information using GPS data and performs reservation authentication considering the user's attributes. For example, the authentication unit can prioritize reservations for users with specific attributes, such as wheelchair users or the elderly. The authentication unit analyzes the user's location information using AI and predicts the time until arrival. For example, the authentication unit can predict the arrival time based on the user's location information and reserve the toilet at an appropriate time. The guidance unit provides route guidance to the toilet based on the reservation authenticated by the authentication unit. The guidance unit can, for example, display the shortest route from the user's current location to the toilet. The guidance unit uses GPS data to determine the user's current location and calculate the shortest route. For example, the guidance unit can navigate the user to the toilet via the shortest route from their current location. The advertising unit delivers advertisements to users guided by the guidance unit. For example, the advertising unit can display relevant advertisements when the user is using the toilet. The advertising unit selects and displays appropriate advertisements based on the user's attributes and location information. For example, the advertising unit can display advertisements for relevant products when the user is using the toilet. As a result, the smart toilet network reservation system according to this embodiment can efficiently perform tasks such as checking and securing available toilets, reservation authentication based on the user's location information and attributes, route guidance, and advertisement delivery.

[0067] The data collection unit can collect information on available toilets. For example, the data collection unit can collect toilet location information using GPS data. For example, the data collection unit can update toilet location information in real time. The data collection unit can also collect the available time of toilets. For example, the data collection unit can display the available time of toilets in real time. Furthermore, the data collection unit can understand the cleaning status of toilets using sensor information. For example, the data collection unit can display the cleaning status of toilets in real time based on data from sensors. As a result, the data collection unit can collect information on available toilets in real time and provide it to users.

[0068] The authentication unit may include an attribute setting unit for setting user attributes. The attribute setting unit can set attributes such as the user's age, gender, and physical characteristics. For example, the attribute setting unit can set the user's age. It can also set the user's gender. Furthermore, the attribute setting unit can set the user's physical characteristics. For example, the attribute setting unit can set specific attributes such as wheelchair users or elderly individuals. This allows the authentication unit to perform more appropriate reservation authentication by setting the user's attributes.

[0069] The authentication unit may include a prediction unit that predicts the arrival time. The prediction unit can predict the arrival time based on, for example, the user's location information. For example, the prediction unit can obtain the user's location information using GPS data and predict the time until arrival. The prediction unit can also predict the arrival time by considering the user's speed and traffic conditions. For example, the prediction unit can predict the arrival time based on the user's speed. Furthermore, the prediction unit can also predict the arrival time by considering traffic conditions. For example, the prediction unit can predict the arrival time based on information such as traffic congestion and road construction. As a result, the authentication unit can predict the user's arrival time, enabling timely reservation authentication.

[0070] The guidance unit can display the route from the user's current location to the restroom. For example, the guidance unit can display the shortest route from the user's current location to the restroom. For example, the guidance unit uses GPS data to determine the user's current location and calculates the shortest route. The guidance unit can also customize the route considering the user's mode of transportation (walking, wheelchair, etc.). For example, if the user is walking, the guidance unit will suggest a pedestrian-only route. Furthermore, if the user is in a wheelchair, the guidance unit can suggest a barrier-free route. For example, if the user is in a wheelchair, the guidance unit will display a barrier-free route. In this way, the guidance unit can guide the user so that they can reach the restroom via the shortest route.

[0071] The advertising department can display advertisements when users use the restroom. For example, the advertising department can display relevant advertisements when users use the restroom. For example, the advertising department can select and display appropriate advertisements based on the user's attributes and location information. The advertising department can also customize advertisements based on the user's past purchase history and current areas of interest. For example, the advertising department can display advertisements related to products the user has purchased in the past. Furthermore, the advertising department can display advertisements based on the user's current areas of interest. For example, the advertising department can display advertisements related to the user's areas of interest based on the user's recent search history. In this way, the advertising department can earn compensation from businesses by displaying relevant advertisements when users use the restroom.

[0072] The data collection unit can display registered toilet availability information. For example, the data collection unit can display toilet availability information in real time. For example, the data collection unit can update toilet availability information in real time and provide it to users. The data collection unit can also analyze toilet usage history and predict availability based on user trends. For example, the data collection unit can predict toilets that are likely to become available during specific time periods based on past usage history. Furthermore, the data collection unit can collect toilet sensor information and understand the toilet's cleaning status and equipment condition in real time. For example, the data collection unit can display the cleaning status in real time based on toilet sensor information. This allows the data collection unit to display toilet availability information in real time, enabling users to quickly check for available toilets.

