system

Abstract

The system according to this embodiment aims to search for the most suitable facility based on the user's conditions and to simplify the procedure. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, and a procedure unit. The reception unit receives user information. The analysis unit analyzes the information entered by the reception unit. The search unit searches for available facilities based on the information analyzed by the analysis unit. The guidance unit provides information on how to use the facilities found by the search unit and the procedures involved. The procedure unit handles facility tour reservations and admission procedures based on the information provided by the guidance unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently find a facility that meets the conditions desired by the user, and the procedure is complicated.

[0005] The system according to the embodiment aims to search for an optimal facility based on the user's conditions and simplify the procedure.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, and a procedure unit. The reception unit receives user input. The analysis unit analyzes the information entered by the reception unit. The search unit searches for available facilities based on the information analyzed by the analysis unit. The guidance unit provides information on how to use the facilities found by the search unit and the procedures involved. The procedure unit handles facility tour reservations and admission procedures based on the information provided by the guidance unit. [Effects of the Invention]

[0007] The system according to this embodiment can search for the most suitable facility based on the user's conditions and simplify the procedure. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The facility guidance system according to an embodiment of the present invention is a system that, while conversing with an AI, allows users to input conditions and preferences such as desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. This allows the system to uncover needs that the user may not be aware of or have forgotten, find available facilities, and provide guidance on how to use each facility and the procedures for each residential area. Furthermore, optional services are available for those who wish to receive guidance and assistance with scheduling visits, subsequent admission procedures, and the procedures for obtaining long-term care certification tailored to their residential area. First, the user converses with the AI ​​and inputs conditions and preferences such as desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. For example, the user might input specific conditions such as, "I'm looking for a facility in Tokyo that I can move into from next month. My budget is within 200,000 yen per month, and the person receiving care is at care level 2." This information is input into the AI. Next, the AI ​​analyzes the input information and uncovers needs that the user may not be aware of or have forgotten. For example, when the user inputs their budget and desired move-in date, the AI ​​can use that information to suggest facility features and services that the user had not considered. This allows the user to find the facility best suited to their needs. Furthermore, the AI ​​searches for available facilities and provides guidance on how to use each facility and the procedures specific to the user's residential area. For example, the AI ​​lists facilities that meet the user's criteria and provides detailed information on how to use each facility and the procedures involved. This makes it easier for users to compare and consider facility options. Additionally, there are options for assistance with scheduling visits, subsequent admission procedures, and the application process for long-term care certification tailored to the user's residential area. For instance, if a user wishes to visit a facility, the AI ​​will schedule the visit and guide them through the admission procedures afterward. Furthermore, the AI ​​provides necessary information and assistance with the application process for long-term care certification tailored to the user's residential area. This system allows users to obtain fresh and "real" information, reducing the burden of facility selection and procedures. For example, users no longer need to make numerous phone calls amidst their busy work and family schedules; the AI ​​collects information and suggests the most suitable facilities. The AI ​​also guides users through the long-term care certification process, saving them the trouble of visiting government offices.This allows the facility guidance system to efficiently input, analyze, search, guide, and process user information.

[0029] The facility guidance system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, and a procedure unit. The reception unit receives user information. User information includes, but is not limited to, the desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. The reception unit can receive user information using, for example, voice input or text input. The analysis unit analyzes the information entered by the reception unit. The analysis unit can, for example, use AI to analyze the user's input information and uncover needs that the user may not be aware of. For example, the analysis unit can suggest facility features and services that the user had not considered based on the budget and desired move-in date entered by the user. The search unit searches for available facilities based on the information analyzed by the analysis unit. The search unit can, for example, use AI to list facilities that match the user's conditions. For example, the search unit can search for the optimal facility based on the user's desired area and budget. The guidance unit provides information on how to use the facilities found by the search unit and the procedures involved. The information department can, for example, use AI to provide detailed information about how to use each facility and the procedures involved. For example, the information department can provide text or voice guidance about how to use the facilities and the procedures involved. The procedures department handles facility tour reservations and admission procedures as instructed by the information department. For example, the procedures department can use AI to make tour reservations and also guide users through the admission procedures after the tour. For example, the procedures department can make reservations for facilities that users wish to visit and also guide users through the admission procedures after the tour. As a result, the facility guidance system according to this embodiment can efficiently input, analyze, search, guide, and process user conditions.

[0030] The reception desk inputs the user's requirements. These requirements may include, but are not limited to, the desired area, desired move-in date, budget, and the condition of the care recipient. The reception desk can accept user requirements using methods such as voice input or text input. Specifically, with voice input, the user speaks into a microphone, the system recognizes the voice, and converts it into text data. Voice recognition technology uses noise cancellation and voice filtering to ensure accurate input. With text input, the user enters the requirements using a keyboard or touchscreen. This allows the user to input their preferences and requirements in detail. Furthermore, the reception desk has a function to temporarily save the user's entered requirements, allowing for later modification or addition. For example, the user can review the requirements they have entered and change them as needed. This ensures that the user accurately reflects their requirements. The reception desk also automatically categorizes the user's entered requirements and checks the data integrity before sending it to the analysis department. This allows the analysis department to process the data efficiently.

[0031] The analysis department analyzes the information entered by the reception department. For example, the analysis department can use AI to analyze user input information and uncover needs that users may not be aware of. Specifically, it uses natural language processing technology to understand user input and extract relevant information. For example, if a user enters "I'm looking for a place where I can relax in a quiet environment," the analysis department will extract keywords such as "quiet environment" and "relaxation" and suggest the characteristics of facilities related to them. It also uses machine learning algorithms to analyze past user data and facility evaluation data to identify the facility that best suits the user's conditions. Furthermore, the analysis department can predict future needs and potential demands based on user input information. For example, if a user enters their current budget and desired move-in date, the analysis department can use that information to suggest services and facility characteristics that will be needed in the future. In this way, the analysis department can analyze the user's conditions in detail and make optimal suggestions. In addition, the analysis department has a function to visually display the analysis results, providing information in a way that is easy for users to understand. For example, it can use graphs and charts to visually show the characteristics and evaluations of facilities that match the user's conditions. This allows users to easily find the facility that best suits their needs.

