System

Abstract

An object of a system according to an embodiment is to efficiently collect information necessary for repair and maintenance of a public toilet and notify a municipality of the information.SOLUTION: A system includes a reception unit, an analysis unit, a notification unit, and a return unit. The reception unit receives a photograph of a public toilet from a user. The analysis unit analyzes the photograph received by the reception unit and detects a part that needs repair. The notification unit notifies a local government that manages the information detected by the analysis unit. The return unit returns points to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that it takes a lot of time and money to collect the information necessary for repairing and maintaining public toilets.

[0005] The system according to the embodiment aims to efficiently collect information necessary for repair and maintenance of public toilets and notify the local government of the information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a notification unit, and a redemption unit. The reception unit receives photos of public restrooms from users. The analysis unit analyzes the photos received by the reception unit and detects areas that need repair. The notification unit notifies the local government that manages the information detected by the analysis unit. The redemption unit redeems points to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect information necessary for repair and maintenance of public toilets and notify the local government of the information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is a system for improving the comfort of public restrooms. In this system, users take photos of public restrooms and send them using a dedicated app. The sent information is stored and read by AI, which detects areas that need repair or improvement and notifies the local government that manages the restrooms. This significantly reduces the manpower and costs required to check for areas in need of repair, leading to the development of clean and safe public restrooms. Furthermore, by rewarding users with points when they send photos of restrooms, the number of submissions from users can be increased. For example, incentives can be provided to users using electronic payment systems or local promotion points. This significantly reduces the manpower and costs required to check for areas in need of repair, leading to the development of clean and safe public restrooms.

[0029] A public restroom management system according to an embodiment includes a reception unit, an analysis unit, a notification unit, and a refund unit. The reception unit receives photos of public restrooms from users. For example, users can take photos of public restrooms and send them via a dedicated app. The reception unit accumulates the photos and passes them to the analysis unit. The analysis unit uses AI to analyze the photos received by the reception unit and detect areas requiring repair or improvement. For example, the AI ​​uses an image recognition algorithm to automatically detect stains or damage in the restroom. The analysis unit can also use AI to analyze the resolution and format of the submitted photos and generate optimal analysis results. The notification unit notifies the local government that manages the information detected by the analysis unit. For example, the notification unit notifies the local government of areas requiring repair or improvement via email, SMS, app notification, or other methods. The notification unit can also use AI to prioritize notifications and send notifications based on importance. The refund unit provides point refunds to users. For example, the refund unit can provide incentives to users using an electronic payment system or a local promotion point system. In addition, the refund unit can use AI to analyze the user's behavior history and feedback and select the optimal refund method. As a result, the public restroom management system according to the embodiment can significantly reduce the manpower and costs required to check for parts of public restrooms that need repair, leading to the development of clean and safe public restrooms.

[0030] The reception unit analyzes the user's past photo sending history and selects an appropriate reception method. The reception unit suggests the optimal reception method, for example, based on the types of photos the user has frequently sent in the past. The reception unit can also preferentially suggest transmission methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used in a specific time period based on the user's past sending history. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0031] When receiving photos, the reception unit filters the photos based on the user's current location information. For example, the reception unit preferentially receives photos of restrooms that are close to the user's current location. The reception unit can also filter photos taking into account the usage status (such as congestion level) of the restroom used by the user. The reception unit can also preferentially receive photos of restrooms used by the user during a specific time period. This makes it possible to receive optimal photos based on the user's location information and usage status. Location information is obtained using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0032] When accepting a photo, the acceptance unit selects an appropriate acceptance means according to the user's input method. For example, the acceptance unit automatically accepts the photo when the user simply voice-inputs "send a photo of the toilet." Furthermore, when the user inputs a description in text, the acceptance unit can also accept the photo based on detailed information. Furthermore, when the user uploads an image, the acceptance unit can perform image analysis and select the optimal acceptance means. This makes it possible to provide the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI.

[0033] When accepting photos, the acceptance unit prioritizes accepting highly relevant photos in consideration of the user's geographical location information. For example, the acceptance unit prioritizes accepting photos of toilets close to the user's current location. The acceptance unit can also prioritize accepting photos taken by the user in a specific area. The acceptance unit can also prioritize accepting photos of places the user has visited in the past. This makes it possible to prioritize accepting highly relevant photos based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or Wi-Fi location information. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI.

