System

Abstract

An object of a system according to an embodiment is to enable a user to quickly find an optimum toilet at a place where the user has gone.SOLUTION: A system includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a real-time acquisition unit. The receiving unit receives an input from a user. The collection unit collects facility information of the current location of the user or the toilet on the basis of the information received by the reception unit. The analysis part analyzes the information collected by the collection part and selects a toilet on the basis of a user's request. The suggestion unit suggests information on the toilet to the user based on the analysis result obtained by the analysis unit. The real-time acquisition unit acquires information in real time.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] With conventional technology, it was difficult to quickly find the most suitable toilet when out and about, and it was particularly difficult to select one that took into account cleanliness and facility information.

[0005] The system according to the embodiment aims to enable a user to quickly find the most suitable toilet when out and about. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a suggestion unit, and a real-time acquisition unit. The reception unit accepts user input. The collection unit collects information about the user's current location or toilet facilities based on the information accepted by the reception unit. The analysis unit analyzes the information collected by the collection unit and selects a toilet based on the user's request. The suggestion unit suggests toilet information to the user based on the analysis results obtained by the analysis unit. The real-time acquisition unit acquires information in real time. [Effects of the Invention]

[0007] The system according to the embodiment allows users to quickly find the best toilet when they are out and about. [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 toilet recommendation system according to an embodiment of the present invention proposes the optimal toilet based on user input and provides up-to-date information in real time. The toilet recommendation system accepts user input, and a generation AI proposes nearby toilet locations and facility information (e.g., number of toilet seats, cleanliness, congestion forecast, diaper changing stations, nursing rooms) based on the user's current location and requests. For example, in the toilet recommendation system, a user inquires about toilet locations and facility information via chat. For example, the user inputs questions such as, "Please tell me which toilets are clean nearby" or "Which toilets have diaper changing stations?" This information is input to the generation AI. The generation AI then analyzes the input information and proposes the optimal toilet location and facility information based on the user's current location and requests. The generation AI selects the optimal toilet for the user based on information such as the number of toilet seats, cleanliness, congestion forecast, diaper changing stations, and nursing rooms. For example, the system provides specific information such as, "The toilet 200 meters from my current location is clean and has a diaper changing station." Furthermore, because the generation AI obtains information in real time, the latest information is always provided. For example, if a restroom's congestion status or cleaning status changes, that information is reflected immediately, making it easier for users to choose a reliable restroom. This allows the restroom recommendation system to solve the problems of people who suddenly need a restroom while out and about, or who want to find a clean restroom or a restroom with a diaper changing station or nursing room. For example, even if a parent with a child suddenly needs a restroom, the generative AI will quickly suggest the most suitable restroom, allowing them to enjoy their outing with peace of mind.

[0029] A restroom recommendation system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a real-time acquisition unit. The reception unit accepts user input. Examples of user input include, but are not limited to, text input, voice input, and touch input. The reception unit allows the user to inquire about restroom locations and facility information via chat, for example. The collection unit collects the user's current location or restroom facility information based on the information accepted by the reception unit. Methods for acquiring the current location include, for example, GPS, Wi-Fi location information, and cell tower location information. The restroom facility information includes, for example, the cleanliness level of the restroom, available hours, and type of facility. The analysis unit analyzes the collected information and selects a restroom based on the user's request. The analysis includes, for example, the type of algorithm, the accuracy of the analysis, and the data used. The proposal unit proposes restroom information to the user based on the analysis results. The proposal format may include, for example, a text message or a voice message. The real-time acquisition unit acquires information on the congestion status or cleaning status of the restroom in real time. The definition of real-time includes, for example, the update frequency of information, delay time, data source, etc. This allows the toilet recommendation system according to the embodiment to recommend the most suitable toilet based on the user's input and provide the latest information in real time.

[0030] The reception unit can accept user input in a chat format. Examples of chat formats include, but are not limited to, text-based chat, voice chat, and video chat. For example, the reception unit allows a user to inquire about the location and facility information of restrooms through text-based chat. The reception unit can also accept user input through voice chat. For example, the user may input by voice, "Please tell me where the nearest clean restroom is." The reception unit can also accept user input through video chat. For example, the user may inquire about the location and facility information of restrooms through video chat. This allows the user to easily inquire about restroom information through chat.

[0031] The collection unit can collect information about the user's current location and toilet facility information. Methods for acquiring the current location include, but are not limited to, GPS, Wi-Fi location information, and cell tower location information. The collection unit acquires the user's current location using, for example, GPS. The collection unit can also acquire the user's current location using Wi-Fi location information. The collection unit can also acquire the user's current location using cell tower location information. The toilet facility information includes, for example, the cleanliness of the toilet, available hours, and type of facility. The collection unit collects, for example, the cleanliness of the toilet. The collection unit can also collect the available hours of the toilet. The collection unit can also collect the type of facility. This makes it possible to accurately collect the user's current location and toilet facility information.

[0032] The analysis unit can analyze the collected information and select a toilet based on the user's request. Examples of the analysis include, but are not limited to, the type of algorithm, the accuracy of the analysis, and the data used. The analysis unit can analyze the collected information using, for example, a machine learning algorithm. The analysis unit can also analyze the collected information using natural language processing technology. Furthermore, the analysis unit can analyze the collected information using statistical analysis techniques. This makes it possible to select the optimal toilet based on the user's request.

[0033] The suggestion unit can suggest toilet information to the user based on the analysis results. The format of the suggestion includes, but is not limited to, for example, a text message, a voice message, etc. The suggestion unit can suggest toilet information to the user using, for example, a text message. The suggestion unit can also suggest toilet information to the user using a voice message. Furthermore, the suggestion unit can also suggest toilet information to the user using a video message. This makes it possible to suggest optimal toilet information to the user based on the analysis results.