[0032] The search unit searches for available facilities based on information analyzed by the analysis unit. For example, the search unit can use AI to list facilities that match the user's criteria. Specifically, it searches facility information stored in the database to identify the facility best suited to the user's requirements. The search unit searches for the optimal facility based on the user's desired area, budget, and desired move-in date. For example, if a user enters "Tokyo area, budget under 100,000 yen per month, desired move-in within 3 months," the search unit will list facilities that meet those conditions. The search unit also considers facility ratings and reviews when providing search results, allowing users to choose reliable facilities. Furthermore, the search unit has a function to display search results in an easy-to-understand format. For example, it can display search results on a map, visually showing the location and characteristics of each facility. This allows users to easily find the facility best suited to their needs. Additionally, the search unit provides a filtering function to help users narrow down their search results. For example, users can narrow down search results based on facility type, services offered, and the availability of equipment. This allows users to efficiently find the facility that best suits their needs.

[0033] The information department guides users through the usage methods and procedures for facilities found by the search department. For example, the information department can use AI to provide detailed information about the usage methods and procedures for each facility. Specifically, it retrieves information about the usage methods and procedures for each facility from a database and provides it to the user. For example, it can provide information such as facility usage fees, contract terms, and required documents. The information department conveys information to users in an easy-to-understand manner using text and voice guidance. For example, if a user asks about how to use a desired facility, the information department will provide an appropriate answer. The information department also provides visual information to help users easily understand the usage methods and procedures. For example, it can illustrate the flow of usage methods and procedures with diagrams. This allows users to easily understand the usage methods and procedures. Furthermore, the information department has a function to provide real-time support if users have any questions about the usage methods and procedures of a facility. For example, it can use a chatbot to immediately answer user questions. This allows users to proceed with facility usage methods and procedures with confidence.

[0034] The Procedures Department handles facility tour reservations and admission procedures as instructed by the Information Department. For example, the Procedures Department can use AI to make tour reservations and guide users through the admission process after the tour. Specifically, it makes tour reservations for facilities the user desires and provides detailed guidance on the admission process after the tour. For instance, when a user enters their desired date and time for a tour, the Procedures Department uses this information to coordinate with the facility and confirm the tour reservation. It also provides detailed guidance on the necessary documents and procedures for admission after the tour. The Procedures Department provides necessary information and support to ensure users can proceed smoothly through the process. For example, it provides download links for necessary documents and offers a function to check the progress of the process in real time. Furthermore, the Procedures Department has a function to provide real-time support if users have questions during the process. For example, it can immediately answer user questions through chatbots or customer support. This allows users to proceed with the process with peace of mind. Additionally, the Procedures Department has a function to notify users of the progress of the process, allowing users to always be aware of its status. This allows the procedures department to support users in completing procedures smoothly and improve the overall usability of the facility guidance system.

[0035] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The reception unit can customize input fields based on the user's current living situation and areas of interest when conditions are entered. For example, when the user enters their current living situation, the reception unit can automatically display relevant input fields. The reception unit can also prioritize the display of relevant input fields based on the user's areas of interest. Furthermore, the reception unit can analyze the user's living situation and areas of interest and suggest the most suitable input fields. This improves the accuracy of input by providing input fields that are tailored to the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's living situation data into a generating AI and have the generating AI suggest the most suitable input fields.

[0037] The reception unit can prioritize displaying highly relevant input fields when the user enters conditions, taking into account the user's geographical location information. For example, when the user enters their current location, the reception unit can automatically display relevant facilities and services. The reception unit can also prioritize displaying relevant input fields based on the user's geographical location information. Furthermore, the reception unit can analyze the user's geographical location information and suggest the most suitable input fields. This improves input accuracy by providing input fields based on geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant input fields.

[0038] The reception unit can analyze the user's social media activity when conditions are entered and suggest relevant input fields. For example, the reception unit can automatically display facilities and services of interest based on the user's social media activity. The reception unit can also prioritize the display of relevant input fields based on the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest the most suitable input fields. This improves the accuracy of input by providing input fields based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0039] The analysis unit can optimize the analysis algorithm by referring to the user's past input information during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past input information. The analysis unit can also analyze the user's past input information and optimize the analysis algorithm. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past input information. This improves the accuracy of the analysis by optimizing the analysis algorithm based on past input information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past input data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0040] The analysis unit can improve the accuracy of its analysis based on the user's lifestyle and areas of interest during the analysis process. For example, when the user's lifestyle is entered, the analysis unit automatically displays relevant analysis items. The analysis unit can also prioritize the display of relevant analysis items based on the user's areas of interest. Furthermore, the analysis unit can analyze the user's lifestyle and areas of interest and propose the most suitable analysis items. This improves the accuracy of the analysis by performing analysis based on the user's lifestyle and areas of interest. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI propose analysis items.

[0041] The analysis unit can improve the accuracy of the analysis by considering the user's geographical location information during the analysis. For example, the analysis unit can select the optimal analysis method based on the user's geographical location information. The analysis unit can also analyze the user's geographical location information and optimize the analysis algorithm. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's geographical location information. This improves the accuracy of the analysis by performing analysis based on geographical location information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0042] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can automatically display facilities and services of interest based on the user's social media activity. The analysis unit can also prioritize the display of relevant analysis results based on the user's social media activity. Furthermore, the analysis unit can analyze the user's social media activity and suggest optimal analysis results. This improves the accuracy of the analysis by providing analysis results based on social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant analysis results.