[0034] The reception unit analyzes the user's social media activity when receiving a photo and receives related photos. For example, the reception unit preferentially receives photos of places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related photos. The reception unit can also receive related photos by referring to the activities of the user's friends on social media. This makes it possible to receive related photos based on the user's social media activity. The analysis of social media activity is performed using, for example, the content of the post, the number of likes, comments, etc. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The reception unit customizes the reception method by reflecting the user's past feedback when receiving a photo. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to customize the reception method based on the user's past feedback. Feedback analysis is performed using, for example, the user's ratings, comments, areas for improvement, etc. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0036] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the photo. For example, the analysis unit performs a detailed analysis on important photos. The analysis unit can also perform a concise analysis on general photos. The analysis unit can also perform a quick analysis on photos with high urgency. This makes it possible to adjust the level of detail of the analysis depending on the importance of the photo. The importance of the photo is evaluated based on, for example, the urgency of repair or the extent of damage. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0037] During analysis, the analysis unit applies an appropriate analysis algorithm depending on the category of the toilet. For example, the analysis unit applies an algorithm that detects specific dirt or damage to park toilets. The analysis unit can also apply an algorithm that analyzes congestion and usage status to station toilets. The analysis unit can also apply an algorithm that analyzes cleaning status to commercial facility toilets. This makes it possible to apply the optimal analysis algorithm depending on the category of the toilet. Toilet categories are classified based on, for example, public toilets, commercial facility toilets, station toilets, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0038] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit adjusts the analysis algorithm based on, for example, analysis results provided by the user in the past. The analysis unit can also learn specific patterns from the user's past analysis results and improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. The use of past analysis results is performed based on, for example, a database of analysis results or past repair history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0039] During analysis, the analysis unit determines the priority of analysis based on the time the photo was taken. For example, the analysis unit prioritizes analysis of recently taken photos. The analysis unit can also prioritize analysis of photos taken during a specific time period. The analysis unit can also prioritize analysis of photos taken at a time specified by the user. This makes it possible to determine the priority of analysis based on the time the photo was taken. The time the photo was taken is obtained based on, for example, the metadata of the photo or information input by the user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0040] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the photos. For example, the analysis unit prioritizes analysis of highly relevant photos. The analysis unit can also prioritize analysis of related photos specified by the user. The analysis unit can also automatically determine relevance using AI and adjust the order of analysis. This makes it possible to adjust the order of analysis based on the relevance of the photos. The relevance of photos is evaluated based on, for example, photos of the same toilet or photos of the same problem. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI.