[0034] The real-time acquisition unit can acquire information on the congestion status or cleaning status of the restroom in real time. The definition of real-time includes, for example, the update frequency of information, delay time, data source, etc., but is not limited to these examples. The real-time acquisition unit acquires the congestion status of the restroom in real time using, for example, a sensor. The real-time acquisition unit can also acquire the cleaning status of the restroom in real time using cleaning history data. Furthermore, the real-time acquisition unit can also acquire the congestion status of the restroom in real time using usage history data. This makes it possible to acquire the latest information on the congestion status and cleaning status of the restroom in real time.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the location and facility information of restrooms that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the location and facility information of restrooms to be used during a specific time period from the user's past input history. This makes it possible to improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0036] The reception unit can simplify the input process based on the user's current situation. For example, when the user is in a hurry, the reception unit prompts the user to input only the minimum amount of information necessary so that input can be completed in the shortest time. Furthermore, when the user is relaxed, the reception unit prompts the user to input detailed information, thereby enabling more accurate suggestions. Furthermore, when the user is feeling stressed, the reception unit can provide a simple interface and minimize input procedures. This simplifies the input process according to the user's current situation, enabling quick input.

[0037] The reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the location and facility information of the restroom using voice recognition technology. Also, if the user selects text input, the reception unit can input the location and facility information of the restroom using keyboard input. Furthermore, if the user selects image input, the reception unit can input the location and facility information of the restroom using image recognition technology. In this way, by selecting the optimal input means depending on the user's input method, it is possible to improve the accuracy and efficiency of input.

[0038] The reception unit can present highly relevant input options by taking into account the user's geographical location information. For example, the reception unit automatically displays toilet locations close to the user's current location as candidates. Furthermore, when the user is in a specific area, the reception unit can preferentially display information about toilet facilities in that area. Furthermore, when the user is traveling, the reception unit can predict and display information about the locations and facilities of toilets at the user's destination. In this way, highly relevant input options can be presented by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and suggest related input options. For example, the reception unit can suggest nearby restroom locations and facility information based on the location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related restroom locations and facility information. Furthermore, the reception unit can also suggest related restroom locations and facility information based on the activity of the user's friends on social media. In this way, related input options can be suggested by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0041] The collection unit can analyze the user's past toilet usage history and select the optimal information collection method. The collection unit selects the optimal information collection method, for example, based on the locations and facility information of toilets the user has used in the past. The collection unit can also prioritize collection of information about toilets used during specific time periods based on the user's past toilet usage history. Furthermore, the collection unit can analyze the user's past toilet usage history and select the most efficient information collection method. This enables efficient information collection by selecting the optimal information collection method based on the user's past toilet usage history.

[0042] When collecting restroom facility information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user is traveling with a child, the collection unit can prioritize collecting information about diaper changing stations and nursing rooms. In addition, if the user is elderly, the collection unit can also prioritize collecting information about barrier-free restrooms. Furthermore, if the user is sightseeing, the collection unit can also prioritize collecting information about restrooms near tourist spots. In this way, by filtering based on the user's current living situation and areas of interest, more relevant information can be collected.

[0043] When collecting restroom facility information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit collects the restroom facility information using voice recognition technology. Also, if the user selects text input, the collection unit can collect the restroom facility information using keyboard input. Furthermore, if the user selects image input, the collection unit can collect the restroom facility information using image recognition technology. In this way, by selecting the optimal collection means depending on the user's input method, the accuracy and efficiency of information collection can be improved.

[0044] When collecting toilet facility information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting facility information for toilets close to the user's current location. Furthermore, if the user is in a specific area, the collection unit can also prioritize collecting facility information for toilets in that area. Furthermore, if the user is traveling, the collection unit can predict and collect facility information for toilets at the user's destination. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0045] When collecting restroom facility information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects nearby restroom facility information based on the location where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts to collect related restroom facility information. Furthermore, the collection unit can also collect related restroom facility information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activity.

[0046] When collecting restroom facility information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit selects the optimal information collection method based on the user's past feedback. The collection unit can also prioritize the collection of specific information (e.g., cleanliness) from the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and customize the information collection means. In this way, the optimal information collection method can be provided by reflecting the user's past feedback.

[0047] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by taking into account the frequency of toilet use and fluctuations in cleanliness. For example, if the frequency of toilet use is high, the analysis unit can improve the accuracy of the analysis by taking into account fluctuations in cleanliness. In addition, if the frequency of toilet use is low, the analysis unit can improve the accuracy of the analysis by taking into account fluctuations in cleanliness. Furthermore, the analysis unit can analyze fluctuations in toilet use and cleanliness in real time and suggest the most suitable toilet. In this way, the accuracy of the analysis can be improved by taking into account fluctuations in toilet use and cleanliness.

[0048] When analyzing the collected information, the analysis unit can apply different analysis methods to each category of restroom facility information. For example, when analyzing information about the number of toilet seats, the analysis unit can take into account usage frequency and congestion predictions. In addition, when analyzing information about cleanliness, the analysis unit can also take into account cleaning frequency and user feedback. Furthermore, when analyzing information about diaper changing stations and nursing rooms, the analysis unit can also take into account specific user needs. In this way, by applying different analysis methods to each category of restroom facility information, more accurate analysis results can be provided.

[0049] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by referring to the user's past toilet usage history. For example, the analysis unit can suggest the most suitable toilet based on information about toilets the user has used in the past. The analysis unit can also prioritize analysis of information about toilets used during specific time periods based on the user's past toilet usage history. Furthermore, the analysis unit can analyze the user's past toilet usage history and suggest the most efficient toilet. In this way, by referring to the user's past toilet usage history, the accuracy of the analysis can be improved.

[0050] When analyzing the collected information, the analysis unit can take into account the geographical distribution of toilets. For example, the analysis unit can take into account the geographical distribution of toilets and prioritize analyzing the nearest toilet. The analysis unit can also take into account the geographical distribution of toilets and prioritize analyzing information about toilets in a specific area. Furthermore, the analysis unit can take into account the geographical distribution of toilets and predict and analyze information about toilets at the destination. In this way, by taking into account the geographical distribution of toilets, more appropriate analysis results can be provided.