[0043] The search unit can improve search accuracy by considering the interrelationships between facilities during the search process. For example, the search unit provides optimal search results based on the interrelationships between facilities. The search unit can also analyze the interrelationships between facilities and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the interrelationships between facilities. This improves search accuracy by performing searches based on the interrelationships between facilities. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input facility interrelationship data into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0044] The search unit can perform searches while considering the attribute information of the facility provider. For example, the search unit can provide the best search results based on the attribute information of the facility provider. The search unit can also analyze the attribute information of the facility provider and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the attribute information of the facility provider. This improves the accuracy of the search by performing searches based on the provider's attribute information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the attribute information data of the facility provider into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0045] The search unit can perform searches while considering the geographical distribution of facilities. For example, the search unit can provide optimal search results based on the geographical distribution of facilities. The search unit can also analyze the geographical distribution of facilities and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the geographical distribution of facilities. This improves the accuracy of searches by performing searches based on geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of facilities into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0046] The search unit can improve search accuracy by referring to relevant literature for the facility during the search process. For example, the search unit provides optimal search results based on the relevant literature for the facility. The search unit can also analyze the relevant literature for the facility and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the relevant literature for the facility. This improves search accuracy by performing searches based on relevant literature. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relevant literature data for the facility into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0047] The guidance unit can optimize the current guidance by referring to past guidance data during guidance. For example, the guidance unit can select the optimal guidance method based on past guidance data. The guidance unit can also analyze past guidance data and optimize the guidance algorithm. Furthermore, the guidance unit can adjust the guidance algorithm by referring to past guidance data. In this way, the accuracy of guidance can be improved by optimizing the current guidance based on past guidance data. Some or all of the above processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past guidance data into a generating AI and have the generating AI perform the optimization of the guidance algorithm.

[0048] The guidance unit can apply different guidance methods depending on the facility category during the guidance process. For example, in the case of a nursing care facility, the guidance unit can focus on providing detailed information about care services. In the case of senior housing, the guidance unit can focus on providing detailed information about the living environment and facilities. Furthermore, in the case of a medical facility, the guidance unit can focus on providing information about medical services and specialists. By providing guidance methods tailored to the facility category, the accuracy of the guidance can be improved. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input facility category data into a generating AI and have the generating AI perform optimization of the guidance method.

[0049] The guidance unit can determine the priority of guidance based on the timing of facility use. For example, the guidance unit will prioritize guidance to facilities that are available in the near future. The guidance unit can also provide guidance with detailed information if the usage date is in the future. Furthermore, the guidance unit can select the most appropriate guidance method depending on the timing of use. This improves the accuracy of guidance by providing guidance according to the timing of use. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input facility usage time data into a generating AI and have the generating AI perform the determination of guidance priorities.

[0050] The guidance unit can provide guidance by referring to relevant market data for the facility. For example, the guidance unit can select the optimal guidance method based on the relevant market data for the facility. The guidance unit can also analyze the relevant market data for the facility and optimize the guidance algorithm. Furthermore, the guidance unit can adjust the guidance algorithm by referring to the relevant market data for the facility. This improves the accuracy of the guidance by providing guidance based on relevant market data. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the relevant market data for the facility into a generating AI and have the generating AI perform the optimization of the guidance algorithm.

[0051] The procedure unit can select the optimal procedure method by referring to the user's past procedure history during a procedure. For example, the procedure unit selects the optimal procedure method based on the user's past procedure history. The procedure unit can also analyze the user's past procedure history and optimize the procedure algorithm. Furthermore, the procedure unit can adjust the procedure algorithm by referring to the user's past procedure history. This improves the efficiency of procedures by providing the optimal procedure method based on past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's past procedure history data into a generating AI and have the generating AI perform the optimization of the procedure algorithm.

[0052] The procedure unit can customize the means of the procedure based on the user's current living situation during the procedure. For example, when the user inputs their current living situation, the procedure unit can automatically display relevant procedures. The procedure unit can also prioritize the display of relevant procedures based on the user's living situation. Furthermore, the procedure unit can analyze the user's living situation and suggest the most suitable procedures. This improves the accuracy of the procedure by providing procedures tailored to the user's living situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's living situation data into a generating AI and have the generating AI suggest procedures.

[0053] The procedure unit can select the optimal procedure method while considering the user's geographical location information. For example, the procedure unit can select the optimal procedure method based on the user's geographical location information. The procedure unit can also analyze the user's geographical location information and optimize the procedure algorithm. Furthermore, the procedure unit can adjust the procedure algorithm by referring to the user's geographical location information. This improves the accuracy of the procedure by providing a procedure method based on geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's geographical location information data into a generating AI and have the generating AI perform the optimization of the procedure algorithm.

[0054] The procedure unit can analyze the user's social media activity during a procedure and suggest a procedure. For example, the procedure unit can automatically display a procedure of interest based on the user's social media activity. The procedure unit can also prioritize the display of relevant procedures based on the user's social media activity. Furthermore, the procedure unit can analyze the user's social media activity and suggest the most suitable procedure. This improves the accuracy of the procedure by providing a procedure based on social media activity. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's social media activity data into a generating AI and have the generating AI execute the suggestion of a procedure.

[0055] The procedure unit can, during a procedure, refer to the user's calendar information and propose a procedure method based on their schedule. For example, the procedure unit can refer to the schedule registered in the user's calendar and propose the most suitable procedure method. The procedure unit can also propose a procedure related to a specific event based on the user's calendar information. Furthermore, the procedure unit can propose a procedure method tailored to the schedule based on the user's calendar information. This improves the efficiency of procedures by providing a procedure method based on calendar information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's calendar information data into a generating AI and have the generating AI execute the procedure method proposal.