[0041] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. Furthermore, if the user has general knowledge, the analysis unit can use concise terminology. Furthermore, if the user is a beginner, the analysis unit can use easy-to-understand terminology. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. The level of expertise is evaluated based on, for example, the user's occupation and past usage history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The notification unit adjusts the level of detail of the notification based on the importance of the analysis result when notifying. For example, the notification unit provides a detailed notification for important analysis results. The notification unit can also provide a concise notification for general analysis results. The notification unit can also provide a quick notification for analysis results with high urgency. This makes it possible to adjust the level of detail of the notification depending on the importance of the analysis result. The importance of the analysis result is evaluated based on, for example, the urgency of repair or the extent of damage. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0043] When making a notification, the notification unit applies an appropriate notification algorithm depending on the response capability of the local government. For example, the notification unit provides detailed notifications to local governments with high response capabilities. The notification unit can also provide concise notifications to local governments with low response capabilities. The notification unit can also analyze the response capability of the local government and apply the optimal notification algorithm. This makes it possible to apply the optimal notification algorithm depending on the response capability of the local government. The response capability of the local government is evaluated based on, for example, the amount of resources and the response speed. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The notification unit improves the accuracy of notifications by referring to past notification results when making notifications. The notification unit adjusts the notification algorithm based on, for example, past notification results. The notification unit can also learn specific patterns from past notification results and improve the accuracy of notifications. The notification unit can also analyze past notification results and improve the accuracy of notifications. This makes it possible to improve the accuracy of notifications based on past notification results. The use of past notification results is based on, for example, the success rate of notifications and the speed of responses. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0045] At the time of notification, the notification unit selects the optimal notification method taking into account the geographical information of the local government. The notification unit selects the optimal notification method based on, for example, the geographical information of the local government. The notification unit can also determine the priority of notifications taking into account the geographical information of the local government. The notification unit can also analyze the geographical information of the local government and propose the optimal notification method. This makes it possible to provide the optimal notification method based on the geographical information of the local government. The geographical information is acquired based on, for example, GPS data or map information. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0046] At the time of notification, the notification unit analyzes the resource status of the local government and proposes the optimal notification means. The notification unit proposes the optimal notification means, for example, based on the resource status of the local government. The notification unit can also determine the priority of notifications taking into account the resource status of the local government. The notification unit can also analyze the resource status of the local government and select the optimal notification means. This makes it possible to provide the optimal notification means based on the resource status of the local government. The resource status is evaluated based on, for example, budget, personnel, facilities, etc. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The notification unit customizes the notification method by reflecting past feedback when notifying. The notification unit, for example, suggests an optimal notification method based on past feedback. The notification unit can also preferentially suggest a specific notification method based on past feedback. The notification unit can also analyze past feedback and customize the notification method. This makes it possible to customize the notification method based on past feedback. Feedback analysis is performed based on, for example, user ratings, comments, and areas for improvement. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0048] When providing a refund, the refund unit analyzes the user's past behavioral history and selects the optimal refund method. For example, the refund unit suggests the optimal refund method based on the refund methods used by the user in the past. The refund unit can also preferentially suggest specific refund methods based on the user's past behavioral history. The refund unit can also analyze the user's past behavioral history and select the optimal refund method. This makes it possible to provide the optimal refund method based on the user's past behavioral history. The behavioral history is analyzed based on, for example, past usage history, purchase history, feedback, etc. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The redemption unit customizes the redemption method based on the user's current living situation at the time of redemption. The redemption unit, for example, suggests the optimal redemption method based on the user's current living situation. The redemption unit can also customize the redemption method taking the user's current living situation into consideration. The redemption unit can also analyze the user's current living situation and select the optimal redemption method. This makes it possible to provide the optimal redemption method based on the user's current living situation. The living situation is evaluated based on, for example, income, family composition, lifestyle, etc. Some or all of the above-mentioned processing in the redemption unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The refund unit improves the refund method by reflecting user feedback at the time of refund. The refund unit improves the refund method based on, for example, user feedback. The refund unit can also preferentially suggest specific refund methods based on user feedback. The refund unit can also analyze user feedback and improve the refund method. This allows the refund method to be improved based on user feedback. Feedback analysis is performed based on, for example, user ratings, comments, and areas for improvement. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The cashback unit selects the optimal cashback method in consideration of the user's geographical location information when providing cashback. For example, the cashback unit suggests the optimal cashback method based on the user's geographical location information. The cashback unit can also determine the priority of cashback in consideration of the user's geographical location information. The cashback unit can also analyze the user's geographical location information and select the optimal cashback method. This makes it possible to provide the optimal cashback method based on the user's geographical location information. The geographical location information is acquired based on, for example, GPS data or Wi-Fi location information. Some or all of the above-described processing in the cashback unit may be performed using, for example, AI, or may be performed without using AI.

[0052] The cashback unit analyzes the user's social media activity and suggests a cashback method when providing cashback. The cashback unit suggests the optimal cashback method based on, for example, the user's social media activity. The cashback unit can also determine the priority of cashback by taking the user's social media activity into consideration. The cashback unit can also analyze the user's social media activity and select the optimal cashback method. This makes it possible to provide the optimal cashback method based on the user's social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of likes, comments, etc. Some or all of the above-described processing in the cashback unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The payout unit customizes the payout method by reflecting the user's past feedback when paying out. For example, the payout unit suggests an optimal payout method based on the user's past feedback. The payout unit can also preferentially suggest a specific payout method based on the user's past feedback. The payout unit can also analyze the user's past feedback and customize the payout method. This allows the payout method to be customized based on the user's past feedback. The feedback analysis is performed based on, for example, the user's ratings, comments, and areas for improvement. Some or all of the above-described processing in the payout unit may be performed using, for example, AI, or may be performed without using AI.

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

[0055] The reception unit can also analyze the user's past photo sending history and select an appropriate reception method. For example, it can suggest the optimal reception method based on the types of photos the user has frequently sent in the past. It can also prioritize and suggest transmission methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the reception method to be used during a specific time period based on the user's past sending history. This makes it possible to provide the optimal reception method based on the user's past history.

[0056] During analysis, the analysis unit can also adjust the level of detail of the analysis based on the importance of the photo. For example, a detailed analysis can be performed on important photos. A simple analysis can also be performed on general photos. Furthermore, a photo with high urgency can be analyzed quickly. This makes it possible to adjust the level of detail of the analysis depending on the importance of the photo.

[0057] The notification unit can also adjust the level of detail of the notification based on the importance of the analysis result when notifying. For example, a detailed notification can be sent for important analysis results. A concise notification can also be sent for general analysis results. Furthermore, a quick notification can be sent for analysis results that require urgency. This makes it possible to adjust the level of detail of the notification depending on the importance of the analysis results.