[0073] The data collection unit can estimate the user's emotions and adjust the frequency of collecting information on available toilets based on those emotions. For example, if the user is feeling stressed, the data collection unit can increase the collection frequency to update availability information in real time. For example, the data collection unit estimates the user's emotions and increases the collection frequency if the user is feeling stressed. The data collection unit can also maintain a normal collection frequency and provide only the necessary information if the user is relaxed. For example, the data collection unit estimates the user's emotions and maintains a normal collection frequency if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can maximize the collection frequency to provide availability information as quickly as possible. For example, the data collection unit estimates the user's emotions and maximizes the collection frequency if the user is in a hurry. This allows the data collection unit to adjust the frequency of collecting information on available toilets according to the user's emotions, enabling the provision of more appropriate information.

[0074] The data collection unit can analyze toilet usage history and predict toilet availability based on user trends. For example, it can predict toilets that are likely to be available during specific time periods based on past usage history. The data collection unit can also analyze user trends and predict toilets that are likely to be available on specific days of the week. For example, it can predict toilets that are likely to be available on specific days of the week based on user trends. Furthermore, the data collection unit can predict toilets that are likely to be available during specific events based on usage history. For example, it can predict toilets that are likely to be available during specific events. This allows the data collection unit to predict availability based on user trends and provide more accurate information.

[0075] The data collection unit can collect sensor information from toilets and understand the cleaning status and equipment condition of the toilets. For example, the data collection unit can collect sensor information from toilets and display the cleaning status in real time. For example, the data collection unit can collect cleaning status from toilet sensors and display toilets that need cleaning in real time. The data collection unit can also collect the equipment condition of toilets from sensor information and notify of malfunctions or abnormalities in real time. For example, the data collection unit can collect the equipment condition of toilets from sensor information and notify of malfunctions or abnormalities in real time. Furthermore, the data collection unit can collect toilet usage information from sensor information and display the congestion level in real time. For example, the data collection unit can collect toilet usage information from sensor information and display the congestion level in real time. As a result, the data collection unit can understand the cleaning status and equipment condition of toilets in real time and provide appropriate information to users.

[0076] The data collection unit can estimate the user's emotions and prioritize the toilet information to collect based on those emotions. For example, if the user is feeling stressed, the data collection unit can prioritize collecting information on the nearest available toilet. For example, the data collection unit estimates the user's emotions and prioritizes collecting information on the nearest available toilet if the user is feeling stressed. The data collection unit can also prioritize collecting information on cleanliness and facility condition if the user is relaxed. For example, the data collection unit estimates the user's emotions and prioritizes collecting information on cleanliness and facility condition if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information on the toilet that can be used most quickly. For example, the data collection unit estimates the user's emotions and prioritizes collecting information on the toilet that can be used most quickly if the user is in a hurry. In this way, the data collection unit can prioritize the toilet information to be collected according to the user's emotions, enabling the provision of more appropriate information.

[0077] The data collection unit can adjust the information it collects when gathering information on toilet availability, taking into account the surrounding congestion level. For example, the data collection unit can analyze the surrounding congestion level and prioritize collecting information on toilets that are not crowded. For example, the data collection unit can analyze the surrounding congestion level and prioritize collecting information on toilets that are not crowded. The data collection unit can also take surrounding event information into account and prioritize collecting information on toilets when events are being held. For example, the data collection unit can take surrounding event information into account and prioritize collecting information on toilets when events are being held. Furthermore, the data collection unit can take surrounding traffic conditions into account and prioritize collecting information on toilets that are easily accessible. For example, the data collection unit can take surrounding traffic conditions into account and prioritize collecting information on toilets that are easily accessible. In this way, the data collection unit can adjust the information taking surrounding congestion levels into account and provide more appropriate toilet information.

[0078] The data collection unit can analyze the user attributes of toilets when collecting toilet availability information and prioritize collecting availability information for specific attributes. For example, the data collection unit can prioritize collecting toilet information for wheelchair users. For example, the data collection unit analyzes the user attributes of toilets and prioritizes collecting toilet information for wheelchair users. The data collection unit can also prioritize collecting toilet information for the elderly. For example, the data collection unit prioritizes collecting toilet information for the elderly. Furthermore, the data collection unit can prioritize collecting toilet information for families with children. For example, the data collection unit prioritizes collecting toilet information for families with children. In this way, the data collection unit can prioritize collecting availability information for specific attributes and provide more appropriate toilet information.