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

[0057] The analysis unit can analyze a user's past condition input history and suggest the optimal input method. For example, it can automatically display conditions that the user has frequently entered in the past as candidates. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions that the user will use during specific time periods based on their past input history. In this way, input efficiency can be improved by suggesting the optimal input method based on past history.

[0058] The reception desk can customize input fields based on the user's current lifestyle and areas of interest when they enter conditions. For example, when a user enters their current lifestyle, it can automatically display relevant input fields. It can also prioritize the display of relevant input fields based on the user's areas of interest. Furthermore, it can analyze the user's lifestyle and areas of interest and suggest the most suitable input fields. This improves the accuracy of input by providing input fields that are tailored to the user's lifestyle and areas of interest.

[0059] The search unit can improve search accuracy by considering the interrelationships between facilities during the search process. For example, it can provide optimal search results based on the interrelationships between facilities. It can also analyze the interrelationships between facilities and optimize the search algorithm. Furthermore, it can adjust the search algorithm by referring to the interrelationships between facilities. This allows for improved search accuracy by performing searches based on the interrelationships between facilities.

[0060] The guidance system can optimize the current guidance by referring to past guidance data. For example, it can select the optimal guidance method based on past guidance data. It can also analyze past guidance data and optimize the guidance algorithm. Furthermore, it can adjust the guidance algorithm by referring to past guidance data. In this way, the accuracy of guidance can be improved by optimizing the current guidance based on past guidance data.

[0061] The procedure unit can select the optimal procedure method by referring to the user's past procedure history during a procedure. For example, it can select the optimal procedure method based on the user's past procedure history. It can also analyze the user's past procedure history and optimize the procedure algorithm. Furthermore, it can adjust the procedure algorithm by referring to the user's past procedure history. This improves the efficiency of procedures by providing the optimal procedure method based on past procedure history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk enters the user's requirements. These requirements include the desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. The reception desk can accept user requirements using voice input or text input. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI to analyze the user's input information and uncover needs that the user may not have been aware of. For example, based on the budget and desired move-in date entered by the user, it can suggest facility features and services that the user had not considered. Step 3: The search unit searches for available facilities based on the information analyzed by the analysis unit. The search unit can use AI to list facilities that meet the user's criteria. For example, it can search for the most suitable facility based on the user's desired area and budget. Step 4: The information desk provides guidance on how to use and the procedures for the facilities found by the search desk. The information desk can use AI to provide detailed information on how to use and the procedures for each facility. For example, it can provide text guidance or voice guidance. Step 5: The Procedures Department handles facility tour reservations and admission procedures as instructed by the Information Department. The Procedures Department can use AI to make tour reservations and guide users through the admission procedures after the tour. For example, it can make reservations for facilities that users wish to visit and guide them through the admission procedures after the tour.

[0064] (Example of form 2) The facility guidance system according to an embodiment of the present invention is a system that, while conversing with an AI, allows users to input conditions and preferences such as desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. This allows the system to uncover needs that the user may not be aware of or have forgotten, find available facilities, and provide guidance on how to use each facility and the procedures for each residential area. Furthermore, optional services are available for those who wish to receive guidance and assistance with scheduling visits, subsequent admission procedures, and the procedures for obtaining long-term care certification tailored to their residential area. First, the user converses with the AI ​​and inputs conditions and preferences such as desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. For example, the user might input specific conditions such as, "I'm looking for a facility in Tokyo that I can move into from next month. My budget is within 200,000 yen per month, and the person receiving care is at care level 2." This information is input into the AI. Next, the AI ​​analyzes the input information and uncovers needs that the user may not be aware of or have forgotten. For example, when the user inputs their budget and desired move-in date, the AI ​​can use that information to suggest facility features and services that the user had not considered. This allows the user to find the facility best suited to their needs. Furthermore, the AI ​​searches for available facilities and provides guidance on how to use each facility and the procedures specific to the user's residential area. For example, the AI ​​lists facilities that meet the user's criteria and provides detailed information on how to use each facility and the procedures involved. This makes it easier for users to compare and consider facility options. Additionally, there are options for assistance with scheduling visits, subsequent admission procedures, and the application process for long-term care certification tailored to the user's residential area. For instance, if a user wishes to visit a facility, the AI ​​will schedule the visit and guide them through the admission procedures afterward. Furthermore, the AI ​​provides necessary information and assistance with the application process for long-term care certification tailored to the user's residential area. This system allows users to obtain fresh and "real" information, reducing the burden of facility selection and procedures. For example, users no longer need to make numerous phone calls amidst their busy work and family schedules; the AI ​​collects information and suggests the most suitable facilities. The AI ​​also guides users through the long-term care certification process, saving them the trouble of visiting government offices.This allows the facility guidance system to efficiently input, analyze, search, guide, and process user information.

[0065] The facility guidance system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, and a procedure unit. The reception unit receives user information. User information includes, but is not limited to, the desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. The reception unit can receive user information using, for example, voice input or text input. The analysis unit analyzes the information entered by the reception unit. The analysis unit can, for example, use AI to analyze the user's input information and uncover needs that the user may not be aware of. For example, the analysis unit can suggest facility features and services that the user had not considered based on the budget and desired move-in date entered by the user. The search unit searches for available facilities based on the information analyzed by the analysis unit. The search unit can, for example, use AI to list facilities that match the user's conditions. For example, the search unit can search for the optimal facility based on the user's desired area and budget. The guidance unit provides information on how to use the facilities found by the search unit and the procedures involved. The information department can, for example, use AI to provide detailed information about how to use each facility and the procedures involved. For example, the information department can provide text or voice guidance about how to use the facilities and the procedures involved. The procedures department handles facility tour reservations and admission procedures as instructed by the information department. For example, the procedures department can use AI to make tour reservations and also guide users through the admission procedures after the tour. For example, the procedures department can make reservations for facilities that users wish to visit and also guide users through the admission procedures after the tour. As a result, the facility guidance system according to this embodiment can efficiently input, analyze, search, guide, and process user conditions.