[0058] The redemption unit can also select the optimal redemption method by analyzing the user's past behavioral history when redeeming a reward. For example, the redemption unit can suggest the optimal redemption method based on the redemption methods the user has used in the past. It can also preferentially suggest specific redemption methods based on the user's past behavioral history. Furthermore, the redemption unit can also analyze the user's past behavioral history to select the optimal redemption method. This allows the optimal redemption method to be provided based on the user's past behavioral history.

[0059] When accepting photos, the accepting unit can also filter the photos based on the user's current location information. For example, photos of restrooms that are close to the user's current location can be accepted with priority. The accepting unit can also filter photos taking into account the usage status (such as congestion) of the restroom used by the user. Furthermore, it can also preferentially accept photos of restrooms used by the user during a specific time period. This makes it possible to accept optimal photos based on the user's location information and usage status.

[0060] During analysis, the analysis unit can also apply an appropriate analysis algorithm depending on the category of restroom. For example, an algorithm that detects specific types of dirt or damage can be applied to restrooms in parks. An algorithm that analyzes congestion levels and usage status can be applied to restrooms in train stations. Furthermore, an algorithm that analyzes cleaning status can be applied to restrooms in commercial facilities. This makes it possible to apply the optimal analysis algorithm depending on the category of restroom.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives photos of public restrooms from users. For example, a user can take a photo of a public restroom and send it via a dedicated app. The reception unit stores the photos and passes them to the analysis unit. Step 2: The analysis unit uses AI to analyze the photos received by the reception unit and detect areas that need repair or improvement. For example, AI can use an image recognition algorithm to automatically detect stains or damage in the toilet. The analysis unit can also analyze the resolution and format of the photos sent to generate optimal analysis results. Step 3: The notification unit notifies the local government that manages the information detected by the analysis unit. For example, the notification unit may notify the local government of areas that require repair or improvement via email, SMS, app notification, etc. The notification unit can also use AI to determine the priority of notifications and send notifications according to their importance. Step 4: The rewards department rewards the user with points. For example, the rewards department may use an electronic payment system or local promotion points to provide incentives to the user. The rewards department may also use AI to analyze the user's behavioral history and feedback and select the optimal reward method.

[0063] (Example 2) A system according to an embodiment of the present invention is a system for improving the comfort of public restrooms. In this system, users take photos of public restrooms and send them using a dedicated app. The sent information is stored and read by AI, which detects areas that need repair or improvement and notifies the local government that manages the restrooms. This significantly reduces the manpower and costs required to check for areas in need of repair, leading to the development of clean and safe public restrooms. Furthermore, by rewarding users with points when they send photos of restrooms, the number of submissions from users can be increased. For example, incentives can be provided to users using electronic payment systems or local promotion points. This significantly reduces the manpower and costs required to check for areas in need of repair, leading to the development of clean and safe public restrooms.

[0064] A public restroom management system according to an embodiment includes a reception unit, an analysis unit, a notification unit, and a refund unit. The reception unit receives photos of public restrooms from users. For example, users can take photos of public restrooms and send them via a dedicated app. The reception unit accumulates the photos and passes them to the analysis unit. The analysis unit uses AI to analyze the photos received by the reception unit and detect areas requiring repair or improvement. For example, the AI ​​uses an image recognition algorithm to automatically detect stains or damage in the restroom. The analysis unit can also use AI to analyze the resolution and format of the submitted photos and generate optimal analysis results. The notification unit notifies the local government that manages the information detected by the analysis unit. For example, the notification unit notifies the local government of areas requiring repair or improvement via email, SMS, app notification, or other methods. The notification unit can also use AI to prioritize notifications and send notifications based on importance. The refund unit provides point refunds to users. For example, the refund unit can provide incentives to users using an electronic payment system or a local promotion point system. In addition, the refund unit can use AI to analyze the user's behavior history and feedback and select the optimal refund method. As a result, the public restroom management system according to the embodiment can significantly reduce the manpower and costs required to check for parts of public restrooms that need repair, leading to the development of clean and safe public restrooms.

[0065] The reception unit estimates the user's emotions and adjusts the timing of receiving photos based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit may quickly receive photos to minimize the effort required. Furthermore, if the user is relaxed, the reception unit may provide an interface that requests detailed explanations and carefully receive photos. Furthermore, if the user is in a hurry, the reception unit may prioritize voice input and quickly receive photos. This allows photos to be received at the optimal timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI.