[0079] The authentication unit can estimate the user's emotions and adjust the priority of the reservation authentication based on the estimated emotions. For example, if the user is feeling stressed, the authentication unit can increase the priority of the reservation authentication. For example, the authentication unit estimates the user's emotions and increases the priority of the reservation authentication if the user is feeling stressed. The authentication unit can also perform the reservation authentication with the normal priority if the user is relaxed. For example, the authentication unit estimates the user's emotions and performs the reservation authentication with the normal priority if the user is relaxed. Furthermore, if the authentication unit is in a hurry, it can maximize the priority of the reservation authentication. For example, the authentication unit estimates the user's emotions and maximizes the priority of the reservation authentication if the user is in a hurry. In this way, the authentication unit can adjust the priority of the reservation authentication according to the user's emotions, enabling more appropriate reservation authentication.

[0080] The authentication unit can analyze a user's past usage history and select an authentication method. For example, the authentication unit can select the optimal authentication method based on the authentication methods the user has used in the past. For example, the authentication unit analyzes the user's past usage history and selects the optimal authentication method based on the authentication methods the user has used in the past. The authentication unit can also select an authentication method suitable for a specific time period based on the user's past usage history. For example, the authentication unit selects an authentication method suitable for a specific time period based on the user's past usage history. Furthermore, the authentication unit can analyze the user's past usage history and select the most efficient authentication method. For example, the authentication unit selects the most efficient authentication method based on the user's past usage history. In this way, the authentication unit can select the optimal authentication method by analyzing the user's past usage history.

[0081] The authentication unit can determine the authentication priority based on the user's health status and urgency during authentication. For example, if the user is in poor health, the authentication unit can increase the authentication priority. For instance, the authentication unit evaluates the user's health status and increases the authentication priority if the user is in poor health. The authentication unit can also maximize the authentication priority if the user's situation is urgent. For example, the authentication unit evaluates the user's urgency and maximizes the authentication priority if it is urgent. Furthermore, the authentication unit can select the optimal authentication method considering the user's health status and urgency. For example, the authentication unit selects the optimal authentication method based on the user's health status and urgency. This allows the authentication unit to perform more appropriate authentication by considering the user's health status and urgency.

[0082] The authentication unit can estimate the user's emotions and simplify the authentication process based on those emotions. For example, if the user is feeling stressed, the authentication unit can simplify the authentication process. For example, the authentication unit estimates the user's emotions and simplifies the authentication process if the user is feeling stressed. The authentication unit can also perform the normal authentication process if the user is relaxed. For example, the authentication unit estimates the user's emotions and performs the normal authentication process if the user is relaxed. Furthermore, if the user is in a hurry, the authentication unit can shorten the authentication process to the shortest possible length. For example, the authentication unit estimates the user's emotions and shortens the authentication process to the shortest possible length if the user is in a hurry. This allows the authentication unit to simplify the authentication process according to the user's emotions, enabling faster authentication.

[0083] The authentication unit can select an authentication method based on the user's device information during authentication. For example, if the user is using a smartphone, the authentication unit can select the most suitable authentication method for the smartphone. For example, the authentication unit selects the most suitable authentication method for the smartphone based on the user's device information. The authentication unit can also select the most suitable authentication method for a tablet if the user is using a tablet. For example, the authentication unit selects the most suitable authentication method for the tablet based on the user's device information. Furthermore, if the user is using a smartwatch, the authentication unit can select the most suitable authentication method for the smartwatch. For example, the authentication unit selects the most suitable authentication method for the smartwatch based on the user's device information. In this way, the authentication unit can select the most suitable authentication method by taking the user's device information into consideration.

[0084] The authentication unit can analyze the user's social media activity during authentication and perform authentication based on relevant information. For example, the authentication unit can analyze the current situation from the user's social media activity and select the optimal authentication method. For example, the authentication unit analyzes the current situation based on the user's social media activity and selects the optimal authentication method. The authentication unit can also determine the urgency of the situation from the user's social media activity and decide on the priority of authentication. For example, the authentication unit determines the urgency of the situation based on the user's social media activity and decides on the priority of authentication. Furthermore, the authentication unit can simplify the authentication procedure based on the user's social media activity. For example, the authentication unit simplifies the authentication procedure based on the user's social media activity. As a result, the authentication unit can perform more appropriate authentication by analyzing the user's social media activity.

[0085] The guidance unit can estimate the user's emotions and adjust the way route guidance is displayed based on those emotions. For example, if the user is nervous, the guidance unit can provide a simple and highly visible display method. For example, the guidance unit can estimate the user's emotions and provide a simple and highly visible display method if the user is nervous. The guidance unit can also provide a display method that includes detailed information if the user is relaxed. For example, the guidance unit can estimate the user's emotions and provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the guidance unit can provide a display method that focuses on the essentials. For example, the guidance unit can estimate the user's emotions and provide a display method that focuses on the essentials if the user is in a hurry. In this way, the guidance unit can adjust the way route guidance is displayed according to the user's emotions, enabling more appropriate guidance.