[0051] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by referring to related literature and data. For example, the analysis unit can analyze information about toilet cleanliness by referring to related literature. The analysis unit can also analyze information about the frequency of toilet use by referring to related data. Furthermore, the analysis unit can also improve the accuracy of the analysis of toilet facility information by referring to related research. In this way, the accuracy of the analysis can be improved by referring to related literature and data.

[0052] When analyzing the collected information, the analysis unit can take into account the market value of the toilets. For example, the analysis unit can take into account the market value of the toilets and prioritize analyzing the toilets with the highest value. The analysis unit can also take into account the market value of the toilets and prioritize analyzing information about toilets in a specific area. Furthermore, the analysis unit can also take into account the market value of the toilets and predict and analyze information about toilets at the destination. In this way, by taking into account the market value of the toilets, it is possible to suggest toilets with higher value.

[0053] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the restroom. For example, the suggestion unit suggests restrooms with high importance (e.g., highly clean restrooms) in detail. The suggestion unit can also suggest restrooms with low importance (e.g., restrooms that are used less frequently) in a concise manner. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the restroom, more appropriate information can be provided.

[0054] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the restroom. For example, when making a suggestion regarding the number of toilet seats, the suggestion unit can take into account the frequency of use and predicted congestion. Furthermore, when making a suggestion regarding cleanliness, the suggestion unit can also take into account the cleaning frequency and user feedback. Furthermore, when making a suggestion regarding a diaper changing station or a nursing room, the suggestion unit can also take into account the specific needs of the user. In this way, by applying different suggestion algorithms depending on the category of the restroom, more accurate suggestions can be made.

[0055] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit suggests the most suitable toilet based on suggestions the user has received in the past. The suggestion unit can also preferentially suggest information about toilets to be used during specific time periods based on the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and suggest the most efficient toilet. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0056] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the toilet will be used. For example, if the time when the toilet will be used is approaching, the suggestion unit can prioritize suggesting the most important information (for example, the location of the toilet). Also, if the time when the toilet will be used is far away, the suggestion unit can also prioritize suggesting detailed information (for example, information about the toilet facilities). Furthermore, the suggestion unit can adjust the priority of the suggestion depending on the time when the toilet will be used. In this way, by determining the priority of the suggestion based on the time when the toilet will be used, more appropriate information can be provided.

[0057] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the restroom. For example, when the relevance of the restroom is high, the suggestion unit can prioritize suggesting the most important information (for example, the location of the restroom). Also, when the relevance of the restroom is low, the suggestion unit can prioritize suggesting detailed information (for example, information about the restroom facilities). Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of the restroom. In this way, by adjusting the order of suggestions based on the relevance of the restroom, more appropriate information can be provided.

[0058] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the suggestion using technical terms. Also, if the user does not have technical expertise, the suggestion unit can make the suggestion in simple language. Furthermore, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, it is possible to provide information that is easier to understand.

[0059] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by taking into account fluctuations in toilet usage. For example, when toilet usage fluctuates, the real-time acquisition unit improves the accuracy of acquiring real-time information. Furthermore, when toilet usage is stable, the real-time acquisition unit can also adjust the accuracy of acquiring real-time information. Furthermore, the real-time acquisition unit can analyze fluctuations in toilet usage in real time and improve the accuracy of acquisition. In this way, by taking fluctuations in toilet usage into account, the accuracy of acquiring real-time information can be improved.

[0060] When acquiring real-time information, the real-time acquisition unit can apply different acquisition methods to each category of restroom facility information. For example, when acquiring information about the number of toilet seats, the real-time acquisition unit can take into account usage frequency and congestion forecasts. Furthermore, when acquiring information about cleanliness, the real-time acquisition unit can also take into account cleaning frequency and user feedback. Furthermore, when acquiring information about diaper changing stations and nursing rooms, the real-time acquisition unit can also take into account specific user needs. In this way, by applying different acquisition methods to each category of restroom facility information, more accurate information can be provided.

[0061] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by referring to the user's past toilet usage history. The real-time acquisition unit, for example, acquires information on the optimal toilet in real time based on information on toilets the user has used in the past. The real-time acquisition unit can also preferentially acquire information on toilets to be used during specific time periods based on the user's past toilet usage history. Furthermore, the real-time acquisition unit can analyze the user's past toilet usage history and acquire information on the most efficient toilet in real time. In this way, by referring to the user's past toilet usage history, the accuracy of acquiring real-time information can be improved.

[0062] When acquiring real-time information, the real-time acquisition unit can take into account the geographical distribution of toilets. For example, the real-time acquisition unit takes into account the geographical distribution of toilets and prioritizes acquiring information on the nearest toilet. The real-time acquisition unit can also take into account the geographical distribution of toilets and prioritize acquiring information on toilets in a specific area. Furthermore, the real-time acquisition unit can also predict and acquire information on toilets at the destination by taking into account the geographical distribution of toilets. In this way, more appropriate information can be provided by taking into account the geographical distribution of toilets.

[0063] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by referring to related literature and data. For example, the real-time acquisition unit can acquire information about the cleanliness of the toilet by referring to related literature. The real-time acquisition unit can also acquire information about the frequency of toilet use by referring to related data. Furthermore, the real-time acquisition unit can also improve the accuracy of acquisition of toilet facility information by referring to related research. In this way, by referring to related literature and data, the accuracy of acquisition of real-time information can be improved.

[0064] When acquiring real-time information, the real-time acquisition unit can take into account the market value of the toilets. For example, the real-time acquisition unit can take into account the market value of the toilets and prioritize acquiring information on toilets with the highest value. The real-time acquisition unit can also take into account the market value of the toilets and prioritize acquiring information on toilets in a specific area. Furthermore, the real-time acquisition unit can also predict and acquire information on toilets at the destination by taking into account the market value of the toilets. In this way, by taking into account the market value of the toilets, it is possible to provide information on toilets with higher value.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze the user's past toilet usage history and select the optimal information collection method. For example, the optimal information collection method is selected based on the location and facility information of toilets the user has used in the past. It can also prioritize the collection of information about toilets used during specific times based on the user's past toilet usage history. Furthermore, it can analyze the user's past toilet usage history and select the most efficient information collection method. This enables efficient information collection by selecting the optimal information collection method based on the user's past toilet usage history.