[0066] The reception unit analyzes the user's past photo sending history and selects an appropriate reception method. The reception unit suggests the optimal reception method, for example, based on the types of photos the user has frequently sent in the past. The reception unit can also preferentially suggest transmission methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used in a specific time period based on the user's past sending history. This makes it possible to provide the optimal reception method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0067] When receiving photos, the reception unit filters the photos based on the user's current location information. For example, the reception unit preferentially receives photos of restrooms that are close to the user's current location. The reception unit can also filter photos taking into account the usage status (such as congestion level) of the restroom used by the user. The reception unit can also preferentially receive photos of restrooms used by the user during a specific time period. This makes it possible to receive optimal photos based on the user's location information and usage status. Location information is obtained using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0068] When accepting a photo, the acceptance unit selects an appropriate acceptance means according to the user's input method. For example, the acceptance unit automatically accepts the photo when the user simply voice-inputs "send a photo of the toilet." Furthermore, when the user inputs a description in text, the acceptance unit can also accept the photo based on detailed information. Furthermore, when the user uploads an image, the acceptance unit can perform image analysis and select the optimal acceptance means. This makes it possible to provide the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI.

[0069] The reception unit estimates the user's emotions and determines the priority of photos to be received based on the estimated user emotions. For example, if the user is nervous, the reception unit may preferentially receive important photos. Furthermore, if the user is relaxed, the reception unit may preferentially receive photos containing detailed information. Furthermore, if the user is in a hurry, the reception unit may preferentially receive photos that require quick processing. This allows the priority of photos to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0070] When accepting photos, the acceptance unit prioritizes accepting highly relevant photos in consideration of the user's geographical location information. For example, the acceptance unit prioritizes accepting photos of toilets close to the user's current location. The acceptance unit can also prioritize accepting photos taken by the user in a specific area. The acceptance unit can also prioritize accepting photos of places the user has visited in the past. This makes it possible to prioritize accepting highly relevant photos based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or Wi-Fi location information. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI.

[0071] The reception unit analyzes the user's social media activity when receiving a photo and receives related photos. For example, the reception unit preferentially receives photos of places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related photos. The reception unit can also receive related photos by referring to the activities of the user's friends on social media. This makes it possible to receive related photos based on the user's social media activity. The analysis of social media activity is performed using, for example, the content of the post, the number of likes, comments, etc. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0072] The reception unit customizes the reception method by reflecting the user's past feedback when receiving a photo. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to customize the reception method based on the user's past feedback. Feedback analysis is performed using, for example, the user's ratings, comments, areas for improvement, etc. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. This makes it possible to adjust the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI.

[0074] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the photo. For example, the analysis unit performs a detailed analysis on important photos. The analysis unit can also perform a concise analysis on general photos. The analysis unit can also perform a quick analysis on photos with high urgency. This makes it possible to adjust the level of detail of the analysis depending on the importance of the photo. The importance of the photo is evaluated based on, for example, the urgency of repair or the extent of damage. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0075] During analysis, the analysis unit applies an appropriate analysis algorithm depending on the category of the toilet. For example, the analysis unit applies an algorithm that detects specific dirt or damage to park toilets. The analysis unit can also apply an algorithm that analyzes congestion and usage status to station toilets. The analysis unit can also apply an algorithm that analyzes cleaning status to commercial facility toilets. This makes it possible to apply the optimal analysis algorithm depending on the category of the toilet. Toilet categories are classified based on, for example, public toilets, commercial facility toilets, station toilets, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0076] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit adjusts the analysis algorithm based on, for example, analysis results provided by the user in the past. The analysis unit can also learn specific patterns from the user's past analysis results and improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results. The use of past analysis results is performed based on, for example, a database of analysis results or past repair history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide an analysis result with a visually stimulating effect if the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0078] During analysis, the analysis unit determines the priority of analysis based on the time the photo was taken. For example, the analysis unit prioritizes analysis of recently taken photos. The analysis unit can also prioritize analysis of photos taken during a specific time period. The analysis unit can also prioritize analysis of photos taken at a time specified by the user. This makes it possible to determine the priority of analysis based on the time the photo was taken. The time the photo was taken is obtained based on, for example, the metadata of the photo or information input by the user. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0079] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the photos. For example, the analysis unit prioritizes analysis of highly relevant photos. The analysis unit can also prioritize analysis of related photos specified by the user. The analysis unit can also automatically determine relevance using AI and adjust the order of analysis. This makes it possible to adjust the order of analysis based on the relevance of the photos. The relevance of photos is evaluated based on, for example, photos of the same toilet or photos of the same problem. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI.