[0086] The navigation system can suggest routes by referring to the user's past travel history. For example, the navigation system can suggest the optimal route based on routes the user has used in the past. For example, the navigation system can suggest the optimal route based on the user's past travel history. The navigation system can also suggest routes that avoid congestion based on the user's past travel history. For example, the navigation system can suggest routes that avoid congestion based on the user's past travel history. Furthermore, the navigation system can analyze the user's past travel history and suggest the most efficient route. For example, the navigation system can suggest the most efficient route based on the user's past travel history. In this way, the navigation system can suggest the optimal route by referring to the user's past travel history.

[0087] The guidance unit can customize the route based on the user's mode of transportation. For example, if the user is walking, the guidance unit can suggest a pedestrian-only route. For example, based on the user's mode of transportation, the guidance unit will suggest a pedestrian-only route if the user is walking. The guidance unit can also suggest a barrier-free route if the user is using a wheelchair. For example, based on the user's mode of transportation, the guidance unit will suggest a barrier-free route if the user is using a wheelchair. Furthermore, if the user is using a bicycle, the guidance unit can suggest a bicycle-only route. For example, based on the user's mode of transportation, the guidance unit will suggest a bicycle-only route if the user is using a bicycle. In this way, the guidance unit can provide the optimal route according to the user's mode of transportation.

[0088] The guidance system can estimate the user's emotions and determine the priority of route guidance based on those emotions. For example, if the user is feeling stressed, the guidance system can prioritize guiding them to the nearest restroom. For example, the guidance system estimates the user's emotions and prioritizes guiding them to the nearest restroom if they are feeling stressed. The guidance system can also prioritize scenic routes if the user is relaxed. For example, the guidance system estimates the user's emotions and prioritizes scenic routes if they are relaxed. Furthermore, if the user is in a hurry, the guidance system can prioritize the shortest route. For example, the guidance system estimates the user's emotions and prioritizes the shortest route if they are in a hurry. In this way, the guidance system can determine the priority of route guidance according to the user's emotions, enabling more appropriate guidance.

[0089] The guidance unit can select a guidance method based on the user's device information when providing guidance. For example, if the user is using a smartphone, the guidance unit can provide the guidance method best suited for a smartphone. For example, the guidance unit provides the guidance method best suited for a smartphone based on the user's device information. The guidance unit can also provide the guidance method best suited for a tablet if the user is using a tablet. For example, the guidance unit provides the guidance method best suited for a tablet based on the user's device information. Furthermore, if the user is using a smartwatch, the guidance unit can provide the guidance method best suited for a smartwatch. For example, the guidance unit provides the guidance method best suited for a smartwatch based on the user's device information. In this way, the guidance unit can provide the optimal guidance method according to the user's device information.

[0090] The guidance system can suggest routes based on the user's geographical location. For example, it can suggest a route to the nearest restroom from the user's current location. It can also suggest a route to the most easily accessible restroom from the user's current location. Furthermore, it can suggest a route to the least crowded restroom from the user's current location. In this way, the guidance system can provide the optimal route based on the user's geographical location.

[0091] The advertising department can estimate the user's emotions and adjust how ads are displayed based on those estimates. For example, if the user is feeling stressed, the advertising department can display ads with a calm design. Similarly, if the user is relaxed, the advertising department can display ads with a bright design. Furthermore, if the user is in a hurry, the advertising department can display concise and highly visible ads. This allows the advertising department to adjust how ads are displayed according to the user's emotions, enabling more effective ad delivery.

[0092] The advertising department can select ads by referring to the user's past purchase history when displaying ads. For example, the advertising department can display ads related to products the user has previously purchased. For example, the advertising department can display ads related to products the user has previously purchased based on the user's past purchase history. The advertising department can also display ads that suggest products the user might be interested in based on the user's past purchase history. For example, the advertising department can display ads that suggest products the user might be interested in based on the user's past purchase history. Furthermore, the advertising department can analyze the user's purchase history and display ads related to specific brands or categories. For example, the advertising department can display ads related to specific brands or categories based on the user's purchase history. In this way, the advertising department can display the most suitable ads by referring to the user's past purchase history.