[0067] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by taking into account the frequency of toilet use and fluctuations in cleanliness. For example, if the frequency of toilet use is high, the accuracy of the analysis can be improved by taking into account fluctuations in cleanliness. Also, if the frequency of toilet use is low, the accuracy of the analysis can be improved by taking into account fluctuations in cleanliness. Furthermore, it is possible to analyze fluctuations in toilet use and cleanliness in real time and suggest the most suitable toilet. In this way, the accuracy of the analysis can be improved by taking into account fluctuations in toilet use and cleanliness.

[0068] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the restroom. For example, a highly important restroom (e.g., a highly clean restroom) can be proposed in detail. A less important restroom (e.g., a restroom that is used less frequently) can also be proposed in a more concise manner. Furthermore, the level of detail of the proposal can be adjusted according to the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the restroom, more appropriate information can be provided.

[0069] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by taking into account fluctuations in toilet usage. For example, when toilet usage fluctuates, the accuracy of acquiring real-time information is improved. Also, when toilet usage is stable, the accuracy of acquiring real-time information can be adjusted. Furthermore, fluctuations in toilet usage can be analyzed in real time to improve the accuracy of acquisition. In this way, by taking fluctuations in toilet usage into account, the accuracy of acquiring real-time information can be improved.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives user input. User input includes text input, voice input, touch input, etc. For example, a user can inquire about the location of a restroom or facility information in chat format. Step 2: The collection unit collects the user's current location or toilet facility information based on the information received by the reception unit. Methods for acquiring the current location include GPS, Wi-Fi location information, cell tower location information, etc. The toilet facility information includes the cleanliness of the toilet, available hours, type of facilities, etc. Step 3: The analysis unit analyzes the information collected by the collection unit and selects toilets based on the user's request. The analysis includes the type of algorithm, the accuracy of the analysis, and the data used. Step 4: The suggestion unit suggests toilet information to the user based on the analysis results obtained by the analysis unit. The suggestion may be in the form of a text message, a voice message, or the like. Step 5: The real-time acquisition unit acquires information on the congestion status or cleaning status of the restroom in real time. The definition of real-time includes the update frequency of the information, delay time, data source, etc.

[0072] (Example 2) A toilet recommendation system according to an embodiment of the present invention proposes the optimal toilet based on user input and provides up-to-date information in real time. The toilet recommendation system accepts user input, and a generation AI proposes nearby toilet locations and facility information (e.g., number of toilet seats, cleanliness, congestion forecast, diaper changing stations, nursing rooms) based on the user's current location and requests. For example, in the toilet recommendation system, a user inquires about toilet locations and facility information via chat. For example, the user inputs questions such as, "Please tell me which toilets are clean nearby" or "Which toilets have diaper changing stations?" This information is input to the generation AI. The generation AI then analyzes the input information and proposes the optimal toilet location and facility information based on the user's current location and requests. The generation AI selects the optimal toilet for the user based on information such as the number of toilet seats, cleanliness, congestion forecast, diaper changing stations, and nursing rooms. For example, the system provides specific information such as, "The toilet 200 meters from my current location is clean and has a diaper changing station." Furthermore, because the generation AI obtains information in real time, the latest information is always provided. For example, if a restroom's congestion status or cleaning status changes, that information is reflected immediately, making it easier for users to choose a reliable restroom. This allows the restroom recommendation system to solve the problems of people who suddenly need a restroom while out and about, or who want to find a clean restroom or a restroom with a diaper changing station or nursing room. For example, even if a parent with a child suddenly needs a restroom, the generative AI will quickly suggest the most suitable restroom, allowing them to enjoy their outing with peace of mind.

[0073] A restroom recommendation system according to an embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, and a real-time acquisition unit. The reception unit accepts user input. Examples of user input include, but are not limited to, text input, voice input, and touch input. The reception unit allows the user to inquire about restroom locations and facility information via chat, for example. The collection unit collects the user's current location or restroom facility information based on the information accepted by the reception unit. Methods for acquiring the current location include, for example, GPS, Wi-Fi location information, and cell tower location information. The restroom facility information includes, for example, the cleanliness level of the restroom, available hours, and type of facility. The analysis unit analyzes the collected information and selects a restroom based on the user's request. The analysis includes, for example, the type of algorithm, the accuracy of the analysis, and the data used. The proposal unit proposes restroom information to the user based on the analysis results. The proposal format may include, for example, a text message or a voice message. The real-time acquisition unit acquires information on the congestion status or cleaning status of the restroom in real time. The definition of real-time includes, for example, the update frequency of information, delay time, data source, etc. This allows the toilet recommendation system according to the embodiment to recommend the most suitable toilet based on the user's input and provide the latest information in real time.

[0074] The reception unit can accept user input in a chat format. Examples of chat formats include, but are not limited to, text-based chat, voice chat, and video chat. For example, the reception unit allows a user to inquire about the location and facility information of restrooms through text-based chat. The reception unit can also accept user input through voice chat. For example, the user may input by voice, "Please tell me where the nearest clean restroom is." The reception unit can also accept user input through video chat. For example, the user may inquire about the location and facility information of restrooms through video chat. This allows the user to easily inquire about restroom information through chat.

[0075] The collection unit can collect information about the user's current location and toilet facility information. Methods for acquiring the current location include, but are not limited to, GPS, Wi-Fi location information, and cell tower location information. The collection unit acquires the user's current location using, for example, GPS. The collection unit can also acquire the user's current location using Wi-Fi location information. The collection unit can also acquire the user's current location using cell tower location information. The toilet facility information includes, for example, the cleanliness of the toilet, available hours, and type of facility. The collection unit collects, for example, the cleanliness of the toilet. The collection unit can also collect the available hours of the toilet. The collection unit can also collect the type of facility. This makes it possible to accurately collect the user's current location and toilet facility information.