[0080] During analysis, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology. Furthermore, if the user has general knowledge, the analysis unit can use concise terminology. Furthermore, if the user is a beginner, the analysis unit can use easy-to-understand terminology. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise. The level of expertise is evaluated based on, for example, the user's occupation and past usage history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0081] The notification unit estimates the user's emotions and adjusts the notification method based on the estimated user emotions. For example, if the user is nervous, the notification unit provides a simple, highly visible notification method. Furthermore, if the user is relaxed, the notification unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the notification unit can also provide a notification method that focuses on the main points. This makes it possible to provide the optimal notification method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI.

[0082] The notification unit adjusts the level of detail of the notification based on the importance of the analysis result when notifying. For example, the notification unit provides a detailed notification for important analysis results. The notification unit can also provide a concise notification for general analysis results. The notification unit can also provide a quick notification for analysis results with high urgency. This makes it possible to adjust the level of detail of the notification depending on the importance of the analysis result. The importance of the analysis result is evaluated based on, for example, the urgency of repair or the extent of damage. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0083] When making a notification, the notification unit applies an appropriate notification algorithm depending on the response capability of the local government. For example, the notification unit provides detailed notifications to local governments with high response capabilities. The notification unit can also provide concise notifications to local governments with low response capabilities. The notification unit can also analyze the response capability of the local government and apply the optimal notification algorithm. This makes it possible to apply the optimal notification algorithm depending on the response capability of the local government. The response capability of the local government is evaluated based on, for example, the amount of resources and the response speed. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The notification unit improves the accuracy of notifications by referring to past notification results when making notifications. The notification unit adjusts the notification algorithm based on, for example, past notification results. The notification unit can also learn specific patterns from past notification results and improve the accuracy of notifications. The notification unit can also analyze past notification results and improve the accuracy of notifications. This makes it possible to improve the accuracy of notifications based on past notification results. The use of past notification results is based on, for example, the success rate of notifications and the speed of responses. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The notification unit estimates the user's emotion and adjusts the timing of the notification based on the estimated user emotion. For example, if the user is nervous, the notification unit provides a prompt notification. Furthermore, if the user is relaxed, the notification unit can also provide a notification at an appropriate timing. Furthermore, if the user is in a hurry, the notification unit can also provide an instant notification. This allows for the notification to be provided at the optimal timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0086] At the time of notification, the notification unit selects the optimal notification method taking into account the geographical information of the local government. The notification unit selects the optimal notification method based on, for example, the geographical information of the local government. The notification unit can also determine the priority of notifications taking into account the geographical information of the local government. The notification unit can also analyze the geographical information of the local government and propose the optimal notification method. This makes it possible to provide the optimal notification method based on the geographical information of the local government. The geographical information is acquired based on, for example, GPS data or map information. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0087] At the time of notification, the notification unit analyzes the resource status of the local government and proposes the optimal notification means. The notification unit proposes the optimal notification means, for example, based on the resource status of the local government. The notification unit can also determine the priority of notifications taking into account the resource status of the local government. The notification unit can also analyze the resource status of the local government and select the optimal notification means. This makes it possible to provide the optimal notification means based on the resource status of the local government. The resource status is evaluated based on, for example, budget, personnel, facilities, etc. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The notification unit customizes the notification method by reflecting past feedback when notifying. The notification unit, for example, suggests an optimal notification method based on past feedback. The notification unit can also preferentially suggest a specific notification method based on past feedback. The notification unit can also analyze past feedback and customize the notification method. This makes it possible to customize the notification method based on past feedback. Feedback analysis is performed based on, for example, user ratings, comments, and areas for improvement. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The return unit estimates the user's emotion and adjusts the return method based on the estimated user's emotion. For example, if the user is relaxed, the return unit provides a detailed return method. If the user is in a hurry, the return unit can also provide a concise return method. If the user is excited, the return unit can also provide a visually stimulating return method. This makes it possible to provide an optimal return method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the return unit may be performed, for example, using AI or without AI.