[0093] The advertising department can customize ads based on the user's current areas of interest when displaying them. For example, the advertising department can display ads related to areas the user is currently interested in. For example, the advertising department can display ads related to areas the user is interested in based on the user's current areas of interest. The advertising department can also display ads related to areas of interest based on the user's recent search history. For example, the advertising department can display ads related to areas of interest based on the user's recent search history. Furthermore, the advertising department can analyze areas of interest from the user's social media activity and display relevant ads. For example, the advertising department can analyze areas of interest from the user's social media activity and display relevant ads. This allows the advertising department to display ads based on the user's areas of interest, enabling more effective ad display.

[0094] The advertising department can estimate the user's emotions and adjust the timing of ad display based on those estimates. For example, if the user is relaxed, the advertising department can delay the display of the ad. For example, the advertising department can estimate the user's emotions and delay the display of the ad if the user is relaxed. The advertising department can also speed up the display of the ad if the user is in a hurry. For example, the advertising department can estimate the user's emotions and speed up the display of the ad if the user is in a hurry. Furthermore, if the advertising department is stressed, the advertising department can adjust the display timing of the ad to reduce stress. For example, the advertising department can estimate the user's emotions and adjust the display timing of the ad to reduce stress if the user is stressed. This allows the advertising department to adjust the display timing of ads according to the user's emotions, enabling more effective ad display.

[0095] The advertising department can select ads based on the user's device information when displaying them. For example, if the user is using a smartphone, the advertising department can display ads optimized for smartphones. For example, the advertising department can display ads optimized for smartphones based on the user's device information. The advertising department can also display ads optimized for tablets if the user is using a tablet. For example, the advertising department can display ads optimized for tablets based on the user's device information. Furthermore, if the user is using a smartwatch, the advertising department can display ads optimized for smartwatches. For example, the advertising department can display ads optimized for smartwatches based on the user's device information. In this way, the advertising department can display the most suitable ads according to the user's device information.

[0096] The advertising department can display ads based on the user's geographical location. For example, it can display ads for nearby stores and services based on the user's current location. The advertising department can also display region-specific promotional ads based on the user's geographical location. Furthermore, the advertising department can display ads for nearby events and activities, taking the user's location into consideration. This allows the advertising department to display more relevant ads based on the user's geographical location, enabling more effective ad display.

[0097] The attribute setting unit can estimate the user's emotions and adjust the attribute setting method based on the estimated emotions. For example, if the user is stressed, the attribute setting unit can provide a simple attribute setting interface. For example, the attribute setting unit can estimate the user's emotions and provide a simple attribute setting interface if the user is stressed. The attribute setting unit can also provide detailed attribute setting options if the user is relaxed. For example, the attribute setting unit can estimate the user's emotions and provide detailed attribute setting options if the user is relaxed. Furthermore, if the user is in a hurry, the attribute setting unit can adjust the interface to complete attribute setting quickly. For example, the attribute setting unit can estimate the user's emotions and adjust the interface to complete attribute setting quickly if the user is in a hurry. In this way, the attribute setting unit can adjust the attribute setting method according to the user's emotions, enabling more appropriate attribute setting.

[0098] The attribute setting unit can set attributes by referring to the user's past usage history when setting attributes. For example, the attribute setting unit can automatically set the optimal attributes based on the attributes the user has set in the past. For example, the attribute setting unit automatically sets the optimal attributes based on the user's past usage history and the attributes they have set in the past. The attribute setting unit can also set attributes suitable for a specific time period based on the user's past usage history. For example, the attribute setting unit sets attributes suitable for a specific time period based on the user's past usage history. Furthermore, the attribute setting unit can analyze the user's past usage history and set the most efficient attributes. For example, the attribute setting unit sets the most efficient attributes based on the user's past usage history. In this way, the attribute setting unit can set the optimal attributes by referring to the user's past usage history.

[0099] The attribute setting unit can estimate the user's emotions and determine the priority of attribute settings based on the estimated emotions. For example, if the user is feeling stressed, the attribute setting unit can prioritize setting important attributes. For example, the attribute setting unit estimates the user's emotions and prioritizes important attributes if the user is feeling stressed. The attribute setting unit can also prioritize detailed attribute settings if the user is relaxed. For example, the attribute setting unit estimates the user's emotions and prioritizes detailed attribute settings if the user is relaxed. Furthermore, if the attribute setting unit is in a hurry, it can prioritize important attributes to ensure that the settings are completed quickly. For example, the attribute setting unit estimates the user's emotions and prioritizes important attributes to ensure that the settings are completed quickly if the user is in a hurry. In this way, the attribute setting unit can determine the priority of attribute settings according to the user's emotions, enabling more appropriate attribute settings.