[0066] The reception desk inputs the user's requirements. These requirements may include, but are not limited to, the desired area, desired move-in date, budget, and the condition of the care recipient. The reception desk can accept user requirements using methods such as voice input or text input. Specifically, with voice input, the user speaks into a microphone, the system recognizes the voice, and converts it into text data. Voice recognition technology uses noise cancellation and voice filtering to ensure accurate input. With text input, the user enters the requirements using a keyboard or touchscreen. This allows the user to input their preferences and requirements in detail. Furthermore, the reception desk has a function to temporarily save the user's entered requirements, allowing for later modification or addition. For example, the user can review the requirements they have entered and change them as needed. This ensures that the user accurately reflects their requirements. The reception desk also automatically categorizes the user's entered requirements and checks the data integrity before sending it to the analysis department. This allows the analysis department to process the data efficiently.

[0067] The analysis department analyzes the information entered by the reception department. For example, the analysis department can use AI to analyze user input information and uncover needs that users may not be aware of. Specifically, it uses natural language processing technology to understand user input and extract relevant information. For example, if a user enters "I'm looking for a place where I can relax in a quiet environment," the analysis department will extract keywords such as "quiet environment" and "relaxation" and suggest the characteristics of facilities related to them. It also uses machine learning algorithms to analyze past user data and facility evaluation data to identify the facility that best suits the user's conditions. Furthermore, the analysis department can predict future needs and potential demands based on user input information. For example, if a user enters their current budget and desired move-in date, the analysis department can use that information to suggest services and facility characteristics that will be needed in the future. In this way, the analysis department can analyze the user's conditions in detail and make optimal suggestions. In addition, the analysis department has a function to visually display the analysis results, providing information in a way that is easy for users to understand. For example, it can use graphs and charts to visually show the characteristics and evaluations of facilities that match the user's conditions. This allows users to easily find the facility that best suits their needs.

[0068] The search unit searches for available facilities based on information analyzed by the analysis unit. For example, the search unit can use AI to list facilities that match the user's criteria. Specifically, it searches facility information stored in the database to identify the facility best suited to the user's requirements. The search unit searches for the optimal facility based on the user's desired area, budget, and desired move-in date. For example, if a user enters "Tokyo area, budget under 100,000 yen per month, desired move-in within 3 months," the search unit will list facilities that meet those conditions. The search unit also considers facility ratings and reviews when providing search results, allowing users to choose reliable facilities. Furthermore, the search unit has a function to display search results in an easy-to-understand format. For example, it can display search results on a map, visually showing the location and characteristics of each facility. This allows users to easily find the facility best suited to their needs. Additionally, the search unit provides a filtering function to help users narrow down their search results. For example, users can narrow down search results based on facility type, services offered, and the availability of equipment. This allows users to efficiently find the facility that best suits their needs.

[0069] The information department guides users through the usage methods and procedures for facilities found by the search department. For example, the information department can use AI to provide detailed information about the usage methods and procedures for each facility. Specifically, it retrieves information about the usage methods and procedures for each facility from a database and provides it to the user. For example, it can provide information such as facility usage fees, contract terms, and required documents. The information department conveys information to users in an easy-to-understand manner using text and voice guidance. For example, if a user asks about how to use a desired facility, the information department will provide an appropriate answer. The information department also provides visual information to help users easily understand the usage methods and procedures. For example, it can illustrate the flow of usage methods and procedures with diagrams. This allows users to easily understand the usage methods and procedures. Furthermore, the information department has a function to provide real-time support if users have any questions about the usage methods and procedures of a facility. For example, it can use a chatbot to immediately answer user questions. This allows users to proceed with facility usage methods and procedures with confidence.

[0070] The Procedures Department handles facility tour reservations and admission procedures as instructed by the Information Department. For example, the Procedures Department can use AI to make tour reservations and guide users through the admission process after the tour. Specifically, it makes tour reservations for facilities the user desires and provides detailed guidance on the admission process after the tour. For instance, when a user enters their desired date and time for a tour, the Procedures Department uses this information to coordinate with the facility and confirm the tour reservation. It also provides detailed guidance on the necessary documents and procedures for admission after the tour. The Procedures Department provides necessary information and support to ensure users can proceed smoothly through the process. For example, it provides download links for necessary documents and offers a function to check the progress of the process in real time. Furthermore, the Procedures Department has a function to provide real-time support if users have questions during the process. For example, it can immediately answer user questions through chatbots or customer support. This allows users to proceed with the process with peace of mind. Additionally, the Procedures Department has a function to notify users of the progress of the process, allowing users to always be aware of its status. This allows the procedures department to support users in completing procedures smoothly and improve the overall usability of the facility guidance system.

[0071] The reception unit can estimate the user's emotions and adjust the input interface based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow for quick input of conditions. This reduces the burden of input by providing an interface that responds 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0073] The reception unit can customize input fields based on the user's current living situation and areas of interest when conditions are entered. For example, when the user enters their current living situation, the reception unit can automatically display relevant input fields. The reception unit can also prioritize the display of relevant input fields based on the user's areas of interest. Furthermore, the reception unit can analyze the user's living situation and areas of interest and suggest the most suitable input fields. This improves the accuracy of input by providing input fields that are tailored to the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's living situation data into a generating AI and have the generating AI suggest the most suitable input fields.