[0090] When providing a refund, the refund unit analyzes the user's past behavioral history and selects the optimal refund method. For example, the refund unit suggests the optimal refund method based on the refund methods used by the user in the past. The refund unit can also preferentially suggest specific refund methods based on the user's past behavioral history. The refund unit can also analyze the user's past behavioral history and select the optimal refund method. This makes it possible to provide the optimal refund method based on the user's past behavioral history. The behavioral history is analyzed based on, for example, past usage history, purchase history, feedback, etc. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The redemption unit customizes the redemption method based on the user's current living situation at the time of redemption. The redemption unit, for example, suggests the optimal redemption method based on the user's current living situation. The redemption unit can also customize the redemption method taking the user's current living situation into consideration. The redemption unit can also analyze the user's current living situation and select the optimal redemption method. This makes it possible to provide the optimal redemption method based on the user's current living situation. The living situation is evaluated based on, for example, income, family composition, lifestyle, etc. Some or all of the above-mentioned processing in the redemption unit may be performed using, for example, AI, or may be performed without using AI.

[0092] The refund unit improves the refund method by reflecting user feedback at the time of refund. The refund unit improves the refund method based on, for example, user feedback. The refund unit can also preferentially suggest specific refund methods based on user feedback. The refund unit can also analyze user feedback and improve the refund method. This allows the refund method to be improved based on user feedback. Feedback analysis is performed based on, for example, user ratings, comments, and areas for improvement. Some or all of the above-described processing in the refund unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The return unit estimates the user's emotions and determines the priority of return based on the estimated user emotions. For example, when the user is nervous, the return unit prioritizes important return. Furthermore, when the user is relaxed, the return unit can prioritize detailed return. Furthermore, when the user is in a hurry, the return unit can quickly provide return. This allows the priority of return to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the return unit may be performed, for example, using AI or without AI.

[0094] The cashback unit selects the optimal cashback method in consideration of the user's geographical location information when providing cashback. For example, the cashback unit suggests the optimal cashback method based on the user's geographical location information. The cashback unit can also determine the priority of cashback in consideration of the user's geographical location information. The cashback unit can also analyze the user's geographical location information and select the optimal cashback method. This makes it possible to provide the optimal cashback method based on the user's geographical location information. The geographical location information is acquired based on, for example, GPS data or Wi-Fi location information. Some or all of the above-described processing in the cashback unit may be performed using, for example, AI, or may be performed without using AI.

[0095] The cashback unit analyzes the user's social media activity and suggests a cashback method when providing cashback. The cashback unit suggests the optimal cashback method based on, for example, the user's social media activity. The cashback unit can also determine the priority of cashback by taking the user's social media activity into consideration. The cashback unit can also analyze the user's social media activity and select the optimal cashback method. This makes it possible to provide the optimal cashback method based on the user's social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of likes, comments, etc. Some or all of the above-described processing in the cashback unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The payout unit customizes the payout method by reflecting the user's past feedback when paying out. For example, the payout unit suggests an optimal payout method based on the user's past feedback. The payout unit can also preferentially suggest a specific payout method based on the user's past feedback. The payout unit can also analyze the user's past feedback and customize the payout method. This allows the payout method to be customized based on the user's past feedback. The feedback analysis is performed based on, for example, the user's ratings, comments, and areas for improvement. Some or all of the above-described processing in the payout unit may be performed using, for example, AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, notification unit, and redemption unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive photos of public restrooms from a user using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using AI to detect areas requiring repair or improvement. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and notifies the local government that manages the detected information. The redemption unit is realized by the control unit 46A of the smart device 14 and redeems points to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, notification unit, and redemption unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a photo of a public restroom from a user using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photo using AI to detect areas requiring repair or improvement. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and notifies the local government that manages the detected information. The redemption unit is realized by the control unit 46A of the smart glasses 214 and redeems points to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, notification unit, and redemption unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive a photo of a public restroom from a user using the camera 42 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the photo using AI to detect parts that need repair or improvement. The notification unit is realized by the specific processing unit 290 of the data processing device 12, and notifies the local government that manages the detected information. The redemption unit is realized by the control unit 46A of the headset type terminal 314, and redeems points to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, notification unit, and redemption unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive photos of public restrooms from users using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the photos using AI to detect areas requiring repair or improvement. The notification unit is realized by the specific processing unit 290 of the data processing device 12, and notifies the local government that manages the detected information. The redemption unit is realized by the control unit 46A of the robot 414, and redeems points to the user.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can also estimate the user's emotions and adjust the timing of receiving photos based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive photos to minimize the effort required. If the user is relaxed, the reception unit can provide an interface that requests detailed explanations and carefully receive photos. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly receive photos. This allows photos to be received at the optimal timing according to the user's emotions.

[0099] The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, analysis results with visually stimulating effects can be provided. In this way, the way the analysis is presented can be adjusted according to the user's emotions.