[0100] The attribute setting unit can set attributes based on the user's device information when setting attributes. For example, if the user is using a smartphone, the attribute setting unit can set attributes optimized for smartphones. For example, the attribute setting unit sets attributes optimized for smartphones based on the user's device information. The attribute setting unit can also set attributes optimized for tablets if the user is using a tablet. For example, the attribute setting unit sets attributes optimized for tablets based on the user's device information. Furthermore, if the user is using a smartwatch, the attribute setting unit can also set attributes optimized for smartwatches. For example, the attribute setting unit sets attributes optimized for smartwatches based on the user's device information. In this way, the attribute setting unit can set optimal attributes according to the user's device information.

[0101] The prediction unit can estimate the user's emotions and adjust the arrival time prediction method based on the estimated user emotions. For example, if the user is feeling stressed, the prediction unit can quickly predict the arrival time. For example, the prediction unit estimates the user's emotions and quickly predicts the arrival time if the user is feeling stressed. The prediction unit can also use the normal prediction method if the user is relaxed. For example, the prediction unit estimates the user's emotions and uses the normal prediction method if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can predict the arrival time in the shortest possible time. For example, the prediction unit estimates the user's emotions and predicts the arrival time in the shortest possible time if the user is in a hurry. In this way, the prediction unit can adjust the arrival time prediction method according to the user's emotions, enabling more accurate predictions.

[0102] The prediction unit can select a prediction method by referring to the user's past travel history during prediction. For example, the prediction unit can select the optimal prediction method based on routes the user has used in the past. For example, the prediction unit selects the optimal prediction method based on routes the user has used in the past, based on the user's past travel history. The prediction unit can also select a prediction method that avoids congestion based on the user's past travel history. For example, the prediction unit selects a prediction method that avoids congestion based on the user's past travel history. Furthermore, the prediction unit can analyze the user's past travel history and select the most efficient prediction method. For example, the prediction unit selects the most efficient prediction method based on the user's past travel history. In this way, the prediction unit can select the optimal prediction method by referring to the user's past travel history.

[0103] The prediction unit can estimate the user's emotions and improve the accuracy of the arrival time prediction based on the estimated emotions. For example, if the user is stressed, the prediction unit can use detailed data to improve prediction accuracy. For example, the prediction unit estimates the user's emotions and uses detailed data to improve prediction accuracy if the user is stressed. The prediction unit can also maintain normal prediction accuracy if the user is relaxed. For example, the prediction unit estimates the user's emotions and maintains normal prediction accuracy if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can use real-time data to quickly improve prediction accuracy. For example, the prediction unit estimates the user's emotions and uses real-time data to quickly improve prediction accuracy if the user is in a hurry. This allows the prediction unit to improve the accuracy of the arrival time prediction in accordance with the user's emotions, enabling more accurate predictions.

[0104] The prediction unit can select a prediction method based on the user's device information during prediction. For example, if the user is using a smartphone, the prediction unit can select the prediction method best suited for the smartphone. For example, the prediction unit selects the prediction method best suited for the smartphone based on the user's device information. The prediction unit can also select the prediction method best suited for the tablet if the user is using a tablet. For example, the prediction unit selects the prediction method best suited for the tablet based on the user's device information. Furthermore, if the user is using a smartwatch, the prediction unit can select the prediction method best suited for the smartwatch. For example, the prediction unit selects the prediction method best suited for the smartwatch based on the user's device information. In this way, the prediction unit can select the optimal prediction method according to the user's device information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, authentication unit, guidance unit, and advertising unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects toilet location information and cleaning status using the camera 42 and GPS data of the smart device 14. The authentication unit analyzes the user's location information and attributes using the identification processing unit 290 of the data processing unit 12 and performs reservation authentication. The guidance unit navigates the user to the shortest route from their current location to the toilet using the control unit 46A of the smart device 14. The advertising unit displays advertisements relevant to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, authentication unit, guidance unit, and advertising unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects toilet location information and cleaning status using the smart glasses 214's camera 42 and GPS data. The authentication unit analyzes the user's location information and attributes using the identification processing unit 290 of the data processing unit 12 and performs reservation authentication. The guidance unit navigates the user to the shortest route from their current location to the toilet using the control unit 46A of the smart glasses 214. The advertising unit displays advertisements relevant to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, authentication unit, guidance unit, and advertising unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects toilet location information and cleaning status using the camera 42 and GPS data of the headset terminal 314. The authentication unit analyzes the user's location information and attributes using the identification processing unit 290 of the data processing unit 12 and performs reservation authentication. The guidance unit navigates the user to the shortest route from their current location to the toilet using the control unit 46A of the headset terminal 314. The advertising unit displays advertisements relevant to the user using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, authentication unit, guidance unit, and advertising unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects toilet location information and cleaning status using the robot 414's camera 42 and GPS data. The authentication unit analyzes the user's location information and attributes using the identification processing unit 290 of the data processing unit 12 and performs reservation authentication. The guidance unit navigates the user to the shortest route from their current location to the toilet using the control unit 46A of the robot 414. The advertising unit displays advertisements relevant to the user using the robot 414's speaker 240.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The smart toilet network reservation system can also be equipped with a health monitoring unit that monitors the user's health status. The health monitoring unit can, for example, measure the user's heart rate and blood pressure, and take emergency action if an abnormality is detected. For instance, if the user's heart rate suddenly increases, the health monitoring unit can provide guidance to the nearest medical facility. Furthermore, if the user's blood pressure shows an abnormal value, the health monitoring unit can temporarily restrict toilet use and prompt the user to contact a medical facility. In addition, the health monitoring unit can adjust toilet usage priority based on the user's health status. For example, if the user's health condition deteriorates, the health monitoring unit can prioritize toilet use. This allows the smart toilet network reservation system to provide a safer toilet environment that takes the user's health status into consideration.