[0074] The reception desk can estimate the user's emotions and determine the priority of input conditions based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize inputting important conditions. If the user is relaxed, the reception desk may also prioritize inputting detailed conditions. Furthermore, if the user is in a hurry, the reception desk may prioritize inputting the most important conditions. This allows important conditions to be prioritized by determining the priority of conditions 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception unit can prioritize displaying highly relevant input fields when the user enters conditions, taking into account the user's geographical location information. For example, when the user enters their current location, the reception unit can automatically display relevant facilities and services. The reception unit can also prioritize displaying relevant input fields based on the user's geographical location information. Furthermore, the reception unit can analyze the user's geographical location information and suggest the most suitable input fields. This improves input accuracy by providing input fields based on geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant input fields.

[0076] The reception unit can analyze the user's social media activity when conditions are entered and suggest relevant input fields. For example, the reception unit can automatically display facilities and services of interest based on the user's social media activity. The reception unit can also prioritize the display of relevant input fields based on the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest the most suitable input fields. This improves the accuracy of input by providing input fields based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis method. It can also provide a more detailed analysis method if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a rapid analysis method. This improves the accuracy of the analysis by providing an analysis method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The analysis unit can optimize the analysis algorithm by referring to the user's past input information during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past input information. The analysis unit can also analyze the user's past input information and optimize the analysis algorithm. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past input information. This improves the accuracy of the analysis by optimizing the analysis algorithm based on past input information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past input data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0079] The analysis unit can improve the accuracy of its analysis based on the user's lifestyle and areas of interest during the analysis process. For example, when the user's lifestyle is entered, the analysis unit automatically displays relevant analysis items. The analysis unit can also prioritize the display of relevant analysis items based on the user's areas of interest. Furthermore, the analysis unit can analyze the user's lifestyle and areas of interest and propose the most suitable analysis items. This improves the accuracy of the analysis by performing analysis based on the user's lifestyle and areas of interest. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI propose analysis items.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By providing a display method that matches the user's emotions, the understanding of the analysis results can be facilitated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can improve the accuracy of the analysis by considering the user's geographical location information during the analysis. For example, the analysis unit can select the optimal analysis method based on the user's geographical location information. The analysis unit can also analyze the user's geographical location information and optimize the analysis algorithm. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's geographical location information. This improves the accuracy of the analysis by performing analysis based on geographical location information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0082] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can automatically display facilities and services of interest based on the user's social media activity. The analysis unit can also prioritize the display of relevant analysis results based on the user's social media activity. Furthermore, the analysis unit can analyze the user's social media activity and suggest optimal analysis results. This improves the accuracy of the analysis by providing analysis results based on social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant analysis results.

[0083] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is stressed, the search unit can provide simple search criteria. If the user is relaxed, the search unit can also provide detailed search criteria. Furthermore, if the user is in a hurry, the search unit can provide rapid search criteria. This improves search accuracy by providing search criteria that are tailored 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The search unit can improve search accuracy by considering the interrelationships between facilities during the search process. For example, the search unit provides optimal search results based on the interrelationships between facilities. The search unit can also analyze the interrelationships between facilities and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the interrelationships between facilities. This improves search accuracy by performing searches based on the interrelationships between facilities. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input facility interrelationship data into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0085] The search unit can perform searches while considering the attribute information of the facility provider. For example, the search unit can provide the best search results based on the attribute information of the facility provider. The search unit can also analyze the attribute information of the facility provider and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the attribute information of the facility provider. This improves the accuracy of the search by performing searches based on the provider's attribute information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the attribute information data of the facility provider into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0086] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is nervous, the search unit can provide a simple and highly visible display order. If the user is relaxed, the search unit can also provide a display order that includes detailed information. Furthermore, if the user is in a hurry, the search unit can provide a concise display order. This facilitates understanding of search results by providing a display order that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The search unit can perform searches while considering the geographical distribution of facilities. For example, the search unit can provide optimal search results based on the geographical distribution of facilities. The search unit can also analyze the geographical distribution of facilities and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the geographical distribution of facilities. This improves the accuracy of searches by performing searches based on geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of facilities into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0088] The search unit can improve search accuracy by referring to relevant literature for the facility during the search process. For example, the search unit provides optimal search results based on the relevant literature for the facility. The search unit can also analyze the relevant literature for the facility and optimize the search algorithm. Furthermore, the search unit can adjust the search algorithm by referring to the relevant literature for the facility. This improves search accuracy by performing searches based on relevant literature. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relevant literature data for the facility into a generating AI and have the generating AI perform the optimization of the search algorithm.

[0089] The guidance unit can estimate the user's emotions and adjust the way the guidance is displayed based on the estimated emotions. For example, if the user is nervous, the guidance unit can provide a simple and highly visible display method. If the user is relaxed, the guidance unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide a display method that gets straight to the point. This facilitates understanding of the guidance by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The guidance unit can optimize the current guidance by referring to past guidance data during guidance. For example, the guidance unit can select the optimal guidance method based on past guidance data. The guidance unit can also analyze past guidance data and optimize the guidance algorithm. Furthermore, the guidance unit can adjust the guidance algorithm by referring to past guidance data. In this way, the accuracy of guidance can be improved by optimizing the current guidance based on past guidance data. Some or all of the above processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past guidance data into a generating AI and have the generating AI perform the optimization of the guidance algorithm.

[0091] The guidance unit can apply different guidance methods depending on the facility category during the guidance process. For example, in the case of a nursing care facility, the guidance unit can focus on providing detailed information about care services. In the case of senior housing, the guidance unit can focus on providing detailed information about the living environment and facilities. Furthermore, in the case of a medical facility, the guidance unit can focus on providing information about medical services and specialists. By providing guidance methods tailored to the facility category, the accuracy of the guidance can be improved. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input facility category data into a generating AI and have the generating AI perform optimization of the guidance method.