[0100] The notification unit can also estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible notification method can be provided. If the user is relaxed, a notification method including detailed information can be provided. Furthermore, if the user is in a hurry, a notification method that focuses on the main points can be provided. This makes it possible to provide the optimal notification method according to the user's emotions.

[0101] The refund unit can also estimate the user's emotion and adjust the refund method based on the estimated user's emotion. For example, if the user is relaxed, a detailed refund method can be provided. If the user is in a hurry, a simple refund method can be provided. Furthermore, if the user is excited, a visually stimulating refund method can be provided. In this way, it is possible to provide an optimal refund method according to the user's emotion.

[0102] The reception unit can also analyze the user's past photo sending history and select an appropriate reception method. For example, it can suggest the optimal reception method based on the types of photos the user has frequently sent in the past. It can also prioritize and suggest transmission methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the reception method to be used during a specific time period based on the user's past sending history. This makes it possible to provide the optimal reception method based on the user's past history.

[0103] During analysis, the analysis unit can also adjust the level of detail of the analysis based on the importance of the photo. For example, a detailed analysis can be performed on important photos. A simple analysis can also be performed on general photos. Furthermore, a photo with high urgency can be analyzed quickly. This makes it possible to adjust the level of detail of the analysis depending on the importance of the photo.

[0104] The notification unit can also adjust the level of detail of the notification based on the importance of the analysis result when notifying. For example, a detailed notification can be sent for important analysis results. A concise notification can also be sent for general analysis results. Furthermore, a quick notification can be sent for analysis results that require urgency. This makes it possible to adjust the level of detail of the notification depending on the importance of the analysis results.

[0105] The redemption unit can also select the optimal redemption method by analyzing the user's past behavioral history when redeeming a reward. For example, the redemption unit can suggest the optimal redemption method based on the redemption methods the user has used in the past. It can also preferentially suggest specific redemption methods based on the user's past behavioral history. Furthermore, the redemption unit can also analyze the user's past behavioral history to select the optimal redemption method. This allows the optimal redemption method to be provided based on the user's past behavioral history.

[0106] When accepting photos, the accepting unit can also filter the photos based on the user's current location information. For example, photos of restrooms that are close to the user's current location can be accepted with priority. The accepting unit can also filter photos taking into account the usage status (such as congestion) of the restroom used by the user. Furthermore, it can also preferentially accept photos of restrooms used by the user during a specific time period. This makes it possible to accept optimal photos based on the user's location information and usage status.

[0107] During analysis, the analysis unit can also apply an appropriate analysis algorithm depending on the category of restroom. For example, an algorithm that detects specific types of dirt or damage can be applied to restrooms in parks. An algorithm that analyzes congestion levels and usage status can be applied to restrooms in train stations. Furthermore, an algorithm that analyzes cleaning status can be applied to restrooms in commercial facilities. This makes it possible to apply the optimal analysis algorithm depending on the category of restroom.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit receives photos of public restrooms from users. For example, a user can take a photo of a public restroom and send it via a dedicated app. The reception unit stores the photos and passes them to the analysis unit. Step 2: The analysis unit uses AI to analyze the photos received by the reception unit and detect areas that need repair or improvement. For example, AI can use an image recognition algorithm to automatically detect stains or damage in the toilet. The analysis unit can also analyze the resolution and format of the photos sent to generate optimal analysis results. Step 3: The notification unit notifies the local government that manages the information detected by the analysis unit. For example, the notification unit may notify the local government of areas that require repair or improvement via email, SMS, app notification, etc. The notification unit can also use AI to determine the priority of notifications and send notifications according to their importance. Step 4: The rewards department rewards the user with points. For example, the rewards department may use an electronic payment system or local promotion points to provide incentives to the user. The rewards department may also use AI to analyze the user's behavioral history and feedback and select the optimal reward method.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] 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.

[0173] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives photos of public toilets from users; an analysis unit that analyzes the photograph received by the reception unit and detects parts that need repair; a notification unit that notifies a local government that manages the information detected by the analysis unit; A redemption unit that redeems points to users. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of accepting photos based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past photo sending history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit When accepting photos, filter them based on the user's current location.

2. The system of claim 1.

5. The reception unit When accepting photos, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the photos to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting photos, the system takes into account the user's geographic location information and prioritizes photos that are highly relevant.

2. The system of claim 1.

8. The reception unit When accepting photos, analyze users' social media activity and accept related photos.

2. The system of claim 1.

Images