[0107] The data collection unit can also collect feedback from toilet users. For example, it can conduct surveys with users after they use the toilets to collect evaluations regarding cleanliness and ease of use. Furthermore, the data collection unit can identify areas for improvement in the toilets based on user feedback and notify the operators. For instance, it can analyze user feedback and notify operators that certain toilets are not being cleaned properly. Additionally, the data collection unit can update toilet usage status in real time based on user feedback. For example, it can notify other users that certain toilets are crowded based on user feedback. This allows the data collection unit to utilize user feedback to improve the toilet environment and create a more comfortable toilet experience.

[0108] The authentication unit can further estimate the user's emotions and select an authentication method based on those emotions. For example, if the user is feeling stressed, the authentication unit can provide a simplified authentication method. For instance, it can estimate the user's emotions and, if stressed, provide a simple authentication method such as facial recognition or fingerprint recognition. The authentication unit can also provide a standard authentication method if the user is relaxed. For example, it can estimate the user's emotions and, if relaxed, provide a standard authentication method such as password authentication or two-factor authentication. Furthermore, if the user is in a hurry, the authentication unit can provide the fastest authentication method. For example, it can estimate the user's emotions and, if in a hurry, provide a fast authentication method such as 2D code (e.g., QR code®) authentication or NFC authentication. This allows the authentication unit to select the optimal authentication method according to the user's emotions, enabling a smoother restroom experience.

[0109] The guidance system can further estimate the user's emotions and adjust the way route guidance is displayed based on those emotions. For example, if the user is feeling anxious, the guidance system can provide a simple and easy-to-read display. It can also provide a display that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the guidance system can provide a concise display. This allows the guidance system to adjust the route guidance display according to the user's emotions, enabling more appropriate guidance.

[0110] The advertising department can further estimate the user's emotions and adjust how ads are displayed based on those estimates. For example, if the user is feeling stressed, the advertising department can display ads with a calm design. Similarly, if the user is relaxed, the advertising department can display ads with a bright design. Furthermore, if the user is in a hurry, the advertising department can display concise and highly visible ads. This allows the advertising department to adjust how ads are displayed according to the user's emotions, enabling more effective ad delivery.

[0111] The data collection unit can also collect feedback from toilet users. For example, it can conduct surveys with users after they use the toilets to collect evaluations regarding cleanliness and ease of use. Furthermore, the data collection unit can identify areas for improvement in the toilets based on user feedback and notify the operators. For instance, it can analyze user feedback and notify operators that certain toilets are not being cleaned properly. Additionally, the data collection unit can update toilet usage status in real time based on user feedback. For example, it can notify other users that certain toilets are crowded based on user feedback. This allows the data collection unit to utilize user feedback to improve the toilet environment and create a more comfortable toilet experience.

[0112] The authentication unit may also include a health monitoring unit that monitors the user's health status. The health monitoring unit can, for example, measure the user's heart rate and blood pressure, and take emergency action if an abnormality is detected. For example, if the user's heart rate suddenly increases, the health monitoring unit can provide guidance to the nearest medical facility. The health monitoring unit can also temporarily restrict toilet use and prompt the user to contact a medical facility if the user's blood pressure shows an abnormal value. Furthermore, the health monitoring unit can adjust the priority of toilet use based on the user's health status. For example, if the user's health condition deteriorates, the health monitoring unit can prioritize toilet use. As a result, the smart toilet network reservation system can provide a safer toilet environment that takes the user's health status into consideration.