[0076] The analysis unit can analyze the collected information and select a toilet based on the user's request. Examples of the analysis include, but are not limited to, the type of algorithm, the accuracy of the analysis, and the data used. The analysis unit can analyze the collected information using, for example, a machine learning algorithm. The analysis unit can also analyze the collected information using natural language processing technology. Furthermore, the analysis unit can analyze the collected information using statistical analysis techniques. This makes it possible to select the optimal toilet based on the user's request.

[0077] The suggestion unit can suggest toilet information to the user based on the analysis results. The format of the suggestion includes, but is not limited to, for example, a text message, a voice message, etc. The suggestion unit can suggest toilet information to the user using, for example, a text message. The suggestion unit can also suggest toilet information to the user using a voice message. Furthermore, the suggestion unit can also suggest toilet information to the user using a video message. This makes it possible to suggest optimal toilet information to the user based on the analysis results.

[0078] The real-time acquisition unit can acquire information on the congestion status or cleaning status of the restroom in real time. The definition of real-time includes, for example, the update frequency of information, delay time, data source, etc., but is not limited to these examples. The real-time acquisition unit acquires the congestion status of the restroom in real time using, for example, a sensor. The real-time acquisition unit can also acquire the cleaning status of the restroom in real time using cleaning history data. Furthermore, the real-time acquisition unit can also acquire the congestion status of the restroom in real time using usage history data. This makes it possible to acquire the latest information on the congestion status and cleaning status of the restroom in real time.

[0079] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly enter restroom locations and facility information. This allows for a more user-friendly interface by adjusting the display method of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the location and facility information of restrooms that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the location and facility information of restrooms to be used during a specific time period from the user's past input history. This makes it possible to improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0081] The reception unit can simplify the input process based on the user's current situation. For example, when the user is in a hurry, the reception unit prompts the user to input only the minimum amount of information necessary so that input can be completed in the shortest time. Furthermore, when the user is relaxed, the reception unit prompts the user to input detailed information, thereby enabling more accurate suggestions. Furthermore, when the user is feeling stressed, the reception unit can provide a simple interface and minimize input procedures. This simplifies the input process according to the user's current situation, enabling quick input.

[0082] The reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the location and facility information of the restroom using voice recognition technology. Also, if the user selects text input, the reception unit can input the location and facility information of the restroom using keyboard input. Furthermore, if the user selects image input, the reception unit can input the location and facility information of the restroom using image recognition technology. In this way, by selecting the optimal input means depending on the user's input method, it is possible to improve the accuracy and efficiency of input.

[0083] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, when the user is in a hurry, the reception unit can prioritize input of the most important information (e.g., the location of the restroom). Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed information (e.g., information about the restroom's facilities). Furthermore, when the user is feeling stressed, the reception unit can prioritize input of simple information (e.g., the cleanliness of the restroom). By prioritizing input content according to the user's emotions, more appropriate information can be prioritized for input. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The reception unit can present highly relevant input options by taking into account the user's geographical location information. For example, the reception unit automatically displays toilet locations close to the user's current location as candidates. Furthermore, when the user is in a specific area, the reception unit can preferentially display information about toilet facilities in that area. Furthermore, when the user is traveling, the reception unit can predict and display information about the locations and facilities of toilets at the user's destination. In this way, highly relevant input options can be presented by taking into account the user's geographical location information.

[0085] The reception unit can analyze the user's social media activity and suggest related input options. For example, the reception unit can suggest nearby restroom locations and facility information based on the location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related restroom locations and facility information. Furthermore, the reception unit can also suggest related restroom locations and facility information based on the activity of the user's friends on social media. In this way, related input options can be suggested by analyzing the user's social media activity.

[0086] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0087] The collection unit can estimate the user's emotions and adjust the range of information to be collected based on the estimated user emotions. For example, if the user is in a hurry, the collection unit can prioritize collecting the most important information (e.g., the location of the restroom). Furthermore, if the user is relaxed, the collection unit can collect detailed information (e.g., information about the restroom's facilities). Furthermore, if the user is feeling stressed, the collection unit can collect simple information (e.g., the cleanliness of the restroom). This allows for more appropriate information to be collected by adjusting the range of information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The collection unit can analyze the user's past toilet usage history and select the optimal information collection method. The collection unit selects the optimal information collection method, for example, based on the locations and facility information of toilets the user has used in the past. The collection unit can also prioritize collection of information about toilets used during specific time periods based on the user's past toilet usage history. Furthermore, the collection unit can analyze the user's past toilet usage history and select the most efficient information collection method. This enables efficient information collection by selecting the optimal information collection method based on the user's past toilet usage history.

[0089] When collecting restroom facility information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, if the user is traveling with a child, the collection unit can prioritize collecting information about diaper changing stations and nursing rooms. In addition, if the user is elderly, the collection unit can also prioritize collecting information about barrier-free restrooms. Furthermore, if the user is sightseeing, the collection unit can also prioritize collecting information about restrooms near tourist spots. In this way, by filtering based on the user's current living situation and areas of interest, more relevant information can be collected.

[0090] When collecting restroom facility information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user selects voice input, the collection unit collects the restroom facility information using voice recognition technology. Also, if the user selects text input, the collection unit can collect the restroom facility information using keyboard input. Furthermore, if the user selects image input, the collection unit can collect the restroom facility information using image recognition technology. In this way, by selecting the optimal collection means depending on the user's input method, the accuracy and efficiency of information collection can be improved.

[0091] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is in a hurry, the collection unit can prioritize collecting the most important information (e.g., the location of the restroom). Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information (e.g., information about the restroom's facilities). Furthermore, when the user is stressed, the collection unit can prioritize collecting simple information (e.g., the cleanliness of the restroom). By determining the priority of information to be collected according to the user's emotions, more appropriate information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] When collecting toilet facility information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting facility information for toilets close to the user's current location. Furthermore, if the user is in a specific area, the collection unit can also prioritize collecting facility information for toilets in that area. Furthermore, if the user is traveling, the collection unit can predict and collect facility information for toilets at the user's destination. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0093] When collecting restroom facility information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects nearby restroom facility information based on the location where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts to collect related restroom facility information. Furthermore, the collection unit can also collect related restroom facility information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activity.