[0092] The guidance unit can estimate the user's emotions and adjust the importance of the guidance based on the estimated emotions. For example, if the user is nervous, the guidance unit will prioritize providing important information. If the user is relaxed, the guidance unit can also provide guidance that includes detailed information. Furthermore, if the user is in a hurry, the guidance unit can provide guidance that gets to the point. This facilitates understanding of the guidance by providing importance levels 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI or not using AI. For example, the guidance unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The guidance unit can determine the priority of guidance based on the timing of facility use. For example, the guidance unit will prioritize guidance to facilities that are available in the near future. The guidance unit can also provide guidance with detailed information if the usage date is in the future. Furthermore, the guidance unit can select the most appropriate guidance method depending on the timing of use. This improves the accuracy of guidance by providing guidance according to the timing of use. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input facility usage time data into a generating AI and have the generating AI perform the determination of guidance priorities.

[0094] The guidance unit can provide guidance by referring to relevant market data for the facility. For example, the guidance unit can select the optimal guidance method based on the relevant market data for the facility. The guidance unit can also analyze the relevant market data for the facility and optimize the guidance algorithm. Furthermore, the guidance unit can adjust the guidance algorithm by referring to the relevant market data for the facility. This improves the accuracy of the guidance by providing guidance based on relevant market data. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the relevant market data for the facility into a generating AI and have the generating AI perform the optimization of the guidance algorithm.

[0095] The procedure unit can estimate the user's emotions and adjust the procedure based on the estimated emotions. For example, if the user is stressed, the procedure unit can provide a simple procedure. If the user is relaxed, the procedure unit can also provide a detailed procedure. Furthermore, if the user is in a hurry, the procedure unit can provide a quick procedure. This reduces the burden of the procedure by providing a procedure that is appropriate 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using AI, or not using AI. For example, the procedure unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The procedure unit can select the optimal procedure method by referring to the user's past procedure history during a procedure. For example, the procedure unit selects the optimal procedure method based on the user's past procedure history. The procedure unit can also analyze the user's past procedure history and optimize the procedure algorithm. Furthermore, the procedure unit can adjust the procedure algorithm by referring to the user's past procedure history. This improves the efficiency of procedures by providing the optimal procedure method based on past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's past procedure history data into a generating AI and have the generating AI perform the optimization of the procedure algorithm.

[0097] The procedure unit can customize the means of the procedure based on the user's current living situation during the procedure. For example, when the user inputs their current living situation, the procedure unit can automatically display relevant procedures. The procedure unit can also prioritize the display of relevant procedures based on the user's living situation. Furthermore, the procedure unit can analyze the user's living situation and suggest the most suitable procedures. This improves the accuracy of the procedure by providing procedures tailored to the user's living situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's living situation data into a generating AI and have the generating AI suggest procedures.

[0098] The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated emotions. For example, if the user is stressed, the procedure unit will prioritize important procedures. If the user is relaxed, the procedure unit can also perform detailed procedures. Furthermore, if the user is in a hurry, the procedure unit can prioritize the most important procedures. This improves the efficiency of procedures by providing priorities 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using AI or not using AI. For example, the procedure unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The procedure unit can select the optimal procedure method while considering the user's geographical location information. For example, the procedure unit can select the optimal procedure method based on the user's geographical location information. The procedure unit can also analyze the user's geographical location information and optimize the procedure algorithm. Furthermore, the procedure unit can adjust the procedure algorithm by referring to the user's geographical location information. This improves the accuracy of the procedure by providing a procedure method based on geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's geographical location information data into a generating AI and have the generating AI perform the optimization of the procedure algorithm.

[0100] The procedure unit can analyze the user's social media activity during a procedure and suggest a procedure. For example, the procedure unit can automatically display a procedure of interest based on the user's social media activity. The procedure unit can also prioritize the display of relevant procedures based on the user's social media activity. Furthermore, the procedure unit can analyze the user's social media activity and suggest the most suitable procedure. This improves the accuracy of the procedure by providing a procedure based on social media activity. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's social media activity data into a generating AI and have the generating AI execute the suggestion of a procedure.

[0101] The procedure unit can, during a procedure, refer to the user's calendar information and propose a procedure method based on their schedule. For example, the procedure unit can refer to the schedule registered in the user's calendar and propose the most suitable procedure method. The procedure unit can also propose a procedure related to a specific event based on the user's calendar information. Furthermore, the procedure unit can propose a procedure method tailored to the schedule based on the user's calendar information. This improves the efficiency of procedures by providing a procedure method based on calendar information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's calendar information data into a generating AI and have the generating AI execute the procedure method proposal.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The reception desk can estimate the user's emotions and adjust the input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of conditions. In this way, the burden of input can be reduced by providing an interface that responds to the user's emotions.

[0104] The analysis unit can analyze a user's past condition input history and suggest the optimal input method. For example, it can automatically display conditions that the user has frequently entered in the past as candidates. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions that the user will use during specific time periods based on their past input history. In this way, input efficiency can be improved by suggesting the optimal input method based on past history.

[0105] The reception desk can customize input fields based on the user's current lifestyle and areas of interest when they enter conditions. For example, when a user enters their current lifestyle, it can automatically display relevant input fields. It can also prioritize the display of relevant input fields based on the user's areas of interest. Furthermore, it can analyze the user's lifestyle and areas of interest and suggest the most suitable input fields. This improves the accuracy of input by providing input fields that are tailored to the user's lifestyle and areas of interest.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, it can provide a simple analysis method. If the user is relaxed, it can provide a more detailed analysis method. Furthermore, if the user is in a hurry, it can provide a rapid analysis method. By providing an analysis method that matches the user's emotions, the accuracy of the analysis can be improved.

[0107] The search unit can estimate the user's emotions and adjust the search criteria based on those emotions. For example, if the user is stressed, it can provide simple search criteria. If the user is relaxed, it can provide detailed search criteria. Furthermore, if the user is in a hurry, it can provide quick search criteria. By providing search criteria that match the user's emotions, the accuracy of the search can be improved.