[0113] The guidance system can further suggest routes by referring to the user's past travel history. For example, the guidance system can suggest the optimal route based on routes the user has used in the past. For example, the guidance system can suggest the optimal route based on the user's past travel history. The guidance system can also suggest routes that avoid congestion based on the user's past travel history. For example, the guidance system can suggest routes that avoid congestion based on the user's past travel history. Furthermore, the guidance system can analyze the user's past travel history and suggest the most efficient route. For example, the guidance system can suggest the most efficient route based on the user's past travel history. In this way, the guidance system can suggest the optimal route by referring to the user's past travel history.

[0114] The advertising department can further select ads by referring to the user's past purchase history. For example, the advertising department can display ads related to products the user has previously purchased. For example, the advertising department can display ads related to products the user has previously purchased based on the user's past purchase history. The advertising department can also display ads that suggest products the user might be interested in based on the user's past purchase history. For example, the advertising department can display ads that suggest products the user might be interested in based on the user's past purchase history. Furthermore, the advertising department can analyze the user's purchase history and display ads related to specific brands or categories. For example, the advertising department can display ads related to specific brands or categories based on the user's purchase history. In this way, the advertising department can display the most suitable ads by referring to the user's past purchase history.

[0115] The prediction unit can further estimate the user's emotions and improve the accuracy of the arrival time prediction based on the estimated emotions. For example, if the user is stressed, the prediction unit can use detailed data to improve prediction accuracy. For example, the prediction unit can estimate the user's emotions and use detailed data to improve prediction accuracy if the user is stressed. The prediction unit can also maintain normal prediction accuracy if the user is relaxed. For example, the prediction unit can estimate the user's emotions and maintain normal prediction accuracy if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can use real-time data to quickly improve prediction accuracy. For example, the prediction unit can estimate the user's emotions and use real-time data to quickly improve prediction accuracy if the user is in a hurry. This allows the prediction unit to improve the accuracy of the arrival time prediction in accordance with the user's emotions, enabling more accurate predictions.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The collection unit collects information on available toilets. The collection unit can collect information such as the toilet's location, available hours, and cleaning status. The collection unit collects toilet location information using GPS data and updates the toilet's available hours in real time. Furthermore, the collection unit uses sensor information to understand the toilet's cleaning status and displays it in real time. Step 2: The authentication unit performs reservation authentication based on the information collected by the data collection unit, taking into account the user's location and attributes. The authentication unit obtains the user's location information using GPS data and performs reservation authentication considering the user's attributes. For example, it prioritizes the authentication of reservations for users with specific attributes, such as wheelchair users or the elderly. Furthermore, the authentication unit uses AI to analyze the user's location information, predict the time until arrival, and reserve a toilet at an appropriate time. Step 3: The guidance unit provides directions to the restroom based on the reservation authenticated by the authentication unit. The guidance unit displays the shortest route from the user's current location to the restroom, using GPS data to determine the user's current location and calculate the shortest route. For example, the guidance unit navigates the user to the shortest route from their current location to the restroom. Step 4: The advertising department delivers advertisements to users referred by the information department. The advertising department displays relevant advertisements when users use the restroom, selecting and displaying appropriate advertisements based on the user's attributes and location information. For example, when a user uses the restroom, advertisements for relevant products are displayed.

[0118] 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.

[0119] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0120] 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.

[0121] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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. 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.

[0136] 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.

[0137] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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. 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.

[0152] 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.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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. 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.

[0169] 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.

[0170] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information on vacant toilets, An authentication unit performs reservation authentication based on the user's location information and attributes, based on the information collected by the aforementioned collection unit. A guidance unit that provides directions to the toilet based on a reservation authenticated by the aforementioned authentication unit, The system includes an advertising unit that delivers advertisements to users guided by the aforementioned guidance unit. A system characterized by the following features.

2. The authentication unit, It includes an attribute setting section for setting user attributes. The system according to feature 1.

3. The authentication unit, It includes a prediction unit that predicts the timing of arrival. The system according to feature 1.

4. The aforementioned guide section is Displays the route from the user's current location to the toilet. The system according to feature 1.

5. The aforementioned advertising department, Advertisements are displayed when you use the restroom. The system according to feature 1.

6. The aforementioned collection unit is Display registered toilet availability information. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting information on available toilets based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is By analyzing toilet usage history, the system predicts toilet availability based on user trends. The system according to feature 1.

9. The aforementioned collection unit is Collect sensor information from toilets to understand the cleanliness status and condition of the facilities. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of toilet information to collect based on the estimated user emotions. The system according to feature 1.

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