[0094] When collecting restroom facility information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit selects the optimal information collection method based on the user's past feedback. The collection unit can also prioritize the collection of specific information (e.g., cleanliness) from the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and customize the information collection means. In this way, the optimal information collection method can be provided by reflecting the user's past feedback.

[0095] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit prioritizes analyzing the most important information (e.g., the location of the restroom). Furthermore, if the user is relaxed, the analysis unit can analyze detailed information (e.g., information about the restroom's facilities). Furthermore, if the user is stressed, the analysis unit can analyze simple information (e.g., the cleanliness of the restroom). This allows the analysis algorithm to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by taking into account the frequency of toilet use and fluctuations in cleanliness. For example, if the frequency of toilet use is high, the analysis unit can improve the accuracy of the analysis by taking into account fluctuations in cleanliness. In addition, if the frequency of toilet use is low, the analysis unit can improve the accuracy of the analysis by taking into account fluctuations in cleanliness. Furthermore, the analysis unit can analyze fluctuations in toilet use and cleanliness in real time and suggest the most suitable toilet. In this way, the accuracy of the analysis can be improved by taking into account fluctuations in toilet use and cleanliness.

[0097] When analyzing the collected information, the analysis unit can apply different analysis methods to each category of restroom facility information. For example, when analyzing information about the number of toilet seats, the analysis unit can take into account usage frequency and congestion predictions. In addition, when analyzing information about cleanliness, the analysis unit can also take into account cleaning frequency and user feedback. Furthermore, when analyzing information about diaper changing stations and nursing rooms, the analysis unit can also take into account specific user needs. In this way, by applying different analysis methods to each category of restroom facility information, more accurate analysis results can be provided.

[0098] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by referring to the user's past toilet usage history. For example, the analysis unit can suggest the most suitable toilet based on information about toilets the user has used in the past. The analysis unit can also prioritize analysis of information about toilets used during specific time periods based on the user's past toilet usage history. Furthermore, the analysis unit can analyze the user's past toilet usage history and suggest the most efficient toilet. In this way, by referring to the user's past toilet usage history, the accuracy of the analysis can be improved.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can prioritize displaying the most important information (e.g., the location of the restroom). Furthermore, if the user is relaxed, the analysis unit can also display detailed information (e.g., information about the restroom facilities). Furthermore, if the user is feeling stressed, the analysis unit can display simple information (e.g., the cleanliness of the restroom). This allows for more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When analyzing the collected information, the analysis unit can take into account the geographical distribution of toilets. For example, the analysis unit can take into account the geographical distribution of toilets and prioritize analyzing the nearest toilet. The analysis unit can also take into account the geographical distribution of toilets and prioritize analyzing information about toilets in a specific area. Furthermore, the analysis unit can take into account the geographical distribution of toilets and predict and analyze information about toilets at the destination. In this way, by taking into account the geographical distribution of toilets, more appropriate analysis results can be provided.

[0101] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by referring to related literature and data. For example, the analysis unit can analyze information about toilet cleanliness by referring to related literature. The analysis unit can also analyze information about the frequency of toilet use by referring to related data. Furthermore, the analysis unit can also improve the accuracy of the analysis of toilet facility information by referring to related research. In this way, the accuracy of the analysis can be improved by referring to related literature and data.

[0102] When analyzing the collected information, the analysis unit can take into account the market value of the toilets. For example, the analysis unit can take into account the market value of the toilets and prioritize analyzing the toilets with the highest value. The analysis unit can also take into account the market value of the toilets and prioritize analyzing information about toilets in a specific area. Furthermore, the analysis unit can also take into account the market value of the toilets and predict and analyze information about toilets at the destination. In this way, by taking into account the market value of the toilets, it is possible to suggest toilets with higher value.

[0103] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can make concise and to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the user is feeling stressed, the suggestion unit can make simple and highly visible suggestions. This allows more appropriate suggestions to be made by adjusting the way suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the restroom. For example, the suggestion unit suggests restrooms with high importance (e.g., highly clean restrooms) in detail. The suggestion unit can also suggest restrooms with low importance (e.g., restrooms that are used less frequently) in a concise manner. Furthermore, the suggestion unit can adjust the level of detail of the proposal according to the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the restroom, more appropriate information can be provided.

[0105] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the restroom. For example, when making a suggestion regarding the number of toilet seats, the suggestion unit can take into account the frequency of use and predicted congestion. Furthermore, when making a suggestion regarding cleanliness, the suggestion unit can also take into account the cleaning frequency and user feedback. Furthermore, when making a suggestion regarding a diaper changing station or a nursing room, the suggestion unit can also take into account the specific needs of the user. In this way, by applying different suggestion algorithms depending on the category of the restroom, more accurate suggestions can be made.

[0106] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit suggests the most suitable toilet based on suggestions the user has received in the past. The suggestion unit can also preferentially suggest information about toilets to be used during specific time periods based on the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and suggest the most efficient toilet. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0107] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also make longer suggestions with detailed explanations. Furthermore, if the user is feeling stressed, the suggestion unit can also make simple, highly visible suggestions. This allows for more appropriate information to be provided by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the time when the toilet will be used. For example, if the time when the toilet will be used is approaching, the suggestion unit can prioritize suggesting the most important information (for example, the location of the toilet). Also, if the time when the toilet will be used is far away, the suggestion unit can also prioritize suggesting detailed information (for example, information about the toilet facilities). Furthermore, the suggestion unit can adjust the priority of the suggestion depending on the time when the toilet will be used. In this way, by determining the priority of the suggestion based on the time when the toilet will be used, more appropriate information can be provided.