[0108] The search unit can improve search accuracy by considering the interrelationships between facilities during the search process. For example, it can provide optimal search results based on the interrelationships between facilities. It can also analyze the interrelationships between facilities and optimize the search algorithm. Furthermore, it can adjust the search algorithm by referring to the interrelationships between facilities. This allows for improved search accuracy by performing searches based on the interrelationships between facilities.

[0109] The guidance system can estimate the user's emotions and adjust the way the guidance is displayed based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By providing a display that matches the user's emotions, the system can facilitate understanding of the guidance.

[0110] The guidance system can optimize the current guidance by referring to past guidance data. For example, it can select the optimal guidance method based on past guidance data. It can also analyze past guidance data and optimize the guidance algorithm. Furthermore, it can adjust the guidance algorithm by referring to past guidance data. In this way, the accuracy of guidance can be improved by optimizing the current guidance based on past guidance data.

[0111] The procedure unit can estimate the user's emotions and adjust the procedure based on those emotions. For example, if the user is stressed, it can provide a simple procedure. If the user is relaxed, it can provide a more detailed procedure. Furthermore, if the user is in a hurry, it can provide a faster procedure. This reduces the burden of procedures by providing procedures that are tailored to the user's emotions.

[0112] The procedure unit can select the optimal procedure method by referring to the user's past procedure history during a procedure. For example, it can select the optimal procedure method based on the user's past procedure history. It can also analyze the user's past procedure history and optimize the procedure algorithm. Furthermore, it can adjust the procedure algorithm by referring to the user's past procedure history. This improves the efficiency of procedures by providing the optimal procedure method based on past procedure history.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk enters the user's requirements. These requirements include the desired area, desired move-in date, budget, and the condition of the person receiving care / assistance. The reception desk can accept user requirements using voice input or text input. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses AI to analyze the user's input information and uncover needs that the user may not have been aware of. For example, based on the budget and desired move-in date entered by the user, it can suggest facility features and services that the user had not considered. Step 3: The search unit searches for available facilities based on the information analyzed by the analysis unit. The search unit can use AI to list facilities that meet the user's criteria. For example, it can search for the most suitable facility based on the user's desired area and budget. Step 4: The information desk provides guidance on how to use and the procedures for the facilities found by the search desk. The information desk can use AI to provide detailed information on how to use and the procedures for each facility. For example, it can provide text guidance or voice guidance. Step 5: The Procedures Department handles facility tour reservations and admission procedures as instructed by the Information Department. The Procedures Department can use AI to make tour reservations and guide users through the admission procedures after the tour. For example, it can make reservations for facilities that users wish to visit and guide them through the admission procedures after the tour.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, and procedure unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit allows the user to input their conditions using the reception device 38 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information using AI. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and lists facilities that match the user's conditions. The guidance unit provides detailed information on how to use each facility and the procedures using the output device 40 of the smart device 14. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and handles things like making tour reservations and admission procedures. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, and procedure unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit allows the user to input conditions using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information using AI. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and lists facilities that match the user's conditions. The guidance unit provides detailed information on how to use each facility and the procedures using the speaker 240 of the smart glasses 214. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and handles things like making reservations for visits and admission procedures. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, and procedure unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit allows the user to input conditions using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information using AI. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and lists facilities that match the user's conditions. The guidance unit provides detailed information on how to use each facility and the procedures using the speaker 240 of the headset terminal 314. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and handles things like making reservations for visits and admission procedures. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, and procedure unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit allows the user to input conditions using the microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information using AI. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and lists facilities that match the user's conditions. The guidance unit provides detailed information about how to use each facility and the procedures using the speaker 240 of the robot 414. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and handles things like making reservations for visits and admission procedures. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A reception area where users enter their conditions, An analysis unit analyzes the information input by the reception unit, A search unit searches for available facilities based on the information analyzed by the aforementioned analysis unit, An information unit that provides information on how to use and the procedures for facilities found by the search unit, The facility comprises a procedures department that handles reservations for tours of facilities guided by the aforementioned information department and handles admission procedures. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering conditions, the input fields are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter conditions, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter criteria, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past input information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system analyzes the user's social media activity and provides relevant analytical results. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, consider the interrelationships between facilities to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the search will take into account the attribute information of the facility provider. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, the search will take into account the geographical distribution of the facilities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, refer to relevant literature for the facility to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is The system estimates the user's emotions and adjusts how the guidance is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is When providing directions, the system optimizes the current directions by referring to past directions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is When providing guidance, different guidance methods will be applied depending on the category of the facility. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is It estimates the user's emotions and adjusts the importance of the guidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is When providing information, we will determine the priority of the information based on when the facility will be used. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is During the tour, we will refer to relevant market data for the facility. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned procedural department, It estimates the user's emotions and adjusts the procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned procedural department, During the procedure, the system will refer to the user's past procedure history to select the most appropriate procedure. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned procedural department, During the process, the procedure methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned procedural department, The system estimates the user's emotions and determines the priority of procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned procedural department, During the process, the system will select the most appropriate procedure based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned procedural department, During the process, we analyze the user's social media activity and suggest appropriate procedures. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned procedural department, During the procedure, we refer to the user's calendar information and suggest a procedure method based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their conditions, An analysis unit analyzes the information input by the reception unit, A search unit searches for available facilities based on the information analyzed by the aforementioned analysis unit, An information unit that provides information on how to use and the procedures for facilities found by the search unit, The facility comprises a procedures department that handles reservations for tours of facilities guided by the aforementioned information department and handles admission procedures. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When entering conditions, the input fields are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input conditions based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter conditions, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter criteria, the system analyzes their social media activity and suggests relevant input fields. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past input information. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's lifestyle and areas of interest. The system according to feature 1.

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