[0109] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the restroom. For example, when the relevance of the restroom is high, the suggestion unit can prioritize suggesting the most important information (for example, the location of the restroom). Also, when the relevance of the restroom is low, the suggestion unit can prioritize suggesting detailed information (for example, information about the restroom facilities). Furthermore, the suggestion unit can adjust the order of suggestions according to the relevance of the restroom. In this way, by adjusting the order of suggestions based on the relevance of the restroom, more appropriate information can be provided.

[0110] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the suggestion using technical terms. Also, if the user does not have technical expertise, the suggestion unit can make the suggestion in simple language. Furthermore, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, it is possible to provide information that is easier to understand.

[0111] The real-time acquisition unit can estimate the user's emotions and adjust the frequency of acquiring real-time information based on the estimated user's emotions. For example, the real-time acquisition unit can increase the frequency of acquiring real-time information when the user is in a hurry. The real-time acquisition unit can also decrease the frequency of acquiring real-time information when the user is relaxed. Furthermore, the real-time acquisition unit can adjust the frequency of acquiring real-time information when the user is feeling stressed. In this way, by adjusting the frequency of acquiring real-time information according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by taking into account fluctuations in toilet usage. For example, when toilet usage fluctuates, the real-time acquisition unit improves the accuracy of acquiring real-time information. Furthermore, when toilet usage is stable, the real-time acquisition unit can also adjust the accuracy of acquiring real-time information. Furthermore, the real-time acquisition unit can analyze fluctuations in toilet usage in real time and improve the accuracy of acquisition. In this way, by taking fluctuations in toilet usage into account, the accuracy of acquiring real-time information can be improved.

[0113] When acquiring real-time information, the real-time acquisition unit can apply different acquisition methods to each category of restroom facility information. For example, when acquiring information about the number of toilet seats, the real-time acquisition unit can take into account usage frequency and congestion forecasts. Furthermore, when acquiring information about cleanliness, the real-time acquisition unit can also take into account cleaning frequency and user feedback. Furthermore, when acquiring information about diaper changing stations and nursing rooms, the real-time acquisition unit can also take into account specific user needs. In this way, by applying different acquisition methods to each category of restroom facility information, more accurate information can be provided.

[0114] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by referring to the user's past toilet usage history. The real-time acquisition unit, for example, acquires information on the optimal toilet in real time based on information on toilets the user has used in the past. The real-time acquisition unit can also preferentially acquire information on toilets to be used during specific time periods based on the user's past toilet usage history. Furthermore, the real-time acquisition unit can analyze the user's past toilet usage history and acquire information on the most efficient toilet in real time. In this way, by referring to the user's past toilet usage history, the accuracy of acquiring real-time information can be improved.

[0115] The real-time acquisition unit can estimate the user's emotions and adjust the display method of real-time information based on the estimated user emotions. For example, when the user is in a hurry, the real-time acquisition unit can provide a concise and to-the-point display method. Furthermore, when the user is relaxed, the real-time acquisition unit can provide a display method including detailed information. Furthermore, when the user is stressed, the real-time acquisition unit can provide a simple and highly visible display method. This allows for adjusting the display method of real-time information according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] When acquiring real-time information, the real-time acquisition unit can take into account the geographical distribution of toilets. For example, the real-time acquisition unit takes into account the geographical distribution of toilets and prioritizes acquiring information on the nearest toilet. The real-time acquisition unit can also take into account the geographical distribution of toilets and prioritize acquiring information on toilets in a specific area. Furthermore, the real-time acquisition unit can also predict and acquire information on toilets at the destination by taking into account the geographical distribution of toilets. In this way, more appropriate information can be provided by taking into account the geographical distribution of toilets.

[0117] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by referring to related literature and data. For example, the real-time acquisition unit can acquire information about the cleanliness of the toilet by referring to related literature. The real-time acquisition unit can also acquire information about the frequency of toilet use by referring to related data. Furthermore, the real-time acquisition unit can also improve the accuracy of acquisition of toilet facility information by referring to related research. In this way, by referring to related literature and data, the accuracy of acquisition of real-time information can be improved.

[0118] When acquiring real-time information, the real-time acquisition unit can take into account the market value of the toilets. For example, the real-time acquisition unit can take into account the market value of the toilets and prioritize acquiring information on toilets with the highest value. The real-time acquisition unit can also take into account the market value of the toilets and prioritize acquiring information on toilets in a specific area. Furthermore, the real-time acquisition unit can also predict and acquire information on toilets at the destination by taking into account the market value of the toilets. In this way, by taking into account the market value of the toilets, it is possible to provide information on toilets with higher value. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and real-time acquisition 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 accepts user input using the touch panel 38A or microphone 38B of the smart device 14. The collection unit acquires the user's current location using the GPS function or Wi-Fi location information of the smart device 14. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 and selects the most suitable restroom based on the user's request. The suggestion unit suggests restroom information to the user based on the analysis results by the identification processing unit 290 of the data processing device 12. The real-time acquisition unit acquires information on the congestion status and cleaning status of the restrooms in real time by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and real-time acquisition 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 accepts a user's voice input using the microphone 238 of the smart glasses 214. The collection unit acquires the user's current location using the GPS function or Wi-Fi location information of the smart glasses 214. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 and selects the most suitable restroom based on the user's request. The suggestion unit suggests restroom information to the user based on the analysis results by the identification processing unit 290 of the data processing device 12. The real-time acquisition unit acquires information on the congestion status and cleaning status of the restrooms in real time by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and real-time acquisition 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 accepts voice input from the user using the microphone 238 of the headset-type terminal 314. The collection unit acquires the user's current location using the GPS function or Wi-Fi location information of the headset-type terminal 314. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 and selects the most suitable restroom based on the user's request. The suggestion unit suggests restroom information to the user based on the analysis results by the identification processing unit 290 of the data processing device 12. The real-time acquisition unit acquires information on the congestion status and cleaning status of the restrooms in real time by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, suggestion unit, and real-time acquisition unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts voice input from the user using the microphone 238 of the robot 414. The collection unit acquires the user's current location using the GPS function or Wi-Fi location information of the robot 414. The analysis unit analyzes the information collected by the identification processing unit 290 of the data processing device 12 and selects the most suitable restroom based on the user's request. The suggestion unit suggests restroom information to the user based on the analysis results by the identification processing unit 290 of the data processing device 12. The real-time acquisition unit acquires information on the congestion status and cleaning status of the restrooms in real time by the identification processing unit 290 of the data processing device 12.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can make a concise and to-the-point suggestion. If the user is relaxed, the suggestion unit can make a suggestion that includes detailed information. Furthermore, if the user is feeling stressed, the suggestion unit can make a simple and highly visible suggestion. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be made.

[0121] The collection unit can analyze the user's past toilet usage history and select the optimal information collection method. For example, the optimal information collection method is selected based on the location and facility information of toilets the user has used in the past. It can also prioritize the collection of information about toilets used during specific times based on the user's past toilet usage history. Furthermore, it can analyze the user's past toilet usage history and select the most efficient information collection method. This enables efficient information collection by selecting the optimal information collection method based on the user's past toilet usage history.

[0122] When analyzing the collected information, the analysis unit can improve the accuracy of the analysis by taking into account the frequency of toilet use and fluctuations in cleanliness. For example, if the frequency of toilet use is high, the accuracy of the analysis can be improved by taking into account fluctuations in cleanliness. Also, if the frequency of toilet use is low, the accuracy of the analysis can be improved by taking into account fluctuations in cleanliness. Furthermore, it is possible to analyze fluctuations in toilet use and cleanliness in real time and suggest the most suitable toilet. In this way, the accuracy of the analysis can be improved by taking into account fluctuations in toilet use and cleanliness.

[0123] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the restroom. For example, a highly important restroom (e.g., a highly clean restroom) can be proposed in detail. A less important restroom (e.g., a restroom that is used less frequently) can also be proposed in a more concise manner. Furthermore, the level of detail of the proposal can be adjusted according to the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the restroom, more appropriate information can be provided.

[0124] When acquiring real-time information, the real-time acquisition unit can improve the accuracy of acquisition by taking into account fluctuations in toilet usage. For example, when toilet usage fluctuates, the accuracy of acquiring real-time information is improved. Also, when toilet usage is stable, the accuracy of acquiring real-time information can be adjusted. Furthermore, fluctuations in toilet usage can be analyzed in real time to improve the accuracy of acquisition. In this way, by taking fluctuations in toilet usage into account, the accuracy of acquiring real-time information can be improved.

[0125] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to quickly input information about restroom locations and facilities. In this way, by adjusting the display method of the input interface according to the user's emotions, a more user-friendly interface can be provided.

[0126] The collection unit can estimate the user's emotions and adjust the range of information to be collected based on the estimated user's emotions. For example, if the user is in a hurry, the most important information (e.g., the location of the restroom) can be collected as a priority. If the user is relaxed, detailed information (e.g., information about the restroom facilities) can be collected. Furthermore, if the user is feeling stressed, simple information (e.g., the cleanliness of the restroom) can be collected. In this way, by adjusting the range of information to be collected according to the user's emotions, more appropriate information can be collected.

[0127] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is in a hurry, it can prioritize analyzing the most important information (e.g., the location of the restroom). If the user is relaxed, it can analyze detailed information (e.g., information about the restroom facilities). Furthermore, if the user is stressed, it can analyze simple information (e.g., the cleanliness of the restroom). By adjusting the analysis algorithm according to the user's emotions, it is possible to provide more appropriate analysis results.

[0128] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is stressed, the suggestion unit can provide simple, highly visible suggestions. In this way, by adjusting the length of the suggestions according to the user's emotions, more appropriate information can be provided.

[0129] The real-time acquisition unit can estimate the user's emotions and adjust the frequency of acquisition of real-time information based on the estimated user's emotions. For example, if the user is in a hurry, the frequency of acquisition of real-time information can be increased. Also, if the user is relaxed, the frequency of acquisition of real-time information can be decreased. Furthermore, if the user is feeling stressed, the frequency of acquisition of real-time information can be adjusted. In this way, by adjusting the frequency of acquisition of real-time information according to the user's emotions, more appropriate information can be provided.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The reception unit receives user input. User input includes text input, voice input, touch input, etc. For example, a user can inquire about the location of a restroom or facility information in chat format. Step 2: The collection unit collects the user's current location or toilet facility information based on the information received by the reception unit. Methods for acquiring the current location include GPS, Wi-Fi location information, cell tower location information, etc. The toilet facility information includes the cleanliness of the toilet, available hours, type of facilities, etc. Step 3: The analysis unit analyzes the information collected by the collection unit and selects toilets based on the user's request. The analysis includes the type of algorithm, the accuracy of the analysis, and the data used. Step 4: The suggestion unit suggests toilet information to the user based on the analysis results obtained by the analysis unit. The suggestion may be in the form of a text message, a voice message, or the like. Step 5: The real-time acquisition unit acquires information on the congestion status or cleaning status of the restroom in real time. The definition of real-time includes the update frequency of the information, delay time, data source, etc.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 AI 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 AI 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, a 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 AI 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Explanation of symbols]

[0204] 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 input from a user; a collection unit that collects information about the user's current location or toilet facilities based on the information received by the reception unit; an analysis unit that analyzes the information collected by the collection unit and selects a toilet based on a user's request; a suggestion unit that suggests toilet information to a user based on the analysis result obtained by the analysis unit; a real-time acquisition unit that acquires information in real time; A system characterized by:

2. The reception unit Accept user input in chat format 2. The system of claim 1.

3. The collecting unit Collect user location and restroom facility information 2. The system of claim 1.

4. The analysis unit Analyze the collected information and select a toilet based on the user's requirements 2. The system of claim 1.

5. The proposal unit Providing toilet information to users based on analysis results 2. The system of claim 1.

6. The real-time acquisition unit Get real-time information on restroom congestion or cleanliness